[RFC] Moving (parts of) the Cling REPL in Clang

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[RFC] Moving (parts of) the Cling REPL in Clang

David Chisnall via cfe-dev
Motivation
===

Over the last decade we have developed an interactive, interpretative
C++ (aka REPL) as part of the high-energy physics (HEP) data analysis
project -- ROOT [1-2]. We invested a significant  effort to replace the
CINT C++ interpreter with a newly implemented REPL based on llvm --
cling [3]. The cling infrastructure is a core component of the data
analysis framework of ROOT and runs in production for approximately 5
years.

Cling is also  a standalone tool, which has a growing community outside
of our field. Cling’s user community includes users in finance, biology
and in a few companies with proprietary software. For example, there is
a xeus-cling jupyter kernel [4]. One of the major challenges we face to
foster that community is  our cling-related patches in llvm and clang
forks. The benefits of using the LLVM community standards for code
reviews, release cycles and integration has been mentioned a number of
times by our "external" users.

Last year we were awarded an NSF grant to improve cling's sustainability
and make it a standalone tool. We thank the LLVM Foundation Board for
supporting us with a non-binding letter of collaboration which was
essential for getting this grant.


Background
===

Cling is a C++ interpreter built on top of clang and llvm. In a
nutshell, it uses clang's incremental compilation facilities to process
code chunk-by-chunk by assuming an ever-growing translation unit [5].
Then code is lowered into llvm IR and run by the llvm jit. Cling has
implemented some language "extensions" such as execution statements on
the global scope and error recovery. Cling is in the core of HEP -- it
is heavily used during data analysis of exabytes of particle physics
data coming from the Large Hadron Collider (LHC) and other particle
physics experiments.


Plans
===

The project foresees three main directions -- move parts of cling
upstream along with the clang and llvm features that enable them; extend
and generalize the language interoperability layer around cling; and
extend and generalize the OpenCL/CUDA support in cling. We are at the
early stages of the project and this email intends to be an RFC for the
first part -- upstreaming parts of cling. Please do share your thoughts
on the rest, too.


Moving Parts of Cling Upstream
---

Over the years we have slowly moved some patches upstream. However we
still have around 100 patches in the clang fork. Most of them are in the
context of extending the incremental compilation support for clang. The
incremental compilation poses some challenges in the clang
infrastructure. For example, we need to tune CodeGen to work with
multiple llvm::Module instances, and finalize per each
end-of-translation unit (we have multiple of them). Other changes
include small adjustments in the FileManager's caching mechanism, and
bug fixes in the SourceManager (code which can be reached mostly from
within our setup). One conclusion we can draw from our research is that
the clang infrastructure fits amazingly well to something which was not
its main use case. The grand total of our diffs against clang-9 is: `62
files changed, 1294 insertions(+), 231 deletions(-)`. Cling is currently
being upgraded from llvm-5 to llvm-9.

A major weakness of cling's infrastructure is that it does not work with
the clang Action infrastructure due to the lack of an
IncrementalAction.  A possible way forward would be to implement a
clang::IncrementalAction as a starting point. This way we should be able
to reduce the amount of setup necessary to use the incremental
infrastructure in clang. However, this will be a bit of a testing
challenge -- cling lives downstream and some of the new code may be
impossible to pick straight away and use. Building a mainline example
tool such as clang-repl which gives us a way to test that incremental
case or repurpose the already existing clang-interpreter may  be able to
address the issue. The major risk of the task is avoiding code in the
clang mainline which is untested by its HEP production environment.
There are several other types of patches to the ROOT fork of Clang,
including ones  in the context of performance,towards  C++ modules
support (D41416), and storage (does not have a patch yet but has an open
projects entry and somebody working on it). These patches can be
considered in parallel independently on the rest.

Extend and Generalize the Language Interoperability Layer Around Cling
---

HEP has extensive experience with on-demand python interoperability
using cppyy[6], which is built around the type information provided by
cling. Unlike tools with custom parsers such as swig and sip and tools
built on top of C-APIs such as boost.python and pybind11, cling can
provide information about memory management patterns (eg refcounting)
and instantiate templates on the fly.We feel that functionality may not
be of general interest to the llvm community but we will prepare another
RFC and send it here later on to gather feedback.


Extend and Generalize the OpenCL/CUDA Support in Cling
---

Cling can incrementally compile CUDA code [7-8] allowing easier set up
and enabling some interesting use cases. There are a number of planned
improvements including talking to HIP [9] and SYCL to support more
hardware architectures.



The primary focus of our work is to upstreaming functionality required
to build an incremental compiler and rework cling build against vanilla
clang and llvm. The last two points are to give the scope of the work
which we will be doing the next 2-3 years. We will send here RFCs for
both of them to trigger technical discussion if there is interest in
pursuing this direction.


Collaboration
===

Open source development nowadays relies on reviewers. LLVM is no
different and we will probably disturb a good number of people in the
community ;)We would like to invite anybody interested in joining our
incremental C++ activities to our open every second week calls.
Announcements will be done via google group: compiler-research-announce
(https://groups.google.com/g/compiler-research-announce).



Many thanks!


David & Vassil

References
===
[1] ROOT GitHub https://github.com/root-project/root
[2] ROOT https://root.cern
[3] Cling https://github.com/root-project/cling
[4] Xeus-Cling
https://blog.jupyter.org/xeus-is-now-a-jupyter-subproject-c4ec5a1bf30b
[5] Cling – The New Interactive Interpreter for ROOT 6,
https://iopscience.iop.org/article/10.1088/1742-6596/396/5/052071
[6] High-performance Python-C++ bindings with PyPy and Cling,
https://dl.acm.org/doi/10.5555/3019083.3019087
[7]
https://indico.cern.ch/event/697389/contributions/3085538/attachments/1712698/2761717/2018_09_10_cling_CUDA.pdf
[8] CUDA C++ in Jupyter: Adding CUDA Runtime Support to Cling',
https://zenodo.org/record/3713753#.Xu8jqvJRXxU
[9] HIP Programming Guide
https://rocmdocs.amd.com/en/latest/Programming_Guides/HIP-GUIDE.html

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Re: [RFC] Moving (parts of) the Cling REPL in Clang

David Chisnall via cfe-dev
Hi Vassil,

Thank you for the very detailed email.  I am not directly involved in clang-dev anymore, but I would love to see Cling get folded back into mainline LLVM development.  The Cling project is really cool and I think that it doesn’t get the recognition it deserves,

-Chris

> On Jul 9, 2020, at 1:46 PM, Vassil Vassilev via cfe-dev <[hidden email]> wrote:
>
> Motivation
> ===
>
> Over the last decade we have developed an interactive, interpretative C++ (aka REPL) as part of the high-energy physics (HEP) data analysis project -- ROOT [1-2]. We invested a significant  effort to replace the CINT C++ interpreter with a newly implemented REPL based on llvm -- cling [3]. The cling infrastructure is a core component of the data analysis framework of ROOT and runs in production for approximately 5 years.
>
> Cling is also  a standalone tool, which has a growing community outside of our field. Cling’s user community includes users in finance, biology and in a few companies with proprietary software. For example, there is a xeus-cling jupyter kernel [4]. One of the major challenges we face to foster that community is  our cling-related patches in llvm and clang forks. The benefits of using the LLVM community standards for code reviews, release cycles and integration has been mentioned a number of times by our "external" users.
>
> Last year we were awarded an NSF grant to improve cling's sustainability and make it a standalone tool. We thank the LLVM Foundation Board for supporting us with a non-binding letter of collaboration which was essential for getting this grant.
>
>
> Background
> ===
>
> Cling is a C++ interpreter built on top of clang and llvm. In a nutshell, it uses clang's incremental compilation facilities to process code chunk-by-chunk by assuming an ever-growing translation unit [5]. Then code is lowered into llvm IR and run by the llvm jit. Cling has implemented some language "extensions" such as execution statements on the global scope and error recovery. Cling is in the core of HEP -- it is heavily used during data analysis of exabytes of particle physics data coming from the Large Hadron Collider (LHC) and other particle physics experiments.
>
>
> Plans
> ===
>
> The project foresees three main directions -- move parts of cling upstream along with the clang and llvm features that enable them; extend and generalize the language interoperability layer around cling; and extend and generalize the OpenCL/CUDA support in cling. We are at the early stages of the project and this email intends to be an RFC for the first part -- upstreaming parts of cling. Please do share your thoughts on the rest, too.
>
>
> Moving Parts of Cling Upstream
> ---
>
> Over the years we have slowly moved some patches upstream. However we still have around 100 patches in the clang fork. Most of them are in the context of extending the incremental compilation support for clang. The incremental compilation poses some challenges in the clang infrastructure. For example, we need to tune CodeGen to work with multiple llvm::Module instances, and finalize per each end-of-translation unit (we have multiple of them). Other changes include small adjustments in the FileManager's caching mechanism, and bug fixes in the SourceManager (code which can be reached mostly from within our setup). One conclusion we can draw from our research is that the clang infrastructure fits amazingly well to something which was not its main use case. The grand total of our diffs against clang-9 is: `62 files changed, 1294 insertions(+), 231 deletions(-)`. Cling is currently being upgraded from llvm-5 to llvm-9.
>
> A major weakness of cling's infrastructure is that it does not work with the clang Action infrastructure due to the lack of an IncrementalAction.  A possible way forward would be to implement a clang::IncrementalAction as a starting point. This way we should be able to reduce the amount of setup necessary to use the incremental infrastructure in clang. However, this will be a bit of a testing challenge -- cling lives downstream and some of the new code may be impossible to pick straight away and use. Building a mainline example tool such as clang-repl which gives us a way to test that incremental case or repurpose the already existing clang-interpreter may  be able to address the issue. The major risk of the task is avoiding code in the clang mainline which is untested by its HEP production environment.
> There are several other types of patches to the ROOT fork of Clang, including ones  in the context of performance,towards  C++ modules support (D41416), and storage (does not have a patch yet but has an open projects entry and somebody working on it). These patches can be considered in parallel independently on the rest.
>
> Extend and Generalize the Language Interoperability Layer Around Cling
> ---
>
> HEP has extensive experience with on-demand python interoperability using cppyy[6], which is built around the type information provided by cling. Unlike tools with custom parsers such as swig and sip and tools built on top of C-APIs such as boost.python and pybind11, cling can provide information about memory management patterns (eg refcounting) and instantiate templates on the fly.We feel that functionality may not be of general interest to the llvm community but we will prepare another RFC and send it here later on to gather feedback.
>
>
> Extend and Generalize the OpenCL/CUDA Support in Cling
> ---
>
> Cling can incrementally compile CUDA code [7-8] allowing easier set up and enabling some interesting use cases. There are a number of planned improvements including talking to HIP [9] and SYCL to support more hardware architectures.
>
>
>
> The primary focus of our work is to upstreaming functionality required to build an incremental compiler and rework cling build against vanilla clang and llvm. The last two points are to give the scope of the work which we will be doing the next 2-3 years. We will send here RFCs for both of them to trigger technical discussion if there is interest in pursuing this direction.
>
>
> Collaboration
> ===
>
> Open source development nowadays relies on reviewers. LLVM is no different and we will probably disturb a good number of people in the community ;)We would like to invite anybody interested in joining our incremental C++ activities to our open every second week calls. Announcements will be done via google group: compiler-research-announce (https://groups.google.com/g/compiler-research-announce).
>
>
>
> Many thanks!
>
>
> David & Vassil
>
> References
> ===
> [1] ROOT GitHub https://github.com/root-project/root
> [2] ROOT https://root.cern
> [3] Cling https://github.com/root-project/cling
> [4] Xeus-Cling https://blog.jupyter.org/xeus-is-now-a-jupyter-subproject-c4ec5a1bf30b
> [5] Cling – The New Interactive Interpreter for ROOT 6, https://iopscience.iop.org/article/10.1088/1742-6596/396/5/052071
> [6] High-performance Python-C++ bindings with PyPy and Cling, https://dl.acm.org/doi/10.5555/3019083.3019087
> [7] https://indico.cern.ch/event/697389/contributions/3085538/attachments/1712698/2761717/2018_09_10_cling_CUDA.pdf
> [8] CUDA C++ in Jupyter: Adding CUDA Runtime Support to Cling', https://zenodo.org/record/3713753#.Xu8jqvJRXxU
> [9] HIP Programming Guide https://rocmdocs.amd.com/en/latest/Programming_Guides/HIP-GUIDE.html
>
> _______________________________________________
> cfe-dev mailing list
> [hidden email]
> https://lists.llvm.org/cgi-bin/mailman/listinfo/cfe-dev

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Re: [RFC] Moving (parts of) the Cling REPL in Clang

David Chisnall via cfe-dev
In reply to this post by David Chisnall via cfe-dev
I think that it would be great to have infrastructure for incremental
C++ compilation, supporting interactive use, just-in-time compilation,
and so on. I think that the best way to deal with the patches, etc., as
well as IncrementalAction, is to first send an RFC explaining the
overall design.

  -Hal

On 7/9/20 3:46 PM, Vassil Vassilev via cfe-dev wrote:

> Motivation
> ===
>
> Over the last decade we have developed an interactive, interpretative
> C++ (aka REPL) as part of the high-energy physics (HEP) data analysis
> project -- ROOT [1-2]. We invested a significant  effort to replace
> the CINT C++ interpreter with a newly implemented REPL based on llvm
> -- cling [3]. The cling infrastructure is a core component of the data
> analysis framework of ROOT and runs in production for approximately 5
> years.
>
> Cling is also  a standalone tool, which has a growing community
> outside of our field. Cling’s user community includes users in
> finance, biology and in a few companies with proprietary software. For
> example, there is a xeus-cling jupyter kernel [4]. One of the major
> challenges we face to foster that community is  our cling-related
> patches in llvm and clang forks. The benefits of using the LLVM
> community standards for code reviews, release cycles and integration
> has been mentioned a number of times by our "external" users.
>
> Last year we were awarded an NSF grant to improve cling's
> sustainability and make it a standalone tool. We thank the LLVM
> Foundation Board for supporting us with a non-binding letter of
> collaboration which was essential for getting this grant.
>
>
> Background
> ===
>
> Cling is a C++ interpreter built on top of clang and llvm. In a
> nutshell, it uses clang's incremental compilation facilities to
> process code chunk-by-chunk by assuming an ever-growing translation
> unit [5]. Then code is lowered into llvm IR and run by the llvm jit.
> Cling has implemented some language "extensions" such as execution
> statements on the global scope and error recovery. Cling is in the
> core of HEP -- it is heavily used during data analysis of exabytes of
> particle physics data coming from the Large Hadron Collider (LHC) and
> other particle physics experiments.
>
>
> Plans
> ===
>
> The project foresees three main directions -- move parts of cling
> upstream along with the clang and llvm features that enable them;
> extend and generalize the language interoperability layer around
> cling; and extend and generalize the OpenCL/CUDA support in cling. We
> are at the early stages of the project and this email intends to be an
> RFC for the first part -- upstreaming parts of cling. Please do share
> your thoughts on the rest, too.
>
>
> Moving Parts of Cling Upstream
> ---
>
> Over the years we have slowly moved some patches upstream. However we
> still have around 100 patches in the clang fork. Most of them are in
> the context of extending the incremental compilation support for
> clang. The incremental compilation poses some challenges in the clang
> infrastructure. For example, we need to tune CodeGen to work with
> multiple llvm::Module instances, and finalize per each
> end-of-translation unit (we have multiple of them). Other changes
> include small adjustments in the FileManager's caching mechanism, and
> bug fixes in the SourceManager (code which can be reached mostly from
> within our setup). One conclusion we can draw from our research is
> that the clang infrastructure fits amazingly well to something which
> was not its main use case. The grand total of our diffs against
> clang-9 is: `62 files changed, 1294 insertions(+), 231 deletions(-)`.
> Cling is currently being upgraded from llvm-5 to llvm-9.
>
> A major weakness of cling's infrastructure is that it does not work
> with the clang Action infrastructure due to the lack of an
> IncrementalAction.  A possible way forward would be to implement a
> clang::IncrementalAction as a starting point. This way we should be
> able to reduce the amount of setup necessary to use the incremental
> infrastructure in clang. However, this will be a bit of a testing
> challenge -- cling lives downstream and some of the new code may be
> impossible to pick straight away and use. Building a mainline example
> tool such as clang-repl which gives us a way to test that incremental
> case or repurpose the already existing clang-interpreter may  be able
> to address the issue. The major risk of the task is avoiding code in
> the clang mainline which is untested by its HEP production environment.
> There are several other types of patches to the ROOT fork of Clang,
> including ones  in the context of performance,towards  C++ modules
> support (D41416), and storage (does not have a patch yet but has an
> open projects entry and somebody working on it). These patches can be
> considered in parallel independently on the rest.
>
> Extend and Generalize the Language Interoperability Layer Around Cling
> ---
>
> HEP has extensive experience with on-demand python interoperability
> using cppyy[6], which is built around the type information provided by
> cling. Unlike tools with custom parsers such as swig and sip and tools
> built on top of C-APIs such as boost.python and pybind11, cling can
> provide information about memory management patterns (eg refcounting)
> and instantiate templates on the fly.We feel that functionality may
> not be of general interest to the llvm community but we will prepare
> another RFC and send it here later on to gather feedback.
>
>
> Extend and Generalize the OpenCL/CUDA Support in Cling
> ---
>
> Cling can incrementally compile CUDA code [7-8] allowing easier set up
> and enabling some interesting use cases. There are a number of planned
> improvements including talking to HIP [9] and SYCL to support more
> hardware architectures.
>
>
>
> The primary focus of our work is to upstreaming functionality required
> to build an incremental compiler and rework cling build against
> vanilla clang and llvm. The last two points are to give the scope of
> the work which we will be doing the next 2-3 years. We will send here
> RFCs for both of them to trigger technical discussion if there is
> interest in pursuing this direction.
>
>
> Collaboration
> ===
>
> Open source development nowadays relies on reviewers. LLVM is no
> different and we will probably disturb a good number of people in the
> community ;)We would like to invite anybody interested in joining our
> incremental C++ activities to our open every second week calls.
> Announcements will be done via google group:
> compiler-research-announce
> (https://groups.google.com/g/compiler-research-announce).
>
>
>
> Many thanks!
>
>
> David & Vassil
>
> References
> ===
> [1] ROOT GitHub https://github.com/root-project/root
> [2] ROOT https://root.cern
> [3] Cling https://github.com/root-project/cling
> [4] Xeus-Cling
> https://blog.jupyter.org/xeus-is-now-a-jupyter-subproject-c4ec5a1bf30b
> [5] Cling – The New Interactive Interpreter for ROOT 6,
> https://iopscience.iop.org/article/10.1088/1742-6596/396/5/052071
> [6] High-performance Python-C++ bindings with PyPy and Cling,
> https://dl.acm.org/doi/10.5555/3019083.3019087
> [7]
> https://indico.cern.ch/event/697389/contributions/3085538/attachments/1712698/2761717/2018_09_10_cling_CUDA.pdf
> [8] CUDA C++ in Jupyter: Adding CUDA Runtime Support to Cling',
> https://zenodo.org/record/3713753#.Xu8jqvJRXxU
> [9] HIP Programming Guide
> https://rocmdocs.amd.com/en/latest/Programming_Guides/HIP-GUIDE.html
>
> _______________________________________________
> cfe-dev mailing list
> [hidden email]
> https://lists.llvm.org/cgi-bin/mailman/listinfo/cfe-dev

--
Hal Finkel
Lead, Compiler Technology and Programming Languages
Leadership Computing Facility
Argonne National Laboratory

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Re: [RFC] Moving (parts of) the Cling REPL in Clang

David Chisnall via cfe-dev
I like cling, and having it integrated with the rest of the project would be neat. I agree with Hal’s suggestion to explain the design of what remains. It sounds like a pretty small amount of code.


> On Jul 9, 2020, at 7:25 PM, Hal Finkel via cfe-dev <[hidden email]> wrote:
>
> I think that it would be great to have infrastructure for incremental C++ compilation, supporting interactive use, just-in-time compilation, and so on. I think that the best way to deal with the patches, etc., as well as IncrementalAction, is to first send an RFC explaining the overall design.
>
>  -Hal
>
> On 7/9/20 3:46 PM, Vassil Vassilev via cfe-dev wrote:
>> Motivation
>> ===
>>
>> Over the last decade we have developed an interactive, interpretative C++ (aka REPL) as part of the high-energy physics (HEP) data analysis project -- ROOT [1-2]. We invested a significant  effort to replace the CINT C++ interpreter with a newly implemented REPL based on llvm -- cling [3]. The cling infrastructure is a core component of the data analysis framework of ROOT and runs in production for approximately 5 years.
>>
>> Cling is also  a standalone tool, which has a growing community outside of our field. Cling’s user community includes users in finance, biology and in a few companies with proprietary software. For example, there is a xeus-cling jupyter kernel [4]. One of the major challenges we face to foster that community is  our cling-related patches in llvm and clang forks. The benefits of using the LLVM community standards for code reviews, release cycles and integration has been mentioned a number of times by our "external" users.
>>
>> Last year we were awarded an NSF grant to improve cling's sustainability and make it a standalone tool. We thank the LLVM Foundation Board for supporting us with a non-binding letter of collaboration which was essential for getting this grant.
>>
>>
>> Background
>> ===
>>
>> Cling is a C++ interpreter built on top of clang and llvm. In a nutshell, it uses clang's incremental compilation facilities to process code chunk-by-chunk by assuming an ever-growing translation unit [5]. Then code is lowered into llvm IR and run by the llvm jit. Cling has implemented some language "extensions" such as execution statements on the global scope and error recovery. Cling is in the core of HEP -- it is heavily used during data analysis of exabytes of particle physics data coming from the Large Hadron Collider (LHC) and other particle physics experiments.
>>
>>
>> Plans
>> ===
>>
>> The project foresees three main directions -- move parts of cling upstream along with the clang and llvm features that enable them; extend and generalize the language interoperability layer around cling; and extend and generalize the OpenCL/CUDA support in cling. We are at the early stages of the project and this email intends to be an RFC for the first part -- upstreaming parts of cling. Please do share your thoughts on the rest, too.
>>
>>
>> Moving Parts of Cling Upstream
>> ---
>>
>> Over the years we have slowly moved some patches upstream. However we still have around 100 patches in the clang fork. Most of them are in the context of extending the incremental compilation support for clang. The incremental compilation poses some challenges in the clang infrastructure. For example, we need to tune CodeGen to work with multiple llvm::Module instances, and finalize per each end-of-translation unit (we have multiple of them). Other changes include small adjustments in the FileManager's caching mechanism, and bug fixes in the SourceManager (code which can be reached mostly from within our setup). One conclusion we can draw from our research is that the clang infrastructure fits amazingly well to something which was not its main use case. The grand total of our diffs against clang-9 is: `62 files changed, 1294 insertions(+), 231 deletions(-)`. Cling is currently being upgraded from llvm-5 to llvm-9.
>>
>> A major weakness of cling's infrastructure is that it does not work with the clang Action infrastructure due to the lack of an IncrementalAction.  A possible way forward would be to implement a clang::IncrementalAction as a starting point. This way we should be able to reduce the amount of setup necessary to use the incremental infrastructure in clang. However, this will be a bit of a testing challenge -- cling lives downstream and some of the new code may be impossible to pick straight away and use. Building a mainline example tool such as clang-repl which gives us a way to test that incremental case or repurpose the already existing clang-interpreter may  be able to address the issue. The major risk of the task is avoiding code in the clang mainline which is untested by its HEP production environment.
>> There are several other types of patches to the ROOT fork of Clang, including ones  in the context of performance,towards  C++ modules support (D41416), and storage (does not have a patch yet but has an open projects entry and somebody working on it). These patches can be considered in parallel independently on the rest.
>>
>> Extend and Generalize the Language Interoperability Layer Around Cling
>> ---
>>
>> HEP has extensive experience with on-demand python interoperability using cppyy[6], which is built around the type information provided by cling. Unlike tools with custom parsers such as swig and sip and tools built on top of C-APIs such as boost.python and pybind11, cling can provide information about memory management patterns (eg refcounting) and instantiate templates on the fly.We feel that functionality may not be of general interest to the llvm community but we will prepare another RFC and send it here later on to gather feedback.
>>
>>
>> Extend and Generalize the OpenCL/CUDA Support in Cling
>> ---
>>
>> Cling can incrementally compile CUDA code [7-8] allowing easier set up and enabling some interesting use cases. There are a number of planned improvements including talking to HIP [9] and SYCL to support more hardware architectures.
>>
>>
>>
>> The primary focus of our work is to upstreaming functionality required to build an incremental compiler and rework cling build against vanilla clang and llvm. The last two points are to give the scope of the work which we will be doing the next 2-3 years. We will send here RFCs for both of them to trigger technical discussion if there is interest in pursuing this direction.
>>
>>
>> Collaboration
>> ===
>>
>> Open source development nowadays relies on reviewers. LLVM is no different and we will probably disturb a good number of people in the community ;)We would like to invite anybody interested in joining our incremental C++ activities to our open every second week calls. Announcements will be done via google group: compiler-research-announce (https://groups.google.com/g/compiler-research-announce).
>>
>>
>>
>> Many thanks!
>>
>>
>> David & Vassil
>>
>> References
>> ===
>> [1] ROOT GitHub https://github.com/root-project/root
>> [2] ROOT https://root.cern
>> [3] Cling https://github.com/root-project/cling
>> [4] Xeus-Cling https://blog.jupyter.org/xeus-is-now-a-jupyter-subproject-c4ec5a1bf30b
>> [5] Cling – The New Interactive Interpreter for ROOT 6, https://iopscience.iop.org/article/10.1088/1742-6596/396/5/052071
>> [6] High-performance Python-C++ bindings with PyPy and Cling, https://dl.acm.org/doi/10.5555/3019083.3019087
>> [7] https://indico.cern.ch/event/697389/contributions/3085538/attachments/1712698/2761717/2018_09_10_cling_CUDA.pdf
>> [8] CUDA C++ in Jupyter: Adding CUDA Runtime Support to Cling', https://zenodo.org/record/3713753#.Xu8jqvJRXxU
>> [9] HIP Programming Guide https://rocmdocs.amd.com/en/latest/Programming_Guides/HIP-GUIDE.html
>>
>> _______________________________________________
>> cfe-dev mailing list
>> [hidden email]
>> https://lists.llvm.org/cgi-bin/mailman/listinfo/cfe-dev
>
> --
> Hal Finkel
> Lead, Compiler Technology and Programming Languages
> Leadership Computing Facility
> Argonne National Laboratory
>
> _______________________________________________
> cfe-dev mailing list
> [hidden email]
> https://lists.llvm.org/cgi-bin/mailman/listinfo/cfe-dev

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Re: [RFC] Moving (parts of) the Cling REPL in Clang

David Chisnall via cfe-dev
I do not know enough about cling, but I like what you describe very much, am particularly intrigued about how your approach could also be appropriated to do ahead-of-time constexpr metaprogramming as well, which also involves incrementally adding declarations to the translation unit.

Dave

> On Jul 9, 2020, at 11:43 PM, JF Bastien via cfe-dev <[hidden email]> wrote:
>
> I like cling, and having it integrated with the rest of the project would be neat. I agree with Hal’s suggestion to explain the design of what remains. It sounds like a pretty small amount of code.
>
>
>> On Jul 9, 2020, at 7:25 PM, Hal Finkel via cfe-dev <[hidden email]> wrote:
>>
>> I think that it would be great to have infrastructure for incremental C++ compilation, supporting interactive use, just-in-time compilation, and so on. I think that the best way to deal with the patches, etc., as well as IncrementalAction, is to first send an RFC explaining the overall design.
>>
>> -Hal
>>
>> On 7/9/20 3:46 PM, Vassil Vassilev via cfe-dev wrote:
>>> Motivation
>>> ===
>>>
>>> Over the last decade we have developed an interactive, interpretative C++ (aka REPL) as part of the high-energy physics (HEP) data analysis project -- ROOT [1-2]. We invested a significant  effort to replace the CINT C++ interpreter with a newly implemented REPL based on llvm -- cling [3]. The cling infrastructure is a core component of the data analysis framework of ROOT and runs in production for approximately 5 years.
>>>
>>> Cling is also  a standalone tool, which has a growing community outside of our field. Cling’s user community includes users in finance, biology and in a few companies with proprietary software. For example, there is a xeus-cling jupyter kernel [4]. One of the major challenges we face to foster that community is  our cling-related patches in llvm and clang forks. The benefits of using the LLVM community standards for code reviews, release cycles and integration has been mentioned a number of times by our "external" users.
>>>
>>> Last year we were awarded an NSF grant to improve cling's sustainability and make it a standalone tool. We thank the LLVM Foundation Board for supporting us with a non-binding letter of collaboration which was essential for getting this grant.
>>>
>>>
>>> Background
>>> ===
>>>
>>> Cling is a C++ interpreter built on top of clang and llvm. In a nutshell, it uses clang's incremental compilation facilities to process code chunk-by-chunk by assuming an ever-growing translation unit [5]. Then code is lowered into llvm IR and run by the llvm jit. Cling has implemented some language "extensions" such as execution statements on the global scope and error recovery. Cling is in the core of HEP -- it is heavily used during data analysis of exabytes of particle physics data coming from the Large Hadron Collider (LHC) and other particle physics experiments.
>>>
>>>
>>> Plans
>>> ===
>>>
>>> The project foresees three main directions -- move parts of cling upstream along with the clang and llvm features that enable them; extend and generalize the language interoperability layer around cling; and extend and generalize the OpenCL/CUDA support in cling. We are at the early stages of the project and this email intends to be an RFC for the first part -- upstreaming parts of cling. Please do share your thoughts on the rest, too.
>>>
>>>
>>> Moving Parts of Cling Upstream
>>> ---
>>>
>>> Over the years we have slowly moved some patches upstream. However we still have around 100 patches in the clang fork. Most of them are in the context of extending the incremental compilation support for clang. The incremental compilation poses some challenges in the clang infrastructure. For example, we need to tune CodeGen to work with multiple llvm::Module instances, and finalize per each end-of-translation unit (we have multiple of them). Other changes include small adjustments in the FileManager's caching mechanism, and bug fixes in the SourceManager (code which can be reached mostly from within our setup). One conclusion we can draw from our research is that the clang infrastructure fits amazingly well to something which was not its main use case. The grand total of our diffs against clang-9 is: `62 files changed, 1294 insertions(+), 231 deletions(-)`. Cling is currently being upgraded from llvm-5 to llvm-9.
>>>
>>> A major weakness of cling's infrastructure is that it does not work with the clang Action infrastructure due to the lack of an IncrementalAction.  A possible way forward would be to implement a clang::IncrementalAction as a starting point. This way we should be able to reduce the amount of setup necessary to use the incremental infrastructure in clang. However, this will be a bit of a testing challenge -- cling lives downstream and some of the new code may be impossible to pick straight away and use. Building a mainline example tool such as clang-repl which gives us a way to test that incremental case or repurpose the already existing clang-interpreter may  be able to address the issue. The major risk of the task is avoiding code in the clang mainline which is untested by its HEP production environment.
>>> There are several other types of patches to the ROOT fork of Clang, including ones  in the context of performance,towards  C++ modules support (D41416), and storage (does not have a patch yet but has an open projects entry and somebody working on it). These patches can be considered in parallel independently on the rest.
>>>
>>> Extend and Generalize the Language Interoperability Layer Around Cling
>>> ---
>>>
>>> HEP has extensive experience with on-demand python interoperability using cppyy[6], which is built around the type information provided by cling. Unlike tools with custom parsers such as swig and sip and tools built on top of C-APIs such as boost.python and pybind11, cling can provide information about memory management patterns (eg refcounting) and instantiate templates on the fly.We feel that functionality may not be of general interest to the llvm community but we will prepare another RFC and send it here later on to gather feedback.
>>>
>>>
>>> Extend and Generalize the OpenCL/CUDA Support in Cling
>>> ---
>>>
>>> Cling can incrementally compile CUDA code [7-8] allowing easier set up and enabling some interesting use cases. There are a number of planned improvements including talking to HIP [9] and SYCL to support more hardware architectures.
>>>
>>>
>>>
>>> The primary focus of our work is to upstreaming functionality required to build an incremental compiler and rework cling build against vanilla clang and llvm. The last two points are to give the scope of the work which we will be doing the next 2-3 years. We will send here RFCs for both of them to trigger technical discussion if there is interest in pursuing this direction.
>>>
>>>
>>> Collaboration
>>> ===
>>>
>>> Open source development nowadays relies on reviewers. LLVM is no different and we will probably disturb a good number of people in the community ;)We would like to invite anybody interested in joining our incremental C++ activities to our open every second week calls. Announcements will be done via google group: compiler-research-announce (https://groups.google.com/g/compiler-research-announce).
>>>
>>>
>>>
>>> Many thanks!
>>>
>>>
>>> David & Vassil
>>>
>>> References
>>> ===
>>> [1] ROOT GitHub https://github.com/root-project/root
>>> [2] ROOT https://root.cern
>>> [3] Cling https://github.com/root-project/cling
>>> [4] Xeus-Cling https://blog.jupyter.org/xeus-is-now-a-jupyter-subproject-c4ec5a1bf30b
>>> [5] Cling – The New Interactive Interpreter for ROOT 6, https://iopscience.iop.org/article/10.1088/1742-6596/396/5/052071
>>> [6] High-performance Python-C++ bindings with PyPy and Cling, https://dl.acm.org/doi/10.5555/3019083.3019087
>>> [7] https://indico.cern.ch/event/697389/contributions/3085538/attachments/1712698/2761717/2018_09_10_cling_CUDA.pdf
>>> [8] CUDA C++ in Jupyter: Adding CUDA Runtime Support to Cling', https://zenodo.org/record/3713753#.Xu8jqvJRXxU
>>> [9] HIP Programming Guide https://rocmdocs.amd.com/en/latest/Programming_Guides/HIP-GUIDE.html
>>>
>>> _______________________________________________
>>> cfe-dev mailing list
>>> [hidden email]
>>> https://lists.llvm.org/cgi-bin/mailman/listinfo/cfe-dev
>>
>> --
>> Hal Finkel
>> Lead, Compiler Technology and Programming Languages
>> Leadership Computing Facility
>> Argonne National Laboratory
>>
>> _______________________________________________
>> cfe-dev mailing list
>> [hidden email]
>> https://lists.llvm.org/cgi-bin/mailman/listinfo/cfe-dev
>
> _______________________________________________
> cfe-dev mailing list
> [hidden email]
> https://lists.llvm.org/cgi-bin/mailman/listinfo/cfe-dev

_______________________________________________
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Re: [RFC] Moving (parts of) the Cling REPL in Clang

David Chisnall via cfe-dev
In reply to this post by David Chisnall via cfe-dev
On 7/10/20 6:43 AM, JF Bastien wrote:
> I like cling, and having it integrated with the rest of the project would be neat. I agree with Hal’s suggestion to explain the design of what remains. It sounds like a pretty small amount of code.


   JF, Hal, did you mean you want a design document of how cling in
general or a design RFC for the patches we have? A design document for
cling would be quite large and will take us some time to write up. OTOH,
we could relatively easily give a rationale for each patch.


>
>
>> On Jul 9, 2020, at 7:25 PM, Hal Finkel via cfe-dev <[hidden email]> wrote:
>>
>> I think that it would be great to have infrastructure for incremental C++ compilation, supporting interactive use, just-in-time compilation, and so on. I think that the best way to deal with the patches, etc., as well as IncrementalAction, is to first send an RFC explaining the overall design.
>>
>>   -Hal
>>
>> On 7/9/20 3:46 PM, Vassil Vassilev via cfe-dev wrote:
>>> Motivation
>>> ===
>>>
>>> Over the last decade we have developed an interactive, interpretative C++ (aka REPL) as part of the high-energy physics (HEP) data analysis project -- ROOT [1-2]. We invested a significant  effort to replace the CINT C++ interpreter with a newly implemented REPL based on llvm -- cling [3]. The cling infrastructure is a core component of the data analysis framework of ROOT and runs in production for approximately 5 years.
>>>
>>> Cling is also  a standalone tool, which has a growing community outside of our field. Cling’s user community includes users in finance, biology and in a few companies with proprietary software. For example, there is a xeus-cling jupyter kernel [4]. One of the major challenges we face to foster that community is  our cling-related patches in llvm and clang forks. The benefits of using the LLVM community standards for code reviews, release cycles and integration has been mentioned a number of times by our "external" users.
>>>
>>> Last year we were awarded an NSF grant to improve cling's sustainability and make it a standalone tool. We thank the LLVM Foundation Board for supporting us with a non-binding letter of collaboration which was essential for getting this grant.
>>>
>>>
>>> Background
>>> ===
>>>
>>> Cling is a C++ interpreter built on top of clang and llvm. In a nutshell, it uses clang's incremental compilation facilities to process code chunk-by-chunk by assuming an ever-growing translation unit [5]. Then code is lowered into llvm IR and run by the llvm jit. Cling has implemented some language "extensions" such as execution statements on the global scope and error recovery. Cling is in the core of HEP -- it is heavily used during data analysis of exabytes of particle physics data coming from the Large Hadron Collider (LHC) and other particle physics experiments.
>>>
>>>
>>> Plans
>>> ===
>>>
>>> The project foresees three main directions -- move parts of cling upstream along with the clang and llvm features that enable them; extend and generalize the language interoperability layer around cling; and extend and generalize the OpenCL/CUDA support in cling. We are at the early stages of the project and this email intends to be an RFC for the first part -- upstreaming parts of cling. Please do share your thoughts on the rest, too.
>>>
>>>
>>> Moving Parts of Cling Upstream
>>> ---
>>>
>>> Over the years we have slowly moved some patches upstream. However we still have around 100 patches in the clang fork. Most of them are in the context of extending the incremental compilation support for clang. The incremental compilation poses some challenges in the clang infrastructure. For example, we need to tune CodeGen to work with multiple llvm::Module instances, and finalize per each end-of-translation unit (we have multiple of them). Other changes include small adjustments in the FileManager's caching mechanism, and bug fixes in the SourceManager (code which can be reached mostly from within our setup). One conclusion we can draw from our research is that the clang infrastructure fits amazingly well to something which was not its main use case. The grand total of our diffs against clang-9 is: `62 files changed, 1294 insertions(+), 231 deletions(-)`. Cling is currently being upgraded from llvm-5 to llvm-9.
>>>
>>> A major weakness of cling's infrastructure is that it does not work with the clang Action infrastructure due to the lack of an IncrementalAction.  A possible way forward would be to implement a clang::IncrementalAction as a starting point. This way we should be able to reduce the amount of setup necessary to use the incremental infrastructure in clang. However, this will be a bit of a testing challenge -- cling lives downstream and some of the new code may be impossible to pick straight away and use. Building a mainline example tool such as clang-repl which gives us a way to test that incremental case or repurpose the already existing clang-interpreter may  be able to address the issue. The major risk of the task is avoiding code in the clang mainline which is untested by its HEP production environment.
>>> There are several other types of patches to the ROOT fork of Clang, including ones  in the context of performance,towards  C++ modules support (D41416), and storage (does not have a patch yet but has an open projects entry and somebody working on it). These patches can be considered in parallel independently on the rest.
>>>
>>> Extend and Generalize the Language Interoperability Layer Around Cling
>>> ---
>>>
>>> HEP has extensive experience with on-demand python interoperability using cppyy[6], which is built around the type information provided by cling. Unlike tools with custom parsers such as swig and sip and tools built on top of C-APIs such as boost.python and pybind11, cling can provide information about memory management patterns (eg refcounting) and instantiate templates on the fly.We feel that functionality may not be of general interest to the llvm community but we will prepare another RFC and send it here later on to gather feedback.
>>>
>>>
>>> Extend and Generalize the OpenCL/CUDA Support in Cling
>>> ---
>>>
>>> Cling can incrementally compile CUDA code [7-8] allowing easier set up and enabling some interesting use cases. There are a number of planned improvements including talking to HIP [9] and SYCL to support more hardware architectures.
>>>
>>>
>>>
>>> The primary focus of our work is to upstreaming functionality required to build an incremental compiler and rework cling build against vanilla clang and llvm. The last two points are to give the scope of the work which we will be doing the next 2-3 years. We will send here RFCs for both of them to trigger technical discussion if there is interest in pursuing this direction.
>>>
>>>
>>> Collaboration
>>> ===
>>>
>>> Open source development nowadays relies on reviewers. LLVM is no different and we will probably disturb a good number of people in the community ;)We would like to invite anybody interested in joining our incremental C++ activities to our open every second week calls. Announcements will be done via google group: compiler-research-announce (https://groups.google.com/g/compiler-research-announce).
>>>
>>>
>>>
>>> Many thanks!
>>>
>>>
>>> David & Vassil
>>>
>>> References
>>> ===
>>> [1] ROOT GitHub https://github.com/root-project/root
>>> [2] ROOT https://root.cern
>>> [3] Cling https://github.com/root-project/cling
>>> [4] Xeus-Cling https://blog.jupyter.org/xeus-is-now-a-jupyter-subproject-c4ec5a1bf30b
>>> [5] Cling – The New Interactive Interpreter for ROOT 6, https://iopscience.iop.org/article/10.1088/1742-6596/396/5/052071
>>> [6] High-performance Python-C++ bindings with PyPy and Cling, https://dl.acm.org/doi/10.5555/3019083.3019087
>>> [7] https://indico.cern.ch/event/697389/contributions/3085538/attachments/1712698/2761717/2018_09_10_cling_CUDA.pdf
>>> [8] CUDA C++ in Jupyter: Adding CUDA Runtime Support to Cling', https://zenodo.org/record/3713753#.Xu8jqvJRXxU
>>> [9] HIP Programming Guide https://rocmdocs.amd.com/en/latest/Programming_Guides/HIP-GUIDE.html
>>>
>>> _______________________________________________
>>> cfe-dev mailing list
>>> [hidden email]
>>> https://lists.llvm.org/cgi-bin/mailman/listinfo/cfe-dev
>> --
>> Hal Finkel
>> Lead, Compiler Technology and Programming Languages
>> Leadership Computing Facility
>> Argonne National Laboratory
>>
>> _______________________________________________
>> cfe-dev mailing list
>> [hidden email]
>> https://lists.llvm.org/cgi-bin/mailman/listinfo/cfe-dev


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Re: [RFC] Moving (parts of) the Cling REPL in Clang

David Chisnall via cfe-dev
In reply to this post by David Chisnall via cfe-dev
On 7/10/20 8:09 PM, David Rector wrote:
> I do not know enough about cling, but I like what you describe very much, am particularly intrigued about how your approach could also be appropriated to do ahead-of-time constexpr metaprogramming as well, which also involves incrementally adding declarations to the translation unit.


   Wow, I do not think we have thought of something like that. Cling
keeps a single clang compiler instance in memory and each new input just
"adds" to it -- nothing fancy on the frontend. The more interesting part
happens in CodeGen where we produce multiple llvm::Modules. Maybe some
parts from that could be reused to pursue the direction you are
intrigued about.


>
> Dave
>
>> On Jul 9, 2020, at 11:43 PM, JF Bastien via cfe-dev <[hidden email]> wrote:
>>
>> I like cling, and having it integrated with the rest of the project would be neat. I agree with Hal’s suggestion to explain the design of what remains. It sounds like a pretty small amount of code.
>>
>>
>>> On Jul 9, 2020, at 7:25 PM, Hal Finkel via cfe-dev <[hidden email]> wrote:
>>>
>>> I think that it would be great to have infrastructure for incremental C++ compilation, supporting interactive use, just-in-time compilation, and so on. I think that the best way to deal with the patches, etc., as well as IncrementalAction, is to first send an RFC explaining the overall design.
>>>
>>> -Hal
>>>
>>> On 7/9/20 3:46 PM, Vassil Vassilev via cfe-dev wrote:
>>>> Motivation
>>>> ===
>>>>
>>>> Over the last decade we have developed an interactive, interpretative C++ (aka REPL) as part of the high-energy physics (HEP) data analysis project -- ROOT [1-2]. We invested a significant  effort to replace the CINT C++ interpreter with a newly implemented REPL based on llvm -- cling [3]. The cling infrastructure is a core component of the data analysis framework of ROOT and runs in production for approximately 5 years.
>>>>
>>>> Cling is also  a standalone tool, which has a growing community outside of our field. Cling’s user community includes users in finance, biology and in a few companies with proprietary software. For example, there is a xeus-cling jupyter kernel [4]. One of the major challenges we face to foster that community is  our cling-related patches in llvm and clang forks. The benefits of using the LLVM community standards for code reviews, release cycles and integration has been mentioned a number of times by our "external" users.
>>>>
>>>> Last year we were awarded an NSF grant to improve cling's sustainability and make it a standalone tool. We thank the LLVM Foundation Board for supporting us with a non-binding letter of collaboration which was essential for getting this grant.
>>>>
>>>>
>>>> Background
>>>> ===
>>>>
>>>> Cling is a C++ interpreter built on top of clang and llvm. In a nutshell, it uses clang's incremental compilation facilities to process code chunk-by-chunk by assuming an ever-growing translation unit [5]. Then code is lowered into llvm IR and run by the llvm jit. Cling has implemented some language "extensions" such as execution statements on the global scope and error recovery. Cling is in the core of HEP -- it is heavily used during data analysis of exabytes of particle physics data coming from the Large Hadron Collider (LHC) and other particle physics experiments.
>>>>
>>>>
>>>> Plans
>>>> ===
>>>>
>>>> The project foresees three main directions -- move parts of cling upstream along with the clang and llvm features that enable them; extend and generalize the language interoperability layer around cling; and extend and generalize the OpenCL/CUDA support in cling. We are at the early stages of the project and this email intends to be an RFC for the first part -- upstreaming parts of cling. Please do share your thoughts on the rest, too.
>>>>
>>>>
>>>> Moving Parts of Cling Upstream
>>>> ---
>>>>
>>>> Over the years we have slowly moved some patches upstream. However we still have around 100 patches in the clang fork. Most of them are in the context of extending the incremental compilation support for clang. The incremental compilation poses some challenges in the clang infrastructure. For example, we need to tune CodeGen to work with multiple llvm::Module instances, and finalize per each end-of-translation unit (we have multiple of them). Other changes include small adjustments in the FileManager's caching mechanism, and bug fixes in the SourceManager (code which can be reached mostly from within our setup). One conclusion we can draw from our research is that the clang infrastructure fits amazingly well to something which was not its main use case. The grand total of our diffs against clang-9 is: `62 files changed, 1294 insertions(+), 231 deletions(-)`. Cling is currently being upgraded from llvm-5 to llvm-9.
>>>>
>>>> A major weakness of cling's infrastructure is that it does not work with the clang Action infrastructure due to the lack of an IncrementalAction.  A possible way forward would be to implement a clang::IncrementalAction as a starting point. This way we should be able to reduce the amount of setup necessary to use the incremental infrastructure in clang. However, this will be a bit of a testing challenge -- cling lives downstream and some of the new code may be impossible to pick straight away and use. Building a mainline example tool such as clang-repl which gives us a way to test that incremental case or repurpose the already existing clang-interpreter may  be able to address the issue. The major risk of the task is avoiding code in the clang mainline which is untested by its HEP production environment.
>>>> There are several other types of patches to the ROOT fork of Clang, including ones  in the context of performance,towards  C++ modules support (D41416), and storage (does not have a patch yet but has an open projects entry and somebody working on it). These patches can be considered in parallel independently on the rest.
>>>>
>>>> Extend and Generalize the Language Interoperability Layer Around Cling
>>>> ---
>>>>
>>>> HEP has extensive experience with on-demand python interoperability using cppyy[6], which is built around the type information provided by cling. Unlike tools with custom parsers such as swig and sip and tools built on top of C-APIs such as boost.python and pybind11, cling can provide information about memory management patterns (eg refcounting) and instantiate templates on the fly.We feel that functionality may not be of general interest to the llvm community but we will prepare another RFC and send it here later on to gather feedback.
>>>>
>>>>
>>>> Extend and Generalize the OpenCL/CUDA Support in Cling
>>>> ---
>>>>
>>>> Cling can incrementally compile CUDA code [7-8] allowing easier set up and enabling some interesting use cases. There are a number of planned improvements including talking to HIP [9] and SYCL to support more hardware architectures.
>>>>
>>>>
>>>>
>>>> The primary focus of our work is to upstreaming functionality required to build an incremental compiler and rework cling build against vanilla clang and llvm. The last two points are to give the scope of the work which we will be doing the next 2-3 years. We will send here RFCs for both of them to trigger technical discussion if there is interest in pursuing this direction.
>>>>
>>>>
>>>> Collaboration
>>>> ===
>>>>
>>>> Open source development nowadays relies on reviewers. LLVM is no different and we will probably disturb a good number of people in the community ;)We would like to invite anybody interested in joining our incremental C++ activities to our open every second week calls. Announcements will be done via google group: compiler-research-announce (https://groups.google.com/g/compiler-research-announce).
>>>>
>>>>
>>>>
>>>> Many thanks!
>>>>
>>>>
>>>> David & Vassil
>>>>
>>>> References
>>>> ===
>>>> [1] ROOT GitHub https://github.com/root-project/root
>>>> [2] ROOT https://root.cern
>>>> [3] Cling https://github.com/root-project/cling
>>>> [4] Xeus-Cling https://blog.jupyter.org/xeus-is-now-a-jupyter-subproject-c4ec5a1bf30b
>>>> [5] Cling – The New Interactive Interpreter for ROOT 6, https://iopscience.iop.org/article/10.1088/1742-6596/396/5/052071
>>>> [6] High-performance Python-C++ bindings with PyPy and Cling, https://dl.acm.org/doi/10.5555/3019083.3019087
>>>> [7] https://indico.cern.ch/event/697389/contributions/3085538/attachments/1712698/2761717/2018_09_10_cling_CUDA.pdf
>>>> [8] CUDA C++ in Jupyter: Adding CUDA Runtime Support to Cling', https://zenodo.org/record/3713753#.Xu8jqvJRXxU
>>>> [9] HIP Programming Guide https://rocmdocs.amd.com/en/latest/Programming_Guides/HIP-GUIDE.html
>>>>
>>>> _______________________________________________
>>>> cfe-dev mailing list
>>>> [hidden email]
>>>> https://lists.llvm.org/cgi-bin/mailman/listinfo/cfe-dev
>>> --
>>> Hal Finkel
>>> Lead, Compiler Technology and Programming Languages
>>> Leadership Computing Facility
>>> Argonne National Laboratory
>>>
>>> _______________________________________________
>>> cfe-dev mailing list
>>> [hidden email]
>>> https://lists.llvm.org/cgi-bin/mailman/listinfo/cfe-dev
>> _______________________________________________
>> cfe-dev mailing list
>> [hidden email]
>> https://lists.llvm.org/cgi-bin/mailman/listinfo/cfe-dev


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Re: [RFC] Moving (parts of) the Cling REPL in Clang

David Chisnall via cfe-dev
In reply to this post by David Chisnall via cfe-dev
Hi Vassil,

This is a very exciting proposal that I can imagine bringing important benefits to the existing cling users and also to the clang user and developer community. Thank you for all the work you and your team have done on cling so far and for offering to bring that work under the LLVM umbrella!

Are you imagining cling being part of the clang repository, or a separate LLVM subproject (with only the changes necessary to support cling-style uses of the clang libraries added to the clang tree)?

On Thu, 9 Jul 2020 at 13:46, Vassil Vassilev via cfe-dev <[hidden email]> wrote:
Motivation
===

Over the last decade we have developed an interactive, interpretative
C++ (aka REPL) as part of the high-energy physics (HEP) data analysis
project -- ROOT [1-2]. We invested a significant  effort to replace the
CINT C++ interpreter with a newly implemented REPL based on llvm --
cling [3]. The cling infrastructure is a core component of the data
analysis framework of ROOT and runs in production for approximately 5
years.

Cling is also  a standalone tool, which has a growing community outside
of our field. Cling’s user community includes users in finance, biology
and in a few companies with proprietary software. For example, there is
a xeus-cling jupyter kernel [4]. One of the major challenges we face to
foster that community is  our cling-related patches in llvm and clang
forks. The benefits of using the LLVM community standards for code
reviews, release cycles and integration has been mentioned a number of
times by our "external" users.

Last year we were awarded an NSF grant to improve cling's sustainability
and make it a standalone tool. We thank the LLVM Foundation Board for
supporting us with a non-binding letter of collaboration which was
essential for getting this grant.


Background
===

Cling is a C++ interpreter built on top of clang and llvm. In a
nutshell, it uses clang's incremental compilation facilities to process
code chunk-by-chunk by assuming an ever-growing translation unit [5].
Then code is lowered into llvm IR and run by the llvm jit. Cling has
implemented some language "extensions" such as execution statements on
the global scope and error recovery. Cling is in the core of HEP -- it
is heavily used during data analysis of exabytes of particle physics
data coming from the Large Hadron Collider (LHC) and other particle
physics experiments.


Plans
===

The project foresees three main directions -- move parts of cling
upstream along with the clang and llvm features that enable them; extend
and generalize the language interoperability layer around cling; and
extend and generalize the OpenCL/CUDA support in cling. We are at the
early stages of the project and this email intends to be an RFC for the
first part -- upstreaming parts of cling. Please do share your thoughts
on the rest, too.


Moving Parts of Cling Upstream
---

Over the years we have slowly moved some patches upstream. However we
still have around 100 patches in the clang fork. Most of them are in the
context of extending the incremental compilation support for clang. The
incremental compilation poses some challenges in the clang
infrastructure. For example, we need to tune CodeGen to work with
multiple llvm::Module instances, and finalize per each
end-of-translation unit (we have multiple of them). Other changes
include small adjustments in the FileManager's caching mechanism, and
bug fixes in the SourceManager (code which can be reached mostly from
within our setup). One conclusion we can draw from our research is that
the clang infrastructure fits amazingly well to something which was not
its main use case. The grand total of our diffs against clang-9 is: `62
files changed, 1294 insertions(+), 231 deletions(-)`. Cling is currently
being upgraded from llvm-5 to llvm-9.

A major weakness of cling's infrastructure is that it does not work with
the clang Action infrastructure due to the lack of an
IncrementalAction.  A possible way forward would be to implement a
clang::IncrementalAction as a starting point. This way we should be able
to reduce the amount of setup necessary to use the incremental
infrastructure in clang. However, this will be a bit of a testing
challenge -- cling lives downstream and some of the new code may be
impossible to pick straight away and use. Building a mainline example
tool such as clang-repl which gives us a way to test that incremental
case or repurpose the already existing clang-interpreter may  be able to
address the issue. The major risk of the task is avoiding code in the
clang mainline which is untested by its HEP production environment.
There are several other types of patches to the ROOT fork of Clang,
including ones  in the context of performance,towards  C++ modules
support (D41416), and storage (does not have a patch yet but has an open
projects entry and somebody working on it). These patches can be
considered in parallel independently on the rest.

Extend and Generalize the Language Interoperability Layer Around Cling
---

HEP has extensive experience with on-demand python interoperability
using cppyy[6], which is built around the type information provided by
cling. Unlike tools with custom parsers such as swig and sip and tools
built on top of C-APIs such as boost.python and pybind11, cling can
provide information about memory management patterns (eg refcounting)
and instantiate templates on the fly.We feel that functionality may not
be of general interest to the llvm community but we will prepare another
RFC and send it here later on to gather feedback.


Extend and Generalize the OpenCL/CUDA Support in Cling
---

Cling can incrementally compile CUDA code [7-8] allowing easier set up
and enabling some interesting use cases. There are a number of planned
improvements including talking to HIP [9] and SYCL to support more
hardware architectures.



The primary focus of our work is to upstreaming functionality required
to build an incremental compiler and rework cling build against vanilla
clang and llvm. The last two points are to give the scope of the work
which we will be doing the next 2-3 years. We will send here RFCs for
both of them to trigger technical discussion if there is interest in
pursuing this direction.


Collaboration
===

Open source development nowadays relies on reviewers. LLVM is no
different and we will probably disturb a good number of people in the
community ;)We would like to invite anybody interested in joining our
incremental C++ activities to our open every second week calls.
Announcements will be done via google group: compiler-research-announce
(https://groups.google.com/g/compiler-research-announce).



Many thanks!


David & Vassil

References
===
[1] ROOT GitHub https://github.com/root-project/root
[2] ROOT https://root.cern
[3] Cling https://github.com/root-project/cling
[4] Xeus-Cling
https://blog.jupyter.org/xeus-is-now-a-jupyter-subproject-c4ec5a1bf30b
[5] Cling – The New Interactive Interpreter for ROOT 6,
https://iopscience.iop.org/article/10.1088/1742-6596/396/5/052071
[6] High-performance Python-C++ bindings with PyPy and Cling,
https://dl.acm.org/doi/10.5555/3019083.3019087
[7]
https://indico.cern.ch/event/697389/contributions/3085538/attachments/1712698/2761717/2018_09_10_cling_CUDA.pdf
[8] CUDA C++ in Jupyter: Adding CUDA Runtime Support to Cling',
https://zenodo.org/record/3713753#.Xu8jqvJRXxU
[9] HIP Programming Guide
https://rocmdocs.amd.com/en/latest/Programming_Guides/HIP-GUIDE.html

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Re: [RFC] Moving (parts of) the Cling REPL in Clang

David Chisnall via cfe-dev
In reply to this post by David Chisnall via cfe-dev

On 7/10/20 1:57 PM, Vassil Vassilev wrote:

> On 7/10/20 6:43 AM, JF Bastien wrote:
>> I like cling, and having it integrated with the rest of the project
>> would be neat. I agree with Hal’s suggestion to explain the design of
>> what remains. It sounds like a pretty small amount of code.
>
>
>   JF, Hal, did you mean you want a design document of how cling in
> general or a design RFC for the patches we have? A design document for
> cling would be quite large and will take us some time to write up.
> OTOH, we could relatively easily give a rationale for each patch.


I had in mind something that's probably in between. Something that
explains the patches and enough about how they fit into a larger system
that we can reason about the context.

  -Hal


>
>
>>
>>
>>> On Jul 9, 2020, at 7:25 PM, Hal Finkel via cfe-dev
>>> <[hidden email]> wrote:
>>>
>>> I think that it would be great to have infrastructure for
>>> incremental C++ compilation, supporting interactive use,
>>> just-in-time compilation, and so on. I think that the best way to
>>> deal with the patches, etc., as well as IncrementalAction, is to
>>> first send an RFC explaining the overall design.
>>>
>>>   -Hal
>>>
>>> On 7/9/20 3:46 PM, Vassil Vassilev via cfe-dev wrote:
>>>> Motivation
>>>> ===
>>>>
>>>> Over the last decade we have developed an interactive,
>>>> interpretative C++ (aka REPL) as part of the high-energy physics
>>>> (HEP) data analysis project -- ROOT [1-2]. We invested a
>>>> significant  effort to replace the CINT C++ interpreter with a
>>>> newly implemented REPL based on llvm -- cling [3]. The cling
>>>> infrastructure is a core component of the data analysis framework
>>>> of ROOT and runs in production for approximately 5 years.
>>>>
>>>> Cling is also  a standalone tool, which has a growing community
>>>> outside of our field. Cling’s user community includes users in
>>>> finance, biology and in a few companies with proprietary software.
>>>> For example, there is a xeus-cling jupyter kernel [4]. One of the
>>>> major challenges we face to foster that community is  our
>>>> cling-related patches in llvm and clang forks. The benefits of
>>>> using the LLVM community standards for code reviews, release cycles
>>>> and integration has been mentioned a number of times by our
>>>> "external" users.
>>>>
>>>> Last year we were awarded an NSF grant to improve cling's
>>>> sustainability and make it a standalone tool. We thank the LLVM
>>>> Foundation Board for supporting us with a non-binding letter of
>>>> collaboration which was essential for getting this grant.
>>>>
>>>>
>>>> Background
>>>> ===
>>>>
>>>> Cling is a C++ interpreter built on top of clang and llvm. In a
>>>> nutshell, it uses clang's incremental compilation facilities to
>>>> process code chunk-by-chunk by assuming an ever-growing translation
>>>> unit [5]. Then code is lowered into llvm IR and run by the llvm
>>>> jit. Cling has implemented some language "extensions" such as
>>>> execution statements on the global scope and error recovery. Cling
>>>> is in the core of HEP -- it is heavily used during data analysis of
>>>> exabytes of particle physics data coming from the Large Hadron
>>>> Collider (LHC) and other particle physics experiments.
>>>>
>>>>
>>>> Plans
>>>> ===
>>>>
>>>> The project foresees three main directions -- move parts of cling
>>>> upstream along with the clang and llvm features that enable them;
>>>> extend and generalize the language interoperability layer around
>>>> cling; and extend and generalize the OpenCL/CUDA support in cling.
>>>> We are at the early stages of the project and this email intends to
>>>> be an RFC for the first part -- upstreaming parts of cling. Please
>>>> do share your thoughts on the rest, too.
>>>>
>>>>
>>>> Moving Parts of Cling Upstream
>>>> ---
>>>>
>>>> Over the years we have slowly moved some patches upstream. However
>>>> we still have around 100 patches in the clang fork. Most of them
>>>> are in the context of extending the incremental compilation support
>>>> for clang. The incremental compilation poses some challenges in the
>>>> clang infrastructure. For example, we need to tune CodeGen to work
>>>> with multiple llvm::Module instances, and finalize per each
>>>> end-of-translation unit (we have multiple of them). Other changes
>>>> include small adjustments in the FileManager's caching mechanism,
>>>> and bug fixes in the SourceManager (code which can be reached
>>>> mostly from within our setup). One conclusion we can draw from our
>>>> research is that the clang infrastructure fits amazingly well to
>>>> something which was not its main use case. The grand total of our
>>>> diffs against clang-9 is: `62 files changed, 1294 insertions(+),
>>>> 231 deletions(-)`. Cling is currently being upgraded from llvm-5 to
>>>> llvm-9.
>>>>
>>>> A major weakness of cling's infrastructure is that it does not work
>>>> with the clang Action infrastructure due to the lack of an
>>>> IncrementalAction.  A possible way forward would be to implement a
>>>> clang::IncrementalAction as a starting point. This way we should be
>>>> able to reduce the amount of setup necessary to use the incremental
>>>> infrastructure in clang. However, this will be a bit of a testing
>>>> challenge -- cling lives downstream and some of the new code may be
>>>> impossible to pick straight away and use. Building a mainline
>>>> example tool such as clang-repl which gives us a way to test that
>>>> incremental case or repurpose the already existing
>>>> clang-interpreter may  be able to address the issue. The major risk
>>>> of the task is avoiding code in the clang mainline which is
>>>> untested by its HEP production environment.
>>>> There are several other types of patches to the ROOT fork of Clang,
>>>> including ones  in the context of performance,towards  C++ modules
>>>> support (D41416), and storage (does not have a patch yet but has an
>>>> open projects entry and somebody working on it). These patches can
>>>> be considered in parallel independently on the rest.
>>>>
>>>> Extend and Generalize the Language Interoperability Layer Around Cling
>>>> ---
>>>>
>>>> HEP has extensive experience with on-demand python interoperability
>>>> using cppyy[6], which is built around the type information provided
>>>> by cling. Unlike tools with custom parsers such as swig and sip and
>>>> tools built on top of C-APIs such as boost.python and pybind11,
>>>> cling can provide information about memory management patterns (eg
>>>> refcounting) and instantiate templates on the fly.We feel that
>>>> functionality may not be of general interest to the llvm community
>>>> but we will prepare another RFC and send it here later on to gather
>>>> feedback.
>>>>
>>>>
>>>> Extend and Generalize the OpenCL/CUDA Support in Cling
>>>> ---
>>>>
>>>> Cling can incrementally compile CUDA code [7-8] allowing easier set
>>>> up and enabling some interesting use cases. There are a number of
>>>> planned improvements including talking to HIP [9] and SYCL to
>>>> support more hardware architectures.
>>>>
>>>>
>>>>
>>>> The primary focus of our work is to upstreaming functionality
>>>> required to build an incremental compiler and rework cling build
>>>> against vanilla clang and llvm. The last two points are to give the
>>>> scope of the work which we will be doing the next 2-3 years. We
>>>> will send here RFCs for both of them to trigger technical
>>>> discussion if there is interest in pursuing this direction.
>>>>
>>>>
>>>> Collaboration
>>>> ===
>>>>
>>>> Open source development nowadays relies on reviewers. LLVM is no
>>>> different and we will probably disturb a good number of people in
>>>> the community ;)We would like to invite anybody interested in
>>>> joining our incremental C++ activities to our open every second
>>>> week calls. Announcements will be done via google group:
>>>> compiler-research-announce
>>>> (https://groups.google.com/g/compiler-research-announce).
>>>>
>>>>
>>>>
>>>> Many thanks!
>>>>
>>>>
>>>> David & Vassil
>>>>
>>>> References
>>>> ===
>>>> [1] ROOT GitHub https://github.com/root-project/root
>>>> [2] ROOT https://root.cern
>>>> [3] Cling https://github.com/root-project/cling
>>>> [4] Xeus-Cling
>>>> https://blog.jupyter.org/xeus-is-now-a-jupyter-subproject-c4ec5a1bf30b
>>>> [5] Cling – The New Interactive Interpreter for ROOT 6,
>>>> https://iopscience.iop.org/article/10.1088/1742-6596/396/5/052071
>>>> [6] High-performance Python-C++ bindings with PyPy and Cling,
>>>> https://dl.acm.org/doi/10.5555/3019083.3019087
>>>> [7]
>>>> https://indico.cern.ch/event/697389/contributions/3085538/attachments/1712698/2761717/2018_09_10_cling_CUDA.pdf
>>>> [8] CUDA C++ in Jupyter: Adding CUDA Runtime Support to Cling',
>>>> https://zenodo.org/record/3713753#.Xu8jqvJRXxU
>>>> [9] HIP Programming Guide
>>>> https://rocmdocs.amd.com/en/latest/Programming_Guides/HIP-GUIDE.html
>>>>
>>>> _______________________________________________
>>>> cfe-dev mailing list
>>>> [hidden email]
>>>> https://lists.llvm.org/cgi-bin/mailman/listinfo/cfe-dev
>>> --
>>> Hal Finkel
>>> Lead, Compiler Technology and Programming Languages
>>> Leadership Computing Facility
>>> Argonne National Laboratory
>>>
>>> _______________________________________________
>>> cfe-dev mailing list
>>> [hidden email]
>>> https://lists.llvm.org/cgi-bin/mailman/listinfo/cfe-dev
>
>
--
Hal Finkel
Lead, Compiler Technology and Programming Languages
Leadership Computing Facility
Argonne National Laboratory

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Re: [RFC] Moving (parts of) the Cling REPL in Clang

David Chisnall via cfe-dev
In reply to this post by David Chisnall via cfe-dev
Hi Richard,

On 7/10/20 11:10 PM, Richard Smith wrote:
Hi Vassil,

This is a very exciting proposal that I can imagine bringing important benefits to the existing cling users and also to the clang user and developer community. Thank you for all the work you and your team have done on cling so far and for offering to bring that work under the LLVM umbrella!

Are you imagining cling being part of the clang repository, or a separate LLVM subproject (with only the changes necessary to support cling-style uses of the clang libraries added to the clang tree)?


  Good question. In principle cling was developed with the idea to become a separate LLVM subproject. Although I'd easily see it fit in clang/tools/.


  Nominally, cling has "high-energy physics"-specific features such as the so called 'meta commands'. For example, `[cling] .L some_file` would try to load a library called some_file.so and if it does not exist, try #include-ing a header with that name; `[cling] .x script.C` includes script.C and calls a function named `script`. I can imagine that broader community may not like/use that. If we start trimming down features like that then it won't really be cling anymore. Here is what I would imagine as a way forward:

  1. Land as many cling/"incremental compilation"-related patches as we can in clang.
  2. Build a simple tool, let's use a strawman name -- clang-repl, which only does the basics. For example, one can feed it incremental C++ and execute it.
  3. Rework cling to use that infrastructure -- ideally, implementing it's specific meta commands and other domain-specific features such as dynamic scopes.

  We could move any of the cling features which the broader community finds useful closer to clang. For the moment I am being conservative as this will also give us the opportunity to rethink some of the features.

  The hard part is what lives where. First bullet point is clear. The second -- not so much. Clang has a clang-interpreter in its examples folder and it looks a little unmaintained. Maybe we can start repurposing that to match 2.

  As for cling itself there are some challenges we should try to solve. Our community lives downstream (currently llvm-5) and a straight-forward llvm upgrade + bugfixing takes around 3 months due to the nature of our software stacks. It would be a non-trivial task to move the cling-based development in llvm upstream. My worry is that HEP-cling will soon depart from LLVM-cling if we don't get both communities on the same codebase (we have experienced such a problem with the getFullyQualified* interfaces). I am hoping that a middleman, such as clang-repl, can help. When we move parts of cling in clang we will develop and test the required functionality using clang-repl. This way users will enjoy cling-like experience and when cling upgrades its llvm its codebase will become smaller in size.

  Am I making sense?



On Thu, 9 Jul 2020 at 13:46, Vassil Vassilev via cfe-dev <[hidden email]> wrote:
Motivation
===

Over the last decade we have developed an interactive, interpretative
C++ (aka REPL) as part of the high-energy physics (HEP) data analysis
project -- ROOT [1-2]. We invested a significant  effort to replace the
CINT C++ interpreter with a newly implemented REPL based on llvm --
cling [3]. The cling infrastructure is a core component of the data
analysis framework of ROOT and runs in production for approximately 5
years.

Cling is also  a standalone tool, which has a growing community outside
of our field. Cling’s user community includes users in finance, biology
and in a few companies with proprietary software. For example, there is
a xeus-cling jupyter kernel [4]. One of the major challenges we face to
foster that community is  our cling-related patches in llvm and clang
forks. The benefits of using the LLVM community standards for code
reviews, release cycles and integration has been mentioned a number of
times by our "external" users.

Last year we were awarded an NSF grant to improve cling's sustainability
and make it a standalone tool. We thank the LLVM Foundation Board for
supporting us with a non-binding letter of collaboration which was
essential for getting this grant.


Background
===

Cling is a C++ interpreter built on top of clang and llvm. In a
nutshell, it uses clang's incremental compilation facilities to process
code chunk-by-chunk by assuming an ever-growing translation unit [5].
Then code is lowered into llvm IR and run by the llvm jit. Cling has
implemented some language "extensions" such as execution statements on
the global scope and error recovery. Cling is in the core of HEP -- it
is heavily used during data analysis of exabytes of particle physics
data coming from the Large Hadron Collider (LHC) and other particle
physics experiments.


Plans
===

The project foresees three main directions -- move parts of cling
upstream along with the clang and llvm features that enable them; extend
and generalize the language interoperability layer around cling; and
extend and generalize the OpenCL/CUDA support in cling. We are at the
early stages of the project and this email intends to be an RFC for the
first part -- upstreaming parts of cling. Please do share your thoughts
on the rest, too.


Moving Parts of Cling Upstream
---

Over the years we have slowly moved some patches upstream. However we
still have around 100 patches in the clang fork. Most of them are in the
context of extending the incremental compilation support for clang. The
incremental compilation poses some challenges in the clang
infrastructure. For example, we need to tune CodeGen to work with
multiple llvm::Module instances, and finalize per each
end-of-translation unit (we have multiple of them). Other changes
include small adjustments in the FileManager's caching mechanism, and
bug fixes in the SourceManager (code which can be reached mostly from
within our setup). One conclusion we can draw from our research is that
the clang infrastructure fits amazingly well to something which was not
its main use case. The grand total of our diffs against clang-9 is: `62
files changed, 1294 insertions(+), 231 deletions(-)`. Cling is currently
being upgraded from llvm-5 to llvm-9.

A major weakness of cling's infrastructure is that it does not work with
the clang Action infrastructure due to the lack of an
IncrementalAction.  A possible way forward would be to implement a
clang::IncrementalAction as a starting point. This way we should be able
to reduce the amount of setup necessary to use the incremental
infrastructure in clang. However, this will be a bit of a testing
challenge -- cling lives downstream and some of the new code may be
impossible to pick straight away and use. Building a mainline example
tool such as clang-repl which gives us a way to test that incremental
case or repurpose the already existing clang-interpreter may  be able to
address the issue. The major risk of the task is avoiding code in the
clang mainline which is untested by its HEP production environment.
There are several other types of patches to the ROOT fork of Clang,
including ones  in the context of performance,towards  C++ modules
support (D41416), and storage (does not have a patch yet but has an open
projects entry and somebody working on it). These patches can be
considered in parallel independently on the rest.

Extend and Generalize the Language Interoperability Layer Around Cling
---

HEP has extensive experience with on-demand python interoperability
using cppyy[6], which is built around the type information provided by
cling. Unlike tools with custom parsers such as swig and sip and tools
built on top of C-APIs such as boost.python and pybind11, cling can
provide information about memory management patterns (eg refcounting)
and instantiate templates on the fly.We feel that functionality may not
be of general interest to the llvm community but we will prepare another
RFC and send it here later on to gather feedback.


Extend and Generalize the OpenCL/CUDA Support in Cling
---

Cling can incrementally compile CUDA code [7-8] allowing easier set up
and enabling some interesting use cases. There are a number of planned
improvements including talking to HIP [9] and SYCL to support more
hardware architectures.



The primary focus of our work is to upstreaming functionality required
to build an incremental compiler and rework cling build against vanilla
clang and llvm. The last two points are to give the scope of the work
which we will be doing the next 2-3 years. We will send here RFCs for
both of them to trigger technical discussion if there is interest in
pursuing this direction.


Collaboration
===

Open source development nowadays relies on reviewers. LLVM is no
different and we will probably disturb a good number of people in the
community ;)We would like to invite anybody interested in joining our
incremental C++ activities to our open every second week calls.
Announcements will be done via google group: compiler-research-announce
(https://groups.google.com/g/compiler-research-announce).



Many thanks!


David & Vassil

References
===
[1] ROOT GitHub https://github.com/root-project/root
[2] ROOT https://root.cern
[3] Cling https://github.com/root-project/cling
[4] Xeus-Cling
https://blog.jupyter.org/xeus-is-now-a-jupyter-subproject-c4ec5a1bf30b
[5] Cling – The New Interactive Interpreter for ROOT 6,
https://iopscience.iop.org/article/10.1088/1742-6596/396/5/052071
[6] High-performance Python-C++ bindings with PyPy and Cling,
https://dl.acm.org/doi/10.5555/3019083.3019087
[7]
https://indico.cern.ch/event/697389/contributions/3085538/attachments/1712698/2761717/2018_09_10_cling_CUDA.pdf
[8] CUDA C++ in Jupyter: Adding CUDA Runtime Support to Cling',
https://zenodo.org/record/3713753#.Xu8jqvJRXxU
[9] HIP Programming Guide
https://rocmdocs.amd.com/en/latest/Programming_Guides/HIP-GUIDE.html

_______________________________________________
cfe-dev mailing list
[hidden email]
https://lists.llvm.org/cgi-bin/mailman/listinfo/cfe-dev



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Re: [RFC] Moving (parts of) the Cling REPL in Clang

David Chisnall via cfe-dev
In reply to this post by David Chisnall via cfe-dev


On Jul 10, 2020, at 1:55 PM, Hal Finkel <[hidden email]> wrote:


On 7/10/20 1:57 PM, Vassil Vassilev wrote:
On 7/10/20 6:43 AM, JF Bastien wrote:
I like cling, and having it integrated with the rest of the project would be neat. I agree with Hal’s suggestion to explain the design of what remains. It sounds like a pretty small amount of code.


  JF, Hal, did you mean you want a design document of how cling in general or a design RFC for the patches we have? A design document for cling would be quite large and will take us some time to write up. OTOH, we could relatively easily give a rationale for each patch.


I had in mind something that's probably in between. Something that explains the patches and enough about how they fit into a larger system that we can reason about the context.

Maybe a purpose would be more useful to understand your request? I assume you meant “I’d like us to understand what we’re signing up to maintain, and why it’s useful to do things this way”. In particular, if there’s undue burden in a particular component, and the code could be changed to work differently with less support overhead, then we’d want to identify this fact ahead of time.

I’m guessing at what Hal is asking, LMK if that’s not what you had in mind!


 -Hal






On Jul 9, 2020, at 7:25 PM, Hal Finkel via cfe-dev <[hidden email]> wrote:

I think that it would be great to have infrastructure for incremental C++ compilation, supporting interactive use, just-in-time compilation, and so on. I think that the best way to deal with the patches, etc., as well as IncrementalAction, is to first send an RFC explaining the overall design.

  -Hal

On 7/9/20 3:46 PM, Vassil Vassilev via cfe-dev wrote:
Motivation
===

Over the last decade we have developed an interactive, interpretative C++ (aka REPL) as part of the high-energy physics (HEP) data analysis project -- ROOT [1-2]. We invested a significant  effort to replace the CINT C++ interpreter with a newly implemented REPL based on llvm -- cling [3]. The cling infrastructure is a core component of the data analysis framework of ROOT and runs in production for approximately 5 years.

Cling is also  a standalone tool, which has a growing community outside of our field. Cling’s user community includes users in finance, biology and in a few companies with proprietary software. For example, there is a xeus-cling jupyter kernel [4]. One of the major challenges we face to foster that community is  our cling-related patches in llvm and clang forks. The benefits of using the LLVM community standards for code reviews, release cycles and integration has been mentioned a number of times by our "external" users.

Last year we were awarded an NSF grant to improve cling's sustainability and make it a standalone tool. We thank the LLVM Foundation Board for supporting us with a non-binding letter of collaboration which was essential for getting this grant.


Background
===

Cling is a C++ interpreter built on top of clang and llvm. In a nutshell, it uses clang's incremental compilation facilities to process code chunk-by-chunk by assuming an ever-growing translation unit [5]. Then code is lowered into llvm IR and run by the llvm jit. Cling has implemented some language "extensions" such as execution statements on the global scope and error recovery. Cling is in the core of HEP -- it is heavily used during data analysis of exabytes of particle physics data coming from the Large Hadron Collider (LHC) and other particle physics experiments.


Plans
===

The project foresees three main directions -- move parts of cling upstream along with the clang and llvm features that enable them; extend and generalize the language interoperability layer around cling; and extend and generalize the OpenCL/CUDA support in cling. We are at the early stages of the project and this email intends to be an RFC for the first part -- upstreaming parts of cling. Please do share your thoughts on the rest, too.


Moving Parts of Cling Upstream
---

Over the years we have slowly moved some patches upstream. However we still have around 100 patches in the clang fork. Most of them are in the context of extending the incremental compilation support for clang. The incremental compilation poses some challenges in the clang infrastructure. For example, we need to tune CodeGen to work with multiple llvm::Module instances, and finalize per each end-of-translation unit (we have multiple of them). Other changes include small adjustments in the FileManager's caching mechanism, and bug fixes in the SourceManager (code which can be reached mostly from within our setup). One conclusion we can draw from our research is that the clang infrastructure fits amazingly well to something which was not its main use case. The grand total of our diffs against clang-9 is: `62 files changed, 1294 insertions(+), 231 deletions(-)`. Cling is currently being upgraded from llvm-5 to llvm-9.

A major weakness of cling's infrastructure is that it does not work with the clang Action infrastructure due to the lack of an IncrementalAction.  A possible way forward would be to implement a clang::IncrementalAction as a starting point. This way we should be able to reduce the amount of setup necessary to use the incremental infrastructure in clang. However, this will be a bit of a testing challenge -- cling lives downstream and some of the new code may be impossible to pick straight away and use. Building a mainline example tool such as clang-repl which gives us a way to test that incremental case or repurpose the already existing clang-interpreter may  be able to address the issue. The major risk of the task is avoiding code in the clang mainline which is untested by its HEP production environment.
There are several other types of patches to the ROOT fork of Clang, including ones  in the context of performance,towards  C++ modules support (D41416), and storage (does not have a patch yet but has an open projects entry and somebody working on it). These patches can be considered in parallel independently on the rest.

Extend and Generalize the Language Interoperability Layer Around Cling
---

HEP has extensive experience with on-demand python interoperability using cppyy[6], which is built around the type information provided by cling. Unlike tools with custom parsers such as swig and sip and tools built on top of C-APIs such as boost.python and pybind11, cling can provide information about memory management patterns (eg refcounting) and instantiate templates on the fly.We feel that functionality may not be of general interest to the llvm community but we will prepare another RFC and send it here later on to gather feedback.


Extend and Generalize the OpenCL/CUDA Support in Cling
---

Cling can incrementally compile CUDA code [7-8] allowing easier set up and enabling some interesting use cases. There are a number of planned improvements including talking to HIP [9] and SYCL to support more hardware architectures.



The primary focus of our work is to upstreaming functionality required to build an incremental compiler and rework cling build against vanilla clang and llvm. The last two points are to give the scope of the work which we will be doing the next 2-3 years. We will send here RFCs for both of them to trigger technical discussion if there is interest in pursuing this direction.


Collaboration
===

Open source development nowadays relies on reviewers. LLVM is no different and we will probably disturb a good number of people in the community ;)We would like to invite anybody interested in joining our incremental C++ activities to our open every second week calls. Announcements will be done via google group: compiler-research-announce (https://groups.google.com/g/compiler-research-announce).



Many thanks!


David & Vassil

References
===
[1] ROOT GitHub https://github.com/root-project/root
[2] ROOT https://root.cern
[3] Cling https://github.com/root-project/cling
[4] Xeus-Cling https://blog.jupyter.org/xeus-is-now-a-jupyter-subproject-c4ec5a1bf30b
[5] Cling – The New Interactive Interpreter for ROOT 6, https://iopscience.iop.org/article/10.1088/1742-6596/396/5/052071
[6] High-performance Python-C++ bindings with PyPy and Cling, https://dl.acm.org/doi/10.5555/3019083.3019087
[7] https://indico.cern.ch/event/697389/contributions/3085538/attachments/1712698/2761717/2018_09_10_cling_CUDA.pdf
[8] CUDA C++ in Jupyter: Adding CUDA Runtime Support to Cling', https://zenodo.org/record/3713753#.Xu8jqvJRXxU
[9] HIP Programming Guide https://rocmdocs.amd.com/en/latest/Programming_Guides/HIP-GUIDE.html

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Leadership Computing Facility
Argonne National Laboratory

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Re: [RFC] Moving (parts of) the Cling REPL in Clang

David Chisnall via cfe-dev
On 7/11/20 12:00 AM, JF Bastien wrote:


On Jul 10, 2020, at 1:55 PM, Hal Finkel <[hidden email]> wrote:


On 7/10/20 1:57 PM, Vassil Vassilev wrote:
On 7/10/20 6:43 AM, JF Bastien wrote:
I like cling, and having it integrated with the rest of the project would be neat. I agree with Hal’s suggestion to explain the design of what remains. It sounds like a pretty small amount of code.


  JF, Hal, did you mean you want a design document of how cling in general or a design RFC for the patches we have? A design document for cling would be quite large and will take us some time to write up. OTOH, we could relatively easily give a rationale for each patch.


I had in mind something that's probably in between. Something that explains the patches and enough about how they fit into a larger system that we can reason about the context.

Maybe a purpose would be more useful to understand your request? I assume you meant “I’d like us to understand what we’re signing up to maintain, and why it’s useful to do things this way”. In particular, if there’s undue burden in a particular component, and the code could be changed to work differently with less support overhead, then we’d want to identify this fact ahead of time.

I’m guessing at what Hal is asking, LMK if that’s not what you had in mind!


  Thanks for the clarification. Sure, we can do that I was hoping that to be part of the particular patch review process. Also, if that's the preference, we can write a short-ish doc with some patch classification and explanations. Btw, I've uploaded the cling-specific patches against the clang-9 codebase: https://github.com/vgvassilev/clang/commits/upgrade_llvm90 Our production LLVM-5 is patch free, I had to introduce some patches in llvm-9 but thanks to Lang I know how to get rid of them.




 -Hal






On Jul 9, 2020, at 7:25 PM, Hal Finkel via cfe-dev <[hidden email]> wrote:

I think that it would be great to have infrastructure for incremental C++ compilation, supporting interactive use, just-in-time compilation, and so on. I think that the best way to deal with the patches, etc., as well as IncrementalAction, is to first send an RFC explaining the overall design.

  -Hal

On 7/9/20 3:46 PM, Vassil Vassilev via cfe-dev wrote:
Motivation
===

Over the last decade we have developed an interactive, interpretative C++ (aka REPL) as part of the high-energy physics (HEP) data analysis project -- ROOT [1-2]. We invested a significant  effort to replace the CINT C++ interpreter with a newly implemented REPL based on llvm -- cling [3]. The cling infrastructure is a core component of the data analysis framework of ROOT and runs in production for approximately 5 years.

Cling is also  a standalone tool, which has a growing community outside of our field. Cling’s user community includes users in finance, biology and in a few companies with proprietary software. For example, there is a xeus-cling jupyter kernel [4]. One of the major challenges we face to foster that community is  our cling-related patches in llvm and clang forks. The benefits of using the LLVM community standards for code reviews, release cycles and integration has been mentioned a number of times by our "external" users.

Last year we were awarded an NSF grant to improve cling's sustainability and make it a standalone tool. We thank the LLVM Foundation Board for supporting us with a non-binding letter of collaboration which was essential for getting this grant.


Background
===

Cling is a C++ interpreter built on top of clang and llvm. In a nutshell, it uses clang's incremental compilation facilities to process code chunk-by-chunk by assuming an ever-growing translation unit [5]. Then code is lowered into llvm IR and run by the llvm jit. Cling has implemented some language "extensions" such as execution statements on the global scope and error recovery. Cling is in the core of HEP -- it is heavily used during data analysis of exabytes of particle physics data coming from the Large Hadron Collider (LHC) and other particle physics experiments.


Plans
===

The project foresees three main directions -- move parts of cling upstream along with the clang and llvm features that enable them; extend and generalize the language interoperability layer around cling; and extend and generalize the OpenCL/CUDA support in cling. We are at the early stages of the project and this email intends to be an RFC for the first part -- upstreaming parts of cling. Please do share your thoughts on the rest, too.


Moving Parts of Cling Upstream
---

Over the years we have slowly moved some patches upstream. However we still have around 100 patches in the clang fork. Most of them are in the context of extending the incremental compilation support for clang. The incremental compilation poses some challenges in the clang infrastructure. For example, we need to tune CodeGen to work with multiple llvm::Module instances, and finalize per each end-of-translation unit (we have multiple of them). Other changes include small adjustments in the FileManager's caching mechanism, and bug fixes in the SourceManager (code which can be reached mostly from within our setup). One conclusion we can draw from our research is that the clang infrastructure fits amazingly well to something which was not its main use case. The grand total of our diffs against clang-9 is: `62 files changed, 1294 insertions(+), 231 deletions(-)`. Cling is currently being upgraded from llvm-5 to llvm-9.

A major weakness of cling's infrastructure is that it does not work with the clang Action infrastructure due to the lack of an IncrementalAction.  A possible way forward would be to implement a clang::IncrementalAction as a starting point. This way we should be able to reduce the amount of setup necessary to use the incremental infrastructure in clang. However, this will be a bit of a testing challenge -- cling lives downstream and some of the new code may be impossible to pick straight away and use. Building a mainline example tool such as clang-repl which gives us a way to test that incremental case or repurpose the already existing clang-interpreter may  be able to address the issue. The major risk of the task is avoiding code in the clang mainline which is untested by its HEP production environment.
There are several other types of patches to the ROOT fork of Clang, including ones  in the context of performance,towards  C++ modules support (D41416), and storage (does not have a patch yet but has an open projects entry and somebody working on it). These patches can be considered in parallel independently on the rest.

Extend and Generalize the Language Interoperability Layer Around Cling
---

HEP has extensive experience with on-demand python interoperability using cppyy[6], which is built around the type information provided by cling. Unlike tools with custom parsers such as swig and sip and tools built on top of C-APIs such as boost.python and pybind11, cling can provide information about memory management patterns (eg refcounting) and instantiate templates on the fly.We feel that functionality may not be of general interest to the llvm community but we will prepare another RFC and send it here later on to gather feedback.


Extend and Generalize the OpenCL/CUDA Support in Cling
---

Cling can incrementally compile CUDA code [7-8] allowing easier set up and enabling some interesting use cases. There are a number of planned improvements including talking to HIP [9] and SYCL to support more hardware architectures.



The primary focus of our work is to upstreaming functionality required to build an incremental compiler and rework cling build against vanilla clang and llvm. The last two points are to give the scope of the work which we will be doing the next 2-3 years. We will send here RFCs for both of them to trigger technical discussion if there is interest in pursuing this direction.


Collaboration
===

Open source development nowadays relies on reviewers. LLVM is no different and we will probably disturb a good number of people in the community ;)We would like to invite anybody interested in joining our incremental C++ activities to our open every second week calls. Announcements will be done via google group: compiler-research-announce (https://groups.google.com/g/compiler-research-announce).



Many thanks!


David & Vassil

References
===
[1] ROOT GitHub https://github.com/root-project/root
[2] ROOT https://root.cern
[3] Cling https://github.com/root-project/cling
[4] Xeus-Cling https://blog.jupyter.org/xeus-is-now-a-jupyter-subproject-c4ec5a1bf30b
[5] Cling – The New Interactive Interpreter for ROOT 6, https://iopscience.iop.org/article/10.1088/1742-6596/396/5/052071
[6] High-performance Python-C++ bindings with PyPy and Cling, https://dl.acm.org/doi/10.5555/3019083.3019087
[7] https://indico.cern.ch/event/697389/contributions/3085538/attachments/1712698/2761717/2018_09_10_cling_CUDA.pdf
[8] CUDA C++ in Jupyter: Adding CUDA Runtime Support to Cling', https://zenodo.org/record/3713753#.Xu8jqvJRXxU
[9] HIP Programming Guide https://rocmdocs.amd.com/en/latest/Programming_Guides/HIP-GUIDE.html

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Leadership Computing Facility
Argonne National Laboratory

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Lead, Compiler Technology and Programming Languages
Leadership Computing Facility
Argonne National Laboratory



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Re: [RFC] Moving (parts of) the Cling REPL in Clang

David Chisnall via cfe-dev
In reply to this post by David Chisnall via cfe-dev


On 7/10/20 4:00 PM, JF Bastien wrote:


On Jul 10, 2020, at 1:55 PM, Hal Finkel <[hidden email]> wrote:


On 7/10/20 1:57 PM, Vassil Vassilev wrote:
On 7/10/20 6:43 AM, JF Bastien wrote:
I like cling, and having it integrated with the rest of the project would be neat. I agree with Hal’s suggestion to explain the design of what remains. It sounds like a pretty small amount of code.


  JF, Hal, did you mean you want a design document of how cling in general or a design RFC for the patches we have? A design document for cling would be quite large and will take us some time to write up. OTOH, we could relatively easily give a rationale for each patch.


I had in mind something that's probably in between. Something that explains the patches and enough about how they fit into a larger system that we can reason about the context.

Maybe a purpose would be more useful to understand your request? I assume you meant “I’d like us to understand what we’re signing up to maintain, and why it’s useful to do things this way”. In particular, if there’s undue burden in a particular component, and the code could be changed to work differently with less support overhead, then we’d want to identify this fact ahead of time.

I’m guessing at what Hal is asking, LMK if that’s not what you had in mind!


Yes. To understand how all of the pieces fit together to enable support for incremental compilation of C++ code. Once everything is in place, if I wanted to use the infrastructure to do some kind of incremental compilation of C++, what would I do? And what do the set of patches aim to do to get us there?

 -Hal




 -Hal






On Jul 9, 2020, at 7:25 PM, Hal Finkel via cfe-dev <[hidden email]> wrote:

I think that it would be great to have infrastructure for incremental C++ compilation, supporting interactive use, just-in-time compilation, and so on. I think that the best way to deal with the patches, etc., as well as IncrementalAction, is to first send an RFC explaining the overall design.

  -Hal

On 7/9/20 3:46 PM, Vassil Vassilev via cfe-dev wrote:
Motivation
===

Over the last decade we have developed an interactive, interpretative C++ (aka REPL) as part of the high-energy physics (HEP) data analysis project -- ROOT [1-2]. We invested a significant  effort to replace the CINT C++ interpreter with a newly implemented REPL based on llvm -- cling [3]. The cling infrastructure is a core component of the data analysis framework of ROOT and runs in production for approximately 5 years.

Cling is also  a standalone tool, which has a growing community outside of our field. Cling’s user community includes users in finance, biology and in a few companies with proprietary software. For example, there is a xeus-cling jupyter kernel [4]. One of the major challenges we face to foster that community is  our cling-related patches in llvm and clang forks. The benefits of using the LLVM community standards for code reviews, release cycles and integration has been mentioned a number of times by our "external" users.

Last year we were awarded an NSF grant to improve cling's sustainability and make it a standalone tool. We thank the LLVM Foundation Board for supporting us with a non-binding letter of collaboration which was essential for getting this grant.


Background
===

Cling is a C++ interpreter built on top of clang and llvm. In a nutshell, it uses clang's incremental compilation facilities to process code chunk-by-chunk by assuming an ever-growing translation unit [5]. Then code is lowered into llvm IR and run by the llvm jit. Cling has implemented some language "extensions" such as execution statements on the global scope and error recovery. Cling is in the core of HEP -- it is heavily used during data analysis of exabytes of particle physics data coming from the Large Hadron Collider (LHC) and other particle physics experiments.


Plans
===

The project foresees three main directions -- move parts of cling upstream along with the clang and llvm features that enable them; extend and generalize the language interoperability layer around cling; and extend and generalize the OpenCL/CUDA support in cling. We are at the early stages of the project and this email intends to be an RFC for the first part -- upstreaming parts of cling. Please do share your thoughts on the rest, too.


Moving Parts of Cling Upstream
---

Over the years we have slowly moved some patches upstream. However we still have around 100 patches in the clang fork. Most of them are in the context of extending the incremental compilation support for clang. The incremental compilation poses some challenges in the clang infrastructure. For example, we need to tune CodeGen to work with multiple llvm::Module instances, and finalize per each end-of-translation unit (we have multiple of them). Other changes include small adjustments in the FileManager's caching mechanism, and bug fixes in the SourceManager (code which can be reached mostly from within our setup). One conclusion we can draw from our research is that the clang infrastructure fits amazingly well to something which was not its main use case. The grand total of our diffs against clang-9 is: `62 files changed, 1294 insertions(+), 231 deletions(-)`. Cling is currently being upgraded from llvm-5 to llvm-9.

A major weakness of cling's infrastructure is that it does not work with the clang Action infrastructure due to the lack of an IncrementalAction.  A possible way forward would be to implement a clang::IncrementalAction as a starting point. This way we should be able to reduce the amount of setup necessary to use the incremental infrastructure in clang. However, this will be a bit of a testing challenge -- cling lives downstream and some of the new code may be impossible to pick straight away and use. Building a mainline example tool such as clang-repl which gives us a way to test that incremental case or repurpose the already existing clang-interpreter may  be able to address the issue. The major risk of the task is avoiding code in the clang mainline which is untested by its HEP production environment.
There are several other types of patches to the ROOT fork of Clang, including ones  in the context of performance,towards  C++ modules support (D41416), and storage (does not have a patch yet but has an open projects entry and somebody working on it). These patches can be considered in parallel independently on the rest.

Extend and Generalize the Language Interoperability Layer Around Cling
---

HEP has extensive experience with on-demand python interoperability using cppyy[6], which is built around the type information provided by cling. Unlike tools with custom parsers such as swig and sip and tools built on top of C-APIs such as boost.python and pybind11, cling can provide information about memory management patterns (eg refcounting) and instantiate templates on the fly.We feel that functionality may not be of general interest to the llvm community but we will prepare another RFC and send it here later on to gather feedback.


Extend and Generalize the OpenCL/CUDA Support in Cling
---

Cling can incrementally compile CUDA code [7-8] allowing easier set up and enabling some interesting use cases. There are a number of planned improvements including talking to HIP [9] and SYCL to support more hardware architectures.



The primary focus of our work is to upstreaming functionality required to build an incremental compiler and rework cling build against vanilla clang and llvm. The last two points are to give the scope of the work which we will be doing the next 2-3 years. We will send here RFCs for both of them to trigger technical discussion if there is interest in pursuing this direction.


Collaboration
===

Open source development nowadays relies on reviewers. LLVM is no different and we will probably disturb a good number of people in the community ;)We would like to invite anybody interested in joining our incremental C++ activities to our open every second week calls. Announcements will be done via google group: compiler-research-announce (https://groups.google.com/g/compiler-research-announce).



Many thanks!


David & Vassil

References
===
[1] ROOT GitHub https://github.com/root-project/root
[2] ROOT https://root.cern
[3] Cling https://github.com/root-project/cling
[4] Xeus-Cling https://blog.jupyter.org/xeus-is-now-a-jupyter-subproject-c4ec5a1bf30b
[5] Cling – The New Interactive Interpreter for ROOT 6, https://iopscience.iop.org/article/10.1088/1742-6596/396/5/052071
[6] High-performance Python-C++ bindings with PyPy and Cling, https://dl.acm.org/doi/10.5555/3019083.3019087
[7] https://indico.cern.ch/event/697389/contributions/3085538/attachments/1712698/2761717/2018_09_10_cling_CUDA.pdf
[8] CUDA C++ in Jupyter: Adding CUDA Runtime Support to Cling', https://zenodo.org/record/3713753#.Xu8jqvJRXxU
[9] HIP Programming Guide https://rocmdocs.amd.com/en/latest/Programming_Guides/HIP-GUIDE.html

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Leadership Computing Facility
Argonne National Laboratory

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Leadership Computing Facility
Argonne National Laboratory

-- 
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Lead, Compiler Technology and Programming Languages
Leadership Computing Facility
Argonne National Laboratory

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Re: [RFC] Moving (parts of) the Cling REPL in Clang

David Chisnall via cfe-dev
In reply to this post by David Chisnall via cfe-dev
On Fri, 10 Jul 2020 at 13:59, Vassil Vassilev via cfe-dev <[hidden email]> wrote:
Hi Richard,

On 7/10/20 11:10 PM, Richard Smith wrote:
Hi Vassil,

This is a very exciting proposal that I can imagine bringing important benefits to the existing cling users and also to the clang user and developer community. Thank you for all the work you and your team have done on cling so far and for offering to bring that work under the LLVM umbrella!

Are you imagining cling being part of the clang repository, or a separate LLVM subproject (with only the changes necessary to support cling-style uses of the clang libraries added to the clang tree)?


  Good question. In principle cling was developed with the idea to become a separate LLVM subproject. Although I'd easily see it fit in clang/tools/.


  Nominally, cling has "high-energy physics"-specific features such as the so called 'meta commands'. For example, `[cling] .L some_file` would try to load a library called some_file.so and if it does not exist, try #include-ing a header with that name; `[cling] .x script.C` includes script.C and calls a function named `script`. I can imagine that broader community may not like/use that. If we start trimming down features like that then it won't really be cling anymore. Here is what I would imagine as a way forward:

  1. Land as many cling/"incremental compilation"-related patches as we can in clang.
  2. Build a simple tool, let's use a strawman name -- clang-repl, which only does the basics. For example, one can feed it incremental C++ and execute it.
  3. Rework cling to use that infrastructure -- ideally, implementing it's specific meta commands and other domain-specific features such as dynamic scopes.

  We could move any of the cling features which the broader community finds useful closer to clang. For the moment I am being conservative as this will also give us the opportunity to rethink some of the features.

  The hard part is what lives where. First bullet point is clear. The second -- not so much. Clang has a clang-interpreter in its examples folder and it looks a little unmaintained. Maybe we can start repurposing that to match 2.

  As for cling itself there are some challenges we should try to solve. Our community lives downstream (currently llvm-5) and a straight-forward llvm upgrade + bugfixing takes around 3 months due to the nature of our software stacks. It would be a non-trivial task to move the cling-based development in llvm upstream. My worry is that HEP-cling will soon depart from LLVM-cling if we don't get both communities on the same codebase (we have experienced such a problem with the getFullyQualified* interfaces). I am hoping that a middleman, such as clang-repl, can help. When we move parts of cling in clang we will develop and test the required functionality using clang-repl. This way users will enjoy cling-like experience and when cling upgrades its llvm its codebase will become smaller in size.

  Am I making sense?

Yes, the above all makes sense to me. I agree that there should be only one thing named 'cling', and that it should broadly have the feature set that current 'cling' has. I think there are a couple of ways we can get there while still providing the a minimalist interpreter to a broader audience: either we can build a simpler clang-interpreter and a more advanced cling binary from a common set of libraries, or we could produce a configurable binary that's able to serve both rules depending on configuration or a plugin or scripting system.

One other thing I think we should consider: there will be substantial overlap between the incremental compilation, code generation, REPL, etc. of cling and that of lldb. For the initial integration of cling into LLVM, there's probably not much we can do about that, but it would seem beneficial for both cling and lldb if common parts could be shared where possible. As an extreme example, if we could fully unify the projects to the point where a user could switch into an 'lldb mode' in the middle of a cling session to do step-by-step debugging of code entered into the REPL, that would seem like an incredibly useful feature. Perhaps there's some common set of base functionality that can be factored out of lldb and cling and unified. It would likely be a good idea to start talking to the lldb folks about that early, in case it guides your work porting cling to trunk.
On Thu, 9 Jul 2020 at 13:46, Vassil Vassilev via cfe-dev <[hidden email]> wrote:
Motivation
===

Over the last decade we have developed an interactive, interpretative
C++ (aka REPL) as part of the high-energy physics (HEP) data analysis
project -- ROOT [1-2]. We invested a significant  effort to replace the
CINT C++ interpreter with a newly implemented REPL based on llvm --
cling [3]. The cling infrastructure is a core component of the data
analysis framework of ROOT and runs in production for approximately 5
years.

Cling is also  a standalone tool, which has a growing community outside
of our field. Cling’s user community includes users in finance, biology
and in a few companies with proprietary software. For example, there is
a xeus-cling jupyter kernel [4]. One of the major challenges we face to
foster that community is  our cling-related patches in llvm and clang
forks. The benefits of using the LLVM community standards for code
reviews, release cycles and integration has been mentioned a number of
times by our "external" users.

Last year we were awarded an NSF grant to improve cling's sustainability
and make it a standalone tool. We thank the LLVM Foundation Board for
supporting us with a non-binding letter of collaboration which was
essential for getting this grant.


Background
===

Cling is a C++ interpreter built on top of clang and llvm. In a
nutshell, it uses clang's incremental compilation facilities to process
code chunk-by-chunk by assuming an ever-growing translation unit [5].
Then code is lowered into llvm IR and run by the llvm jit. Cling has
implemented some language "extensions" such as execution statements on
the global scope and error recovery. Cling is in the core of HEP -- it
is heavily used during data analysis of exabytes of particle physics
data coming from the Large Hadron Collider (LHC) and other particle
physics experiments.


Plans
===

The project foresees three main directions -- move parts of cling
upstream along with the clang and llvm features that enable them; extend
and generalize the language interoperability layer around cling; and
extend and generalize the OpenCL/CUDA support in cling. We are at the
early stages of the project and this email intends to be an RFC for the
first part -- upstreaming parts of cling. Please do share your thoughts
on the rest, too.


Moving Parts of Cling Upstream
---

Over the years we have slowly moved some patches upstream. However we
still have around 100 patches in the clang fork. Most of them are in the
context of extending the incremental compilation support for clang. The
incremental compilation poses some challenges in the clang
infrastructure. For example, we need to tune CodeGen to work with
multiple llvm::Module instances, and finalize per each
end-of-translation unit (we have multiple of them). Other changes
include small adjustments in the FileManager's caching mechanism, and
bug fixes in the SourceManager (code which can be reached mostly from
within our setup). One conclusion we can draw from our research is that
the clang infrastructure fits amazingly well to something which was not
its main use case. The grand total of our diffs against clang-9 is: `62
files changed, 1294 insertions(+), 231 deletions(-)`. Cling is currently
being upgraded from llvm-5 to llvm-9.

A major weakness of cling's infrastructure is that it does not work with
the clang Action infrastructure due to the lack of an
IncrementalAction.  A possible way forward would be to implement a
clang::IncrementalAction as a starting point. This way we should be able
to reduce the amount of setup necessary to use the incremental
infrastructure in clang. However, this will be a bit of a testing
challenge -- cling lives downstream and some of the new code may be
impossible to pick straight away and use. Building a mainline example
tool such as clang-repl which gives us a way to test that incremental
case or repurpose the already existing clang-interpreter may  be able to
address the issue. The major risk of the task is avoiding code in the
clang mainline which is untested by its HEP production environment.
There are several other types of patches to the ROOT fork of Clang,
including ones  in the context of performance,towards  C++ modules
support (D41416), and storage (does not have a patch yet but has an open
projects entry and somebody working on it). These patches can be
considered in parallel independently on the rest.

Extend and Generalize the Language Interoperability Layer Around Cling
---

HEP has extensive experience with on-demand python interoperability
using cppyy[6], which is built around the type information provided by
cling. Unlike tools with custom parsers such as swig and sip and tools
built on top of C-APIs such as boost.python and pybind11, cling can
provide information about memory management patterns (eg refcounting)
and instantiate templates on the fly.We feel that functionality may not
be of general interest to the llvm community but we will prepare another
RFC and send it here later on to gather feedback.


Extend and Generalize the OpenCL/CUDA Support in Cling
---

Cling can incrementally compile CUDA code [7-8] allowing easier set up
and enabling some interesting use cases. There are a number of planned
improvements including talking to HIP [9] and SYCL to support more
hardware architectures.



The primary focus of our work is to upstreaming functionality required
to build an incremental compiler and rework cling build against vanilla
clang and llvm. The last two points are to give the scope of the work
which we will be doing the next 2-3 years. We will send here RFCs for
both of them to trigger technical discussion if there is interest in
pursuing this direction.


Collaboration
===

Open source development nowadays relies on reviewers. LLVM is no
different and we will probably disturb a good number of people in the
community ;)We would like to invite anybody interested in joining our
incremental C++ activities to our open every second week calls.
Announcements will be done via google group: compiler-research-announce
(https://groups.google.com/g/compiler-research-announce).



Many thanks!


David & Vassil

References
===
[1] ROOT GitHub https://github.com/root-project/root
[2] ROOT https://root.cern
[3] Cling https://github.com/root-project/cling
[4] Xeus-Cling
https://blog.jupyter.org/xeus-is-now-a-jupyter-subproject-c4ec5a1bf30b
[5] Cling – The New Interactive Interpreter for ROOT 6,
https://iopscience.iop.org/article/10.1088/1742-6596/396/5/052071
[6] High-performance Python-C++ bindings with PyPy and Cling,
https://dl.acm.org/doi/10.5555/3019083.3019087
[7]
https://indico.cern.ch/event/697389/contributions/3085538/attachments/1712698/2761717/2018_09_10_cling_CUDA.pdf
[8] CUDA C++ in Jupyter: Adding CUDA Runtime Support to Cling',
https://zenodo.org/record/3713753#.Xu8jqvJRXxU
[9] HIP Programming Guide
https://rocmdocs.amd.com/en/latest/Programming_Guides/HIP-GUIDE.html

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Re: [RFC] Moving (parts of) the Cling REPL in Clang

David Chisnall via cfe-dev
On Jul 10, 2020, at 2:58 PM, Richard Smith via cfe-dev <[hidden email]> wrote:

One other thing I think we should consider: there will be substantial overlap between the incremental compilation, code generation, REPL, etc. of cling and that of lldb. For the initial integration of cling into LLVM, there's probably not much we can do about that, but it would seem beneficial for both cling and lldb if common parts could be shared where possible. As an extreme example, if we could fully unify the projects to the point where a user could switch into an 'lldb mode' in the middle of a cling session to do step-by-step debugging of code entered into the REPL, that would seem like an incredibly useful feature. Perhaps there's some common set of base functionality that can be factored out of lldb and cling and unified. It would likely be a good idea to start talking to the lldb folks about that early, in case it guides your work porting cling to trunk.

This is a really good point.  I’m not sure how much awareness there is on this list, but the Swift REPL is worth looking at if you haven’t seen it.  It is built on/in LLDB, and provides some really nice user experience features.

For example, if you evaluate an expression that crashes, you get a full backtrace and integrated debugger experience.  There are a couple of examples on this page, and more detailed info online:

-Chris


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Re: [RFC] Moving (parts of) the Cling REPL in Clang

David Chisnall via cfe-dev
In reply to this post by David Chisnall via cfe-dev
On 7/11/20 12:58 AM, Richard Smith wrote:
On Fri, 10 Jul 2020 at 13:59, Vassil Vassilev via cfe-dev <[hidden email]> wrote:
Hi Richard,

On 7/10/20 11:10 PM, Richard Smith wrote:
Hi Vassil,

This is a very exciting proposal that I can imagine bringing important benefits to the existing cling users and also to the clang user and developer community. Thank you for all the work you and your team have done on cling so far and for offering to bring that work under the LLVM umbrella!

Are you imagining cling being part of the clang repository, or a separate LLVM subproject (with only the changes necessary to support cling-style uses of the clang libraries added to the clang tree)?


  Good question. In principle cling was developed with the idea to become a separate LLVM subproject. Although I'd easily see it fit in clang/tools/.


  Nominally, cling has "high-energy physics"-specific features such as the so called 'meta commands'. For example, `[cling] .L some_file` would try to load a library called some_file.so and if it does not exist, try #include-ing a header with that name; `[cling] .x script.C` includes script.C and calls a function named `script`. I can imagine that broader community may not like/use that. If we start trimming down features like that then it won't really be cling anymore. Here is what I would imagine as a way forward:

  1. Land as many cling/"incremental compilation"-related patches as we can in clang.
  2. Build a simple tool, let's use a strawman name -- clang-repl, which only does the basics. For example, one can feed it incremental C++ and execute it.
  3. Rework cling to use that infrastructure -- ideally, implementing it's specific meta commands and other domain-specific features such as dynamic scopes.

  We could move any of the cling features which the broader community finds useful closer to clang. For the moment I am being conservative as this will also give us the opportunity to rethink some of the features.

  The hard part is what lives where. First bullet point is clear. The second -- not so much. Clang has a clang-interpreter in its examples folder and it looks a little unmaintained. Maybe we can start repurposing that to match 2.

  As for cling itself there are some challenges we should try to solve. Our community lives downstream (currently llvm-5) and a straight-forward llvm upgrade + bugfixing takes around 3 months due to the nature of our software stacks. It would be a non-trivial task to move the cling-based development in llvm upstream. My worry is that HEP-cling will soon depart from LLVM-cling if we don't get both communities on the same codebase (we have experienced such a problem with the getFullyQualified* interfaces). I am hoping that a middleman, such as clang-repl, can help. When we move parts of cling in clang we will develop and test the required functionality using clang-repl. This way users will enjoy cling-like experience and when cling upgrades its llvm its codebase will become smaller in size.

  Am I making sense?

Yes, the above all makes sense to me. I agree that there should be only one thing named 'cling', and that it should broadly have the feature set that current 'cling' has. I think there are a couple of ways we can get there while still providing the a minimalist interpreter to a broader audience: either we can build a simpler clang-interpreter and a more advanced cling binary from a common set of libraries, or we could produce a configurable binary that's able to serve both rules depending on configuration or a plugin or scripting system.


  Good point. We could make it extendable, and actually that should be a design goal. The question how exactly is not very clear to me. Can you elaborate on what you had in mind as configuration or scripting system (plugin system I think I know what you meant). I will give an example with 3 distinct features in cling which we have implemented over the years and had different requirements:

  * AST-based automatic differentiation with the clad library -- here we essentially extend cling's runtime by providing a `clad::differentiate`, `clad::gradient`, `clad::hessian` and `clad::jacobian` primitives. Each primitive is a specially annotated wrapper over a function, say `double pow2(double x) { return x*x; }; auto pow2dx = clad::differentiate(pow2,/*wrt*/0);`. Here we let clang build a valid AST and the plugin creates the first order derivative and swaps the DeclRefExpr just before codegen so that we call the derivative instead. This is achievable by the current clang plugin system ( a bit problematic on windows as clang plugins do not work there ).

  * Language extensions which require Sema support -- we have a legacy feature which should define a variable on the prompt if not defined (something like implicit auto) `cling[] i = 13;` should be translated into `cling[] auto i = 13;` if I is undefined. We solve that by adding some last resort lookup callback which marks `i` of dependent type so that we can produce an AST which we can later 'fix'.

  * Language extensions which require delayed lookup rules (aka dynamic scope) -- ROOT has an I/O system bound to cling people can write:`if (TFile::Open("file_that_has_hist_cpp_obj.root")) hist->Draw();`. Here we use the approach from the previous bullet and synthesize `if (TFile::Open("file_that_has_hist_cpp_obj.root")) eval<void>("hist->Draw()", /*escape some context*/...);`.


  The implementation of these three features can be considered as possible with current clang. The issue is that it seems more like hacking clang rather than extending it. If we can come up with a sound way of implementing these features that would be awesome.



One other thing I think we should consider: there will be substantial overlap between the incremental compilation, code generation, REPL, etc. of cling and that of lldb.


  I would love to hear opinions from the lldb folks. We have chatted number of times and I have looked at how they do it. I think lldb spawns (used to spawn last time I looked) a compiler instance per input line. That is not acceptable for cling due to its high-performance requirements. Most of the issues that need solving for lldb comes from materializing debug information to AST. LLDB folks, correct me if I am wrong.

  That being said doesn't mean that we should not aim for centralizing the incremental compilation for both projects. We should but may be challenging because of the different focus which defines project priorities.


For the initial integration of cling into LLVM, there's probably not much we can do about that, but it would seem beneficial for both cling and lldb if common parts could be shared where possible. As an extreme example, if we could fully unify the projects to the point where a user could switch into an 'lldb mode' in the middle of a cling session to do step-by-step debugging of code entered into the REPL, that would seem like an incredibly useful feature. Perhaps there's some common set of base functionality that can be factored out of lldb and cling and unified. It would likely be a good idea to start talking to the lldb folks about that early, in case it guides your work porting cling to trunk.


  Indeed. There have been user requests to be able to run step-by-step in cling. That would be the ultimate long term goal!


On Thu, 9 Jul 2020 at 13:46, Vassil Vassilev via cfe-dev <[hidden email]> wrote:
Motivation
===

Over the last decade we have developed an interactive, interpretative
C++ (aka REPL) as part of the high-energy physics (HEP) data analysis
project -- ROOT [1-2]. We invested a significant  effort to replace the
CINT C++ interpreter with a newly implemented REPL based on llvm --
cling [3]. The cling infrastructure is a core component of the data
analysis framework of ROOT and runs in production for approximately 5
years.

Cling is also  a standalone tool, which has a growing community outside
of our field. Cling’s user community includes users in finance, biology
and in a few companies with proprietary software. For example, there is
a xeus-cling jupyter kernel [4]. One of the major challenges we face to
foster that community is  our cling-related patches in llvm and clang
forks. The benefits of using the LLVM community standards for code
reviews, release cycles and integration has been mentioned a number of
times by our "external" users.

Last year we were awarded an NSF grant to improve cling's sustainability
and make it a standalone tool. We thank the LLVM Foundation Board for
supporting us with a non-binding letter of collaboration which was
essential for getting this grant.


Background
===

Cling is a C++ interpreter built on top of clang and llvm. In a
nutshell, it uses clang's incremental compilation facilities to process
code chunk-by-chunk by assuming an ever-growing translation unit [5].
Then code is lowered into llvm IR and run by the llvm jit. Cling has
implemented some language "extensions" such as execution statements on
the global scope and error recovery. Cling is in the core of HEP -- it
is heavily used during data analysis of exabytes of particle physics
data coming from the Large Hadron Collider (LHC) and other particle
physics experiments.


Plans
===

The project foresees three main directions -- move parts of cling
upstream along with the clang and llvm features that enable them; extend
and generalize the language interoperability layer around cling; and
extend and generalize the OpenCL/CUDA support in cling. We are at the
early stages of the project and this email intends to be an RFC for the
first part -- upstreaming parts of cling. Please do share your thoughts
on the rest, too.


Moving Parts of Cling Upstream
---

Over the years we have slowly moved some patches upstream. However we
still have around 100 patches in the clang fork. Most of them are in the
context of extending the incremental compilation support for clang. The
incremental compilation poses some challenges in the clang
infrastructure. For example, we need to tune CodeGen to work with
multiple llvm::Module instances, and finalize per each
end-of-translation unit (we have multiple of them). Other changes
include small adjustments in the FileManager's caching mechanism, and
bug fixes in the SourceManager (code which can be reached mostly from
within our setup). One conclusion we can draw from our research is that
the clang infrastructure fits amazingly well to something which was not
its main use case. The grand total of our diffs against clang-9 is: `62
files changed, 1294 insertions(+), 231 deletions(-)`. Cling is currently
being upgraded from llvm-5 to llvm-9.

A major weakness of cling's infrastructure is that it does not work with
the clang Action infrastructure due to the lack of an
IncrementalAction.  A possible way forward would be to implement a
clang::IncrementalAction as a starting point. This way we should be able
to reduce the amount of setup necessary to use the incremental
infrastructure in clang. However, this will be a bit of a testing
challenge -- cling lives downstream and some of the new code may be
impossible to pick straight away and use. Building a mainline example
tool such as clang-repl which gives us a way to test that incremental
case or repurpose the already existing clang-interpreter may  be able to
address the issue. The major risk of the task is avoiding code in the
clang mainline which is untested by its HEP production environment.
There are several other types of patches to the ROOT fork of Clang,
including ones  in the context of performance,towards  C++ modules
support (D41416), and storage (does not have a patch yet but has an open
projects entry and somebody working on it). These patches can be
considered in parallel independently on the rest.

Extend and Generalize the Language Interoperability Layer Around Cling
---

HEP has extensive experience with on-demand python interoperability
using cppyy[6], which is built around the type information provided by
cling. Unlike tools with custom parsers such as swig and sip and tools
built on top of C-APIs such as boost.python and pybind11, cling can
provide information about memory management patterns (eg refcounting)
and instantiate templates on the fly.We feel that functionality may not
be of general interest to the llvm community but we will prepare another
RFC and send it here later on to gather feedback.


Extend and Generalize the OpenCL/CUDA Support in Cling
---

Cling can incrementally compile CUDA code [7-8] allowing easier set up
and enabling some interesting use cases. There are a number of planned
improvements including talking to HIP [9] and SYCL to support more
hardware architectures.



The primary focus of our work is to upstreaming functionality required
to build an incremental compiler and rework cling build against vanilla
clang and llvm. The last two points are to give the scope of the work
which we will be doing the next 2-3 years. We will send here RFCs for
both of them to trigger technical discussion if there is interest in
pursuing this direction.


Collaboration
===

Open source development nowadays relies on reviewers. LLVM is no
different and we will probably disturb a good number of people in the
community ;)We would like to invite anybody interested in joining our
incremental C++ activities to our open every second week calls.
Announcements will be done via google group: compiler-research-announce
(https://groups.google.com/g/compiler-research-announce).



Many thanks!


David & Vassil

References
===
[1] ROOT GitHub https://github.com/root-project/root
[2] ROOT https://root.cern
[3] Cling https://github.com/root-project/cling
[4] Xeus-Cling
https://blog.jupyter.org/xeus-is-now-a-jupyter-subproject-c4ec5a1bf30b
[5] Cling – The New Interactive Interpreter for ROOT 6,
https://iopscience.iop.org/article/10.1088/1742-6596/396/5/052071
[6] High-performance Python-C++ bindings with PyPy and Cling,
https://dl.acm.org/doi/10.5555/3019083.3019087
[7]
https://indico.cern.ch/event/697389/contributions/3085538/attachments/1712698/2761717/2018_09_10_cling_CUDA.pdf
[8] CUDA C++ in Jupyter: Adding CUDA Runtime Support to Cling',
https://zenodo.org/record/3713753#.Xu8jqvJRXxU
[9] HIP Programming Guide
https://rocmdocs.amd.com/en/latest/Programming_Guides/HIP-GUIDE.html

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Re: [RFC] Moving (parts of) the Cling REPL in Clang

David Chisnall via cfe-dev
In reply to this post by David Chisnall via cfe-dev
On 7/11/20 1:50 AM, Chris Lattner wrote:
On Jul 10, 2020, at 2:58 PM, Richard Smith via cfe-dev <[hidden email]> wrote:

One other thing I think we should consider: there will be substantial overlap between the incremental compilation, code generation, REPL, etc. of cling and that of lldb. For the initial integration of cling into LLVM, there's probably not much we can do about that, but it would seem beneficial for both cling and lldb if common parts could be shared where possible. As an extreme example, if we could fully unify the projects to the point where a user could switch into an 'lldb mode' in the middle of a cling session to do step-by-step debugging of code entered into the REPL, that would seem like an incredibly useful feature. Perhaps there's some common set of base functionality that can be factored out of lldb and cling and unified. It would likely be a good idea to start talking to the lldb folks about that early, in case it guides your work porting cling to trunk.

This is a really good point.  I’m not sure how much awareness there is on this list, but the Swift REPL is worth looking at if you haven’t seen it.  It is built on/in LLDB, and provides some really nice user experience features.

For example, if you evaluate an expression that crashes, you get a full backtrace and integrated debugger experience.  There are a couple of examples on this page, and more detailed info online:


  Thanks Chris! The comments coming from John McCall allude to this that we need some broader discussion on how we do things for incremental compilation. I still have not forgotten that I need to get back to him with that ;)

  That's one of the challenges I see. Currently upstreaming our patches did not have enough context. Now I hope that we can start with something minimal (and likely wrong) but we will have a common tool in the context of which we can discuss how to make things better. Putting swift-repl folks, lldb folks and cling folks in one virtual room may be helpful.



-Chris



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Re: [RFC] Moving (parts of) the Cling REPL in Clang

David Chisnall via cfe-dev
In reply to this post by David Chisnall via cfe-dev
Vassil is right, it's just one Clang instance per expression. This is by design as it allows LLDB's expression evaluator to be flexible and it also makes the code simpler and clearer.

Regarding the REPL part: LLDB's expression parser for C++ isn't meant to be a full REPL. There is only some limited data sharing between each expression (e.g., result types and a few specifically marked declarations from the user) as the goal is to make an AST that fits the context where the expression is evaluated. That means that we need to support that a user can type "MyStruct" and in one expression it might refer to a struct type, but in the next expression (which could by at some other point in the program) it might be a typedef, or a macro, or a Objective-C class, or a global/local variable name, or a member variable as the evaluation context changed into a class, or a keyword in the current C-language, or also a struct but with a different definition or not even anything at all.

I really don't see a sane way to support just this one simple feature with with Cling's single shared AST + incremental CodeGen approach.

Also LLDB's expression parser doesn't really have a lot of non-LLDB specific code left that could be shared with other projects. We used to have a bunch of Clang in the expression parser but most of it is be gone by now. The rest is really LLDB-specific (e.g., configuring Clang to match the target we are trying to debug, a lot of code for setting up the right evaluation context of the location where the program is stopped).

Having said that, I think Cling should be upstream any shared code we can find between Cling and LLDB should be shared. Feel free to add me to patches and I'll see what I can find.

- Raphael

On 11 Jul 2020, at 09:02, Vassil Vassilev <[hidden email]> wrote:

On 7/11/20 12:58 AM, Richard Smith wrote:
On Fri, 10 Jul 2020 at 13:59, Vassil Vassilev via cfe-dev <[hidden email]> wrote:
Hi Richard,

On 7/10/20 11:10 PM, Richard Smith wrote:
Hi Vassil,

This is a very exciting proposal that I can imagine bringing important benefits to the existing cling users and also to the clang user and developer community. Thank you for all the work you and your team have done on cling so far and for offering to bring that work under the LLVM umbrella!

Are you imagining cling being part of the clang repository, or a separate LLVM subproject (with only the changes necessary to support cling-style uses of the clang libraries added to the clang tree)?


  Good question. In principle cling was developed with the idea to become a separate LLVM subproject. Although I'd easily see it fit in clang/tools/.


  Nominally, cling has "high-energy physics"-specific features such as the so called 'meta commands'. For example, `[cling] .L some_file` would try to load a library called some_file.so and if it does not exist, try #include-ing a header with that name; `[cling] .x script.C` includes script.C and calls a function named `script`. I can imagine that broader community may not like/use that. If we start trimming down features like that then it won't really be cling anymore. Here is what I would imagine as a way forward:

  1. Land as many cling/"incremental compilation"-related patches as we can in clang.
  2. Build a simple tool, let's use a strawman name -- clang-repl, which only does the basics. For example, one can feed it incremental C++ and execute it.
  3. Rework cling to use that infrastructure -- ideally, implementing it's specific meta commands and other domain-specific features such as dynamic scopes.

  We could move any of the cling features which the broader community finds useful closer to clang. For the moment I am being conservative as this will also give us the opportunity to rethink some of the features.

  The hard part is what lives where. First bullet point is clear. The second -- not so much. Clang has a clang-interpreter in its examples folder and it looks a little unmaintained. Maybe we can start repurposing that to match 2.

  As for cling itself there are some challenges we should try to solve. Our community lives downstream (currently llvm-5) and a straight-forward llvm upgrade + bugfixing takes around 3 months due to the nature of our software stacks. It would be a non-trivial task to move the cling-based development in llvm upstream. My worry is that HEP-cling will soon depart from LLVM-cling if we don't get both communities on the same codebase (we have experienced such a problem with the getFullyQualified* interfaces). I am hoping that a middleman, such as clang-repl, can help. When we move parts of cling in clang we will develop and test the required functionality using clang-repl. This way users will enjoy cling-like experience and when cling upgrades its llvm its codebase will become smaller in size.

  Am I making sense?

Yes, the above all makes sense to me. I agree that there should be only one thing named 'cling', and that it should broadly have the feature set that current 'cling' has. I think there are a couple of ways we can get there while still providing the a minimalist interpreter to a broader audience: either we can build a simpler clang-interpreter and a more advanced cling binary from a common set of libraries, or we could produce a configurable binary that's able to serve both rules depending on configuration or a plugin or scripting system.


  Good point. We could make it extendable, and actually that should be a design goal. The question how exactly is not very clear to me. Can you elaborate on what you had in mind as configuration or scripting system (plugin system I think I know what you meant). I will give an example with 3 distinct features in cling which we have implemented over the years and had different requirements:

  * AST-based automatic differentiation with the clad library -- here we essentially extend cling's runtime by providing a `clad::differentiate`, `clad::gradient`, `clad::hessian` and `clad::jacobian` primitives. Each primitive is a specially annotated wrapper over a function, say `double pow2(double x) { return x*x; }; auto pow2dx = clad::differentiate(pow2,/*wrt*/0);`. Here we let clang build a valid AST and the plugin creates the first order derivative and swaps the DeclRefExpr just before codegen so that we call the derivative instead. This is achievable by the current clang plugin system ( a bit problematic on windows as clang plugins do not work there ).

  * Language extensions which require Sema support -- we have a legacy feature which should define a variable on the prompt if not defined (something like implicit auto) `cling[] i = 13;` should be translated into `cling[] auto i = 13;` if I is undefined. We solve that by adding some last resort lookup callback which marks `i` of dependent type so that we can produce an AST which we can later 'fix'.

  * Language extensions which require delayed lookup rules (aka dynamic scope) -- ROOT has an I/O system bound to cling people can write:`if (TFile::Open("file_that_has_hist_cpp_obj.root")) hist->Draw();`. Here we use the approach from the previous bullet and synthesize `if (TFile::Open("file_that_has_hist_cpp_obj.root")) eval<void>("hist->Draw()", /*escape some context*/...);`.


  The implementation of these three features can be considered as possible with current clang. The issue is that it seems more like hacking clang rather than extending it. If we can come up with a sound way of implementing these features that would be awesome.



One other thing I think we should consider: there will be substantial overlap between the incremental compilation, code generation, REPL, etc. of cling and that of lldb.


  I would love to hear opinions from the lldb folks. We have chatted number of times and I have looked at how they do it. I think lldb spawns (used to spawn last time I looked) a compiler instance per input line. That is not acceptable for cling due to its high-performance requirements. Most of the issues that need solving for lldb comes from materializing debug information to AST. LLDB folks, correct me if I am wrong.

  That being said doesn't mean that we should not aim for centralizing the incremental compilation for both projects. We should but may be challenging because of the different focus which defines project priorities.


For the initial integration of cling into LLVM, there's probably not much we can do about that, but it would seem beneficial for both cling and lldb if common parts could be shared where possible. As an extreme example, if we could fully unify the projects to the point where a user could switch into an 'lldb mode' in the middle of a cling session to do step-by-step debugging of code entered into the REPL, that would seem like an incredibly useful feature. Perhaps there's some common set of base functionality that can be factored out of lldb and cling and unified. It would likely be a good idea to start talking to the lldb folks about that early, in case it guides your work porting cling to trunk.


  Indeed. There have been user requests to be able to run step-by-step in cling. That would be the ultimate long term goal!


On Thu, 9 Jul 2020 at 13:46, Vassil Vassilev via cfe-dev <[hidden email]> wrote:
Motivation
===

Over the last decade we have developed an interactive, interpretative
C++ (aka REPL) as part of the high-energy physics (HEP) data analysis
project -- ROOT [1-2]. We invested a significant  effort to replace the
CINT C++ interpreter with a newly implemented REPL based on llvm --
cling [3]. The cling infrastructure is a core component of the data
analysis framework of ROOT and runs in production for approximately 5
years.

Cling is also  a standalone tool, which has a growing community outside
of our field. Cling’s user community includes users in finance, biology
and in a few companies with proprietary software. For example, there is
a xeus-cling jupyter kernel [4]. One of the major challenges we face to
foster that community is  our cling-related patches in llvm and clang
forks. The benefits of using the LLVM community standards for code
reviews, release cycles and integration has been mentioned a number of
times by our "external" users.

Last year we were awarded an NSF grant to improve cling's sustainability
and make it a standalone tool. We thank the LLVM Foundation Board for
supporting us with a non-binding letter of collaboration which was
essential for getting this grant.


Background
===

Cling is a C++ interpreter built on top of clang and llvm. In a
nutshell, it uses clang's incremental compilation facilities to process
code chunk-by-chunk by assuming an ever-growing translation unit [5].
Then code is lowered into llvm IR and run by the llvm jit. Cling has
implemented some language "extensions" such as execution statements on
the global scope and error recovery. Cling is in the core of HEP -- it
is heavily used during data analysis of exabytes of particle physics
data coming from the Large Hadron Collider (LHC) and other particle
physics experiments.


Plans
===

The project foresees three main directions -- move parts of cling
upstream along with the clang and llvm features that enable them; extend
and generalize the language interoperability layer around cling; and
extend and generalize the OpenCL/CUDA support in cling. We are at the
early stages of the project and this email intends to be an RFC for the
first part -- upstreaming parts of cling. Please do share your thoughts
on the rest, too.


Moving Parts of Cling Upstream
---

Over the years we have slowly moved some patches upstream. However we
still have around 100 patches in the clang fork. Most of them are in the
context of extending the incremental compilation support for clang. The
incremental compilation poses some challenges in the clang
infrastructure. For example, we need to tune CodeGen to work with
multiple llvm::Module instances, and finalize per each
end-of-translation unit (we have multiple of them). Other changes
include small adjustments in the FileManager's caching mechanism, and
bug fixes in the SourceManager (code which can be reached mostly from
within our setup). One conclusion we can draw from our research is that
the clang infrastructure fits amazingly well to something which was not
its main use case. The grand total of our diffs against clang-9 is: `62
files changed, 1294 insertions(+), 231 deletions(-)`. Cling is currently
being upgraded from llvm-5 to llvm-9.

A major weakness of cling's infrastructure is that it does not work with
the clang Action infrastructure due to the lack of an
IncrementalAction.  A possible way forward would be to implement a
clang::IncrementalAction as a starting point. This way we should be able
to reduce the amount of setup necessary to use the incremental
infrastructure in clang. However, this will be a bit of a testing
challenge -- cling lives downstream and some of the new code may be
impossible to pick straight away and use. Building a mainline example
tool such as clang-repl which gives us a way to test that incremental
case or repurpose the already existing clang-interpreter may  be able to
address the issue. The major risk of the task is avoiding code in the
clang mainline which is untested by its HEP production environment.
There are several other types of patches to the ROOT fork of Clang,
including ones  in the context of performance,towards  C++ modules
support (D41416), and storage (does not have a patch yet but has an open
projects entry and somebody working on it). These patches can be
considered in parallel independently on the rest.

Extend and Generalize the Language Interoperability Layer Around Cling
---

HEP has extensive experience with on-demand python interoperability
using cppyy[6], which is built around the type information provided by
cling. Unlike tools with custom parsers such as swig and sip and tools
built on top of C-APIs such as boost.python and pybind11, cling can
provide information about memory management patterns (eg refcounting)
and instantiate templates on the fly.We feel that functionality may not
be of general interest to the llvm community but we will prepare another
RFC and send it here later on to gather feedback.


Extend and Generalize the OpenCL/CUDA Support in Cling
---

Cling can incrementally compile CUDA code [7-8] allowing easier set up
and enabling some interesting use cases. There are a number of planned
improvements including talking to HIP [9] and SYCL to support more
hardware architectures.



The primary focus of our work is to upstreaming functionality required
to build an incremental compiler and rework cling build against vanilla
clang and llvm. The last two points are to give the scope of the work
which we will be doing the next 2-3 years. We will send here RFCs for
both of them to trigger technical discussion if there is interest in
pursuing this direction.


Collaboration
===

Open source development nowadays relies on reviewers. LLVM is no
different and we will probably disturb a good number of people in the
community ;)We would like to invite anybody interested in joining our
incremental C++ activities to our open every second week calls.
Announcements will be done via google group: compiler-research-announce
(https://groups.google.com/g/compiler-research-announce).



Many thanks!


David & Vassil

References
===
[1] ROOT GitHub https://github.com/root-project/root
[2] ROOT https://root.cern
[3] Cling https://github.com/root-project/cling
[4] Xeus-Cling
https://blog.jupyter.org/xeus-is-now-a-jupyter-subproject-c4ec5a1bf30b
[5] Cling – The New Interactive Interpreter for ROOT 6,
https://iopscience.iop.org/article/10.1088/1742-6596/396/5/052071
[6] High-performance Python-C++ bindings with PyPy and Cling,
https://dl.acm.org/doi/10.5555/3019083.3019087
[7]
https://indico.cern.ch/event/697389/contributions/3085538/attachments/1712698/2761717/2018_09_10_cling_CUDA.pdf
[8] CUDA C++ in Jupyter: Adding CUDA Runtime Support to Cling',
https://zenodo.org/record/3713753#.Xu8jqvJRXxU
[9] HIP Programming Guide
https://rocmdocs.amd.com/en/latest/Programming_Guides/HIP-GUIDE.html

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