llvm dev - Jul 2020 - [cfe-dev] [RFC] Moving (parts of) the Cling REPL in Clang

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

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 <cfe-dev at
lists.llvm.org> 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
> cfe-dev at lists.llvm.org
> https://lists.llvm.org/cgi-bin/mailman/listinfo/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
> cfe-dev at lists.llvm.org
> https://lists.llvm.org/cgi-bin/mailman/listinfo/cfe-dev
-- 
Hal Finkel
Lead, Compiler Technology and Programming Languages
Leadership Computing Facility
Argonne National Laboratory

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 <cfe-dev at
lists.llvm.org> 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
>> cfe-dev at lists.llvm.org
>> 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
> cfe-dev at lists.llvm.org
> https://lists.llvm.org/cgi-bin/mailman/listinfo/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 <
cfe-dev at lists.llvm.org> 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
> cfe-dev at lists.llvm.org
> https://lists.llvm.org/cgi-bin/mailman/listinfo/cfe-dev
>
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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 
> <cfe-dev at lists.llvm.org <mailto:cfe-dev at lists.llvm.org>>
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
>     cfe-dev at lists.llvm.org <mailto:cfe-dev at lists.llvm.org>
>     https://lists.llvm.org/cgi-bin/mailman/listinfo/cfe-dev
>
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