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Add triangular solvers to benchmark #664

Merged
merged 2 commits into from
Nov 20, 2020
Merged

Add triangular solvers to benchmark #664

merged 2 commits into from
Nov 20, 2020

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upsj
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@upsj upsj commented Nov 19, 2020

For @lksriemer's experiments, this PR adds the triangular solvers to our benchmarks.

Usage:

echo '[{"filename":"upper-triangular.mtx", "optimal": {"spmv":"csr"}}]' > input.json
echo '[{"filename":"upper-triangular.mtx", "rhs": "rhs.mtx", "optimal": {"spmv":"csr"}}]' > input2.json
benchmark/solver/solver -solvers upper_trs -executor cuda < input.json
benchmark/solver/solver -solvers upper_trs -executor cuda < input2.json

Output:

[
    {
        "filename": "upper-triangular.mtx",
        "optimal": {
            "spmv": "csr"
        },
        "solver": {
            "upper_trs": {
                "recurrent_residuals": [],
                "true_residuals": [],
                "iteration_timestamps": [],
                "rhs_norm": 6.0,
                "generate": {
                    "components": {
                        "upper_trs::generate#3": 100,
                        "upper_trs::init_struct#1": 700
                    },
                    "time": 700.0
                },
                "apply": {
                    "components": {
                        "upper_trs::should_perform_transpose#1": 200,
                        "upper_trs::solve#6": 400
                    },
                    "iterations": 0,
                    "time": 700.0
                },
                "residual_norm": 7.12901703041511e-16,
                "completed": true
            }
        }
    }
]

By default, the right-hand side consists of all ones or the specified vector in the rhs json parameter, it can be set to random values using the -random_rhs flag

The specified runtimes are in nanoseconds.

If you want to extract the lower triangular factor of a MatrixMarket file, you can use

awk '{ if ($1 >= $2 || $1 ~ /^%/) print }' input.mtx > lower_triangular.mtx
awk '{ if ($1 <= $2 || $1 ~ /^%/) print }' input.mtx > upper_triangular.mtx

you only need to modify the number of non-zeros in the header line afterwards

Finally, you can specify different strategies by using different csr formats in optimal, i.e. csrc, csrm, csri.

@upsj upsj self-assigned this Nov 19, 2020
@upsj upsj added reg:benchmarking This is related to benchmarking. 1:ST:ready-for-review This PR is ready for review labels Nov 19, 2020
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LGTM

benchmark/solver/solver.cpp Outdated Show resolved Hide resolved
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Just wanted to say thanks a lot! That'll be a great help to me.

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Codecov Report

Merging #664 (f25a8d7) into develop (471108e) will decrease coverage by 0.02%.
The diff coverage is n/a.

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@@             Coverage Diff             @@
##           develop     #664      +/-   ##
===========================================
- Coverage    92.87%   92.84%   -0.03%     
===========================================
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  Lines        23296    23296              
===========================================
- Hits         21635    21630       -5     
- Misses        1661     1666       +5     
Impacted Files Coverage Δ
omp/reorder/rcm_kernels.cpp 94.44% <0.00%> (-3.09%) ⬇️

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LGTM!

{"lower_trs",
[](std::shared_ptr<const gko::Executor> exec,
std::shared_ptr<const gko::LinOpFactory> precond) {
return gko::solver::LowerTrs<>::build()
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Just a question, we dont use a template type here ? Maybe it is better to add ValueType to all template parameters ?

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That's a valid point! However, I believe we should address this in another PR

@upsj upsj added 1:ST:ready-to-merge This PR is ready to merge. and removed 1:ST:ready-for-review This PR is ready for review labels Nov 19, 2020
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LGTM!

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This would be useful. I just had a small nitpick about the documentation.

BENCHMARKING.md Outdated Show resolved Hide resolved
BENCHMARKING.md Outdated Show resolved Hide resolved
Co-authored-by: tcojean <[email protected]>
Co-authored-by: Aditya Kashi <[email protected]>
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LGTM

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sonarcloud bot commented Nov 20, 2020

Kudos, SonarCloud Quality Gate passed!

Bug A 0 Bugs
Vulnerability A 0 Vulnerabilities (and Security Hotspot 0 Security Hotspots to review)
Code Smell A 0 Code Smells

0.0% 0.0% Coverage
0.0% 0.0% Duplication

@upsj upsj merged commit a5f362c into develop Nov 20, 2020
@upsj upsj deleted the trisolve_benchmark branch November 20, 2020 16:31
* `SOLVERS={bicgstab,bicg,cg,cgs,fcg,gmres,lower_trs,upper_trs}` - the solvers
which should be benchmarked. Multiple options can be passed. The default
is `cg`. Note that `lower/upper_trs` by default don't use a preconditioner,
as they are by default exact direct solvers.
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Not sure whether this is necessary, but should we mention that lowe/upper_trs expect a triangular matrix?

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Too late sorry, the PR is already merged 🤷‍♂️

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Essentially, I guess we should define what are 'lower_trs' and 'upper_trs', in case people are not familiar. From an applications point of view, or indeed from most common points of view, these two are really not in the same category as bicgstab, gmres, etc. We should address this in some other PR.

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I don't think we have to explain everything about the TRS here necessarily, because this here is only the benchmarks description. If you want information from a user POV, the class description etc should give you all you need. On the other hand, someone who reads this should know what to benchmark, and only wants to know how.

tcojean added a commit that referenced this pull request Aug 20, 2021
Ginkgo release 1.4.0

The Ginkgo team is proud to announce the new Ginkgo minor release 1.4.0. This
release brings most of the Ginkgo functionality to the Intel DPC++ ecosystem
which enables Intel-GPU and CPU execution. The only Ginkgo features which have
not been ported yet are some preconditioners.

Ginkgo's mixed-precision support is greatly enhanced thanks to:
1. The new Accessor concept, which allows writing kernels featuring on-the-fly
memory compression, among other features. The accessor can be used as
header-only, see the [accessor BLAS benchmarks repository](https://github.com/ginkgo-project/accessor-BLAS/tree/develop) as a usage example.
2. All LinOps now transparently support mixed-precision execution. By default,
this is done through a temporary copy which may have a performance impact but
already allows mixed-precision research.

Native mixed-precision ELL kernels are implemented which do not see this cost.
The accessor is also leveraged in a new CB-GMRES solver which allows for
performance improvements by compressing the Krylov basis vectors. Many other
features have been added to Ginkgo, such as reordering support, a new IDR
solver, Incomplete Cholesky preconditioner, matrix assembly support (only CPU
for now), machine topology information, and more!

Supported systems and requirements:
+ For all platforms, cmake 3.13+
+ C++14 compliant compiler
+ Linux and MacOS
  + gcc: 5.3+, 6.3+, 7.3+, all versions after 8.1+
  + clang: 3.9+
  + Intel compiler: 2018+
  + Apple LLVM: 8.0+
  + CUDA module: CUDA 9.0+
  + HIP module: ROCm 3.5+
  + DPC++ module: Intel OneAPI 2021.3. Set the CXX compiler to `dpcpp`.
+ Windows
  + MinGW and Cygwin: gcc 5.3+, 6.3+, 7.3+, all versions after 8.1+
  + Microsoft Visual Studio: VS 2019
  + CUDA module: CUDA 9.0+, Microsoft Visual Studio
  + OpenMP module: MinGW or Cygwin.


Algorithm and important feature additions:
+ Add a new DPC++ Executor for SYCL execution and other base utilities
  [#648](#648), [#661](#661), [#757](#757), [#832](#832)
+ Port matrix formats, solvers and related kernels to DPC++. For some kernels,
  also make use of a shared kernel implementation for all executors (except
  Reference). [#710](#710), [#799](#799), [#779](#779), [#733](#733), [#844](#844), [#843](#843), [#789](#789), [#845](#845), [#849](#849), [#855](#855), [#856](#856)
+ Add accessors which allow multi-precision kernels, among other things.
  [#643](#643), [#708](#708)
+ Add support for mixed precision operations through apply in all LinOps. [#677](#677)
+ Add incomplete Cholesky factorizations and preconditioners as well as some
  improvements to ILU. [#672](#672), [#837](#837), [#846](#846)
+ Add an AMGX implementation and kernels on all devices but DPC++.
  [#528](#528), [#695](#695), [#860](#860)
+ Add a new mixed-precision capability solver, Compressed Basis GMRES
  (CB-GMRES). [#693](#693), [#763](#763)
+ Add the IDR(s) solver. [#620](#620)
+ Add a new fixed-size block CSR matrix format (for the Reference executor).
  [#671](#671), [#730](#730)
+ Add native mixed-precision support to the ELL format. [#717](#717), [#780](#780)
+ Add Reverse Cuthill-McKee reordering [#500](#500), [#649](#649)
+ Add matrix assembly support on CPUs. [#644](#644)
+ Extends ISAI from triangular to general and spd matrices. [#690](#690)

Other additions:
+ Add the possibility to apply real matrices to complex vectors.
  [#655](#655), [#658](#658)
+ Add functions to compute the absolute of a matrix format. [#636](#636)
+ Add symmetric permutation and improve existing permutations.
  [#684](#684), [#657](#657), [#663](#663)
+ Add a MachineTopology class with HWLOC support [#554](#554), [#697](#697)
+ Add an implicit residual norm criterion. [#702](#702), [#818](#818), [#850](#850)
+ Row-major accessor is generalized to more than 2 dimensions and a new
  "block column-major" accessor has been added. [#707](#707)
+ Add an heat equation example. [#698](#698), [#706](#706)
+ Add ccache support in CMake and CI. [#725](#725), [#739](#739)
+ Allow tuning and benchmarking variables non intrusively. [#692](#692)
+ Add triangular solver benchmark [#664](#664)
+ Add benchmarks for BLAS operations [#772](#772), [#829](#829)
+ Add support for different precisions and consistent index types in benchmarks.
  [#675](#675), [#828](#828)
+ Add a Github bot system to facilitate development and PR management.
  [#667](#667), [#674](#674), [#689](#689), [#853](#853)
+ Add Intel (DPC++) CI support and enable CI on HPC systems. [#736](#736), [#751](#751), [#781](#781)
+ Add ssh debugging for Github Actions CI. [#749](#749)
+ Add pipeline segmentation for better CI speed. [#737](#737)


Changes:
+ Add a Scalar Jacobi specialization and kernels. [#808](#808), [#834](#834), [#854](#854)
+ Add implicit residual log for solvers and benchmarks. [#714](#714)
+ Change handling of the conjugate in the dense dot product. [#755](#755)
+ Improved Dense stride handling. [#774](#774)
+ Multiple improvements to the OpenMP kernels performance, including COO,
an exclusive prefix sum, and more. [#703](#703), [#765](#765), [#740](#740)
+ Allow specialization of submatrix and other dense creation functions in solvers. [#718](#718)
+ Improved Identity constructor and treatment of rectangular matrices. [#646](#646)
+ Allow CUDA/HIP executors to select allocation mode. [#758](#758)
+ Check if executors share the same memory. [#670](#670)
+ Improve test install and smoke testing support. [#721](#721)
+ Update the JOSS paper citation and add publications in the documentation.
  [#629](#629), [#724](#724)
+ Improve the version output. [#806](#806)
+ Add some utilities for dim and span. [#821](#821)
+ Improved solver and preconditioner benchmarks. [#660](#660)
+ Improve benchmark timing and output. [#669](#669), [#791](#791), [#801](#801), [#812](#812)


Fixes:
+ Sorting fix for the Jacobi preconditioner. [#659](#659)
+ Also log the first residual norm in CGS [#735](#735)
+ Fix BiCG and HIP CSR to work with complex matrices. [#651](#651)
+ Fix Coo SpMV on strided vectors. [#807](#807)
+ Fix segfault of extract_diagonal, add short-and-fat test. [#769](#769)
+ Fix device_reset issue by moving counter/mutex to device. [#810](#810)
+ Fix `EnableLogging` superclass. [#841](#841)
+ Support ROCm 4.1.x and breaking HIP_PLATFORM changes. [#726](#726)
+ Decreased test size for a few device tests. [#742](#742)
+ Fix multiple issues with our CMake HIP and RPATH setup.
  [#712](#712), [#745](#745), [#709](#709)
+ Cleanup our CMake installation step. [#713](#713)
+ Various simplification and fixes to the Windows CMake setup. [#720](#720), [#785](#785)
+ Simplify third-party integration. [#786](#786)
+ Improve Ginkgo device arch flags management. [#696](#696)
+ Other fixes and improvements to the CMake setup.
  [#685](#685), [#792](#792), [#705](#705), [#836](#836)
+ Clarification of dense norm documentation [#784](#784)
+ Various development tools fixes and improvements [#738](#738), [#830](#830), [#840](#840)
+ Make multiple operators/constructors explicit. [#650](#650), [#761](#761)
+ Fix some issues, memory leaks and warnings found by MSVC.
  [#666](#666), [#731](#731)
+ Improved solver memory estimates and consistent iteration counts [#691](#691)
+ Various logger improvements and fixes [#728](#728), [#743](#743), [#754](#754)
+ Fix for ForwardIterator requirements in iterator_factory. [#665](#665)
+ Various benchmark fixes. [#647](#647), [#673](#673), [#722](#722)
+ Various CI fixes and improvements. [#642](#642), [#641](#641), [#795](#795), [#783](#783), [#793](#793), [#852](#852)


Related PR: #857
tcojean added a commit that referenced this pull request Aug 23, 2021
Release 1.4.0 to master

The Ginkgo team is proud to announce the new Ginkgo minor release 1.4.0. This
release brings most of the Ginkgo functionality to the Intel DPC++ ecosystem
which enables Intel-GPU and CPU execution. The only Ginkgo features which have
not been ported yet are some preconditioners.

Ginkgo's mixed-precision support is greatly enhanced thanks to:
1. The new Accessor concept, which allows writing kernels featuring on-the-fly
memory compression, among other features. The accessor can be used as
header-only, see the [accessor BLAS benchmarks repository](https://github.com/ginkgo-project/accessor-BLAS/tree/develop) as a usage example.
2. All LinOps now transparently support mixed-precision execution. By default,
this is done through a temporary copy which may have a performance impact but
already allows mixed-precision research.

Native mixed-precision ELL kernels are implemented which do not see this cost.
The accessor is also leveraged in a new CB-GMRES solver which allows for
performance improvements by compressing the Krylov basis vectors. Many other
features have been added to Ginkgo, such as reordering support, a new IDR
solver, Incomplete Cholesky preconditioner, matrix assembly support (only CPU
for now), machine topology information, and more!

Supported systems and requirements:
+ For all platforms, cmake 3.13+
+ C++14 compliant compiler
+ Linux and MacOS
  + gcc: 5.3+, 6.3+, 7.3+, all versions after 8.1+
  + clang: 3.9+
  + Intel compiler: 2018+
  + Apple LLVM: 8.0+
  + CUDA module: CUDA 9.0+
  + HIP module: ROCm 3.5+
  + DPC++ module: Intel OneAPI 2021.3. Set the CXX compiler to `dpcpp`.
+ Windows
  + MinGW and Cygwin: gcc 5.3+, 6.3+, 7.3+, all versions after 8.1+
  + Microsoft Visual Studio: VS 2019
  + CUDA module: CUDA 9.0+, Microsoft Visual Studio
  + OpenMP module: MinGW or Cygwin.


Algorithm and important feature additions:
+ Add a new DPC++ Executor for SYCL execution and other base utilities
  [#648](#648), [#661](#661), [#757](#757), [#832](#832)
+ Port matrix formats, solvers and related kernels to DPC++. For some kernels,
  also make use of a shared kernel implementation for all executors (except
  Reference). [#710](#710), [#799](#799), [#779](#779), [#733](#733), [#844](#844), [#843](#843), [#789](#789), [#845](#845), [#849](#849), [#855](#855), [#856](#856)
+ Add accessors which allow multi-precision kernels, among other things.
  [#643](#643), [#708](#708)
+ Add support for mixed precision operations through apply in all LinOps. [#677](#677)
+ Add incomplete Cholesky factorizations and preconditioners as well as some
  improvements to ILU. [#672](#672), [#837](#837), [#846](#846)
+ Add an AMGX implementation and kernels on all devices but DPC++.
  [#528](#528), [#695](#695), [#860](#860)
+ Add a new mixed-precision capability solver, Compressed Basis GMRES
  (CB-GMRES). [#693](#693), [#763](#763)
+ Add the IDR(s) solver. [#620](#620)
+ Add a new fixed-size block CSR matrix format (for the Reference executor).
  [#671](#671), [#730](#730)
+ Add native mixed-precision support to the ELL format. [#717](#717), [#780](#780)
+ Add Reverse Cuthill-McKee reordering [#500](#500), [#649](#649)
+ Add matrix assembly support on CPUs. [#644](#644)
+ Extends ISAI from triangular to general and spd matrices. [#690](#690)

Other additions:
+ Add the possibility to apply real matrices to complex vectors.
  [#655](#655), [#658](#658)
+ Add functions to compute the absolute of a matrix format. [#636](#636)
+ Add symmetric permutation and improve existing permutations.
  [#684](#684), [#657](#657), [#663](#663)
+ Add a MachineTopology class with HWLOC support [#554](#554), [#697](#697)
+ Add an implicit residual norm criterion. [#702](#702), [#818](#818), [#850](#850)
+ Row-major accessor is generalized to more than 2 dimensions and a new
  "block column-major" accessor has been added. [#707](#707)
+ Add an heat equation example. [#698](#698), [#706](#706)
+ Add ccache support in CMake and CI. [#725](#725), [#739](#739)
+ Allow tuning and benchmarking variables non intrusively. [#692](#692)
+ Add triangular solver benchmark [#664](#664)
+ Add benchmarks for BLAS operations [#772](#772), [#829](#829)
+ Add support for different precisions and consistent index types in benchmarks.
  [#675](#675), [#828](#828)
+ Add a Github bot system to facilitate development and PR management.
  [#667](#667), [#674](#674), [#689](#689), [#853](#853)
+ Add Intel (DPC++) CI support and enable CI on HPC systems. [#736](#736), [#751](#751), [#781](#781)
+ Add ssh debugging for Github Actions CI. [#749](#749)
+ Add pipeline segmentation for better CI speed. [#737](#737)


Changes:
+ Add a Scalar Jacobi specialization and kernels. [#808](#808), [#834](#834), [#854](#854)
+ Add implicit residual log for solvers and benchmarks. [#714](#714)
+ Change handling of the conjugate in the dense dot product. [#755](#755)
+ Improved Dense stride handling. [#774](#774)
+ Multiple improvements to the OpenMP kernels performance, including COO,
an exclusive prefix sum, and more. [#703](#703), [#765](#765), [#740](#740)
+ Allow specialization of submatrix and other dense creation functions in solvers. [#718](#718)
+ Improved Identity constructor and treatment of rectangular matrices. [#646](#646)
+ Allow CUDA/HIP executors to select allocation mode. [#758](#758)
+ Check if executors share the same memory. [#670](#670)
+ Improve test install and smoke testing support. [#721](#721)
+ Update the JOSS paper citation and add publications in the documentation.
  [#629](#629), [#724](#724)
+ Improve the version output. [#806](#806)
+ Add some utilities for dim and span. [#821](#821)
+ Improved solver and preconditioner benchmarks. [#660](#660)
+ Improve benchmark timing and output. [#669](#669), [#791](#791), [#801](#801), [#812](#812)


Fixes:
+ Sorting fix for the Jacobi preconditioner. [#659](#659)
+ Also log the first residual norm in CGS [#735](#735)
+ Fix BiCG and HIP CSR to work with complex matrices. [#651](#651)
+ Fix Coo SpMV on strided vectors. [#807](#807)
+ Fix segfault of extract_diagonal, add short-and-fat test. [#769](#769)
+ Fix device_reset issue by moving counter/mutex to device. [#810](#810)
+ Fix `EnableLogging` superclass. [#841](#841)
+ Support ROCm 4.1.x and breaking HIP_PLATFORM changes. [#726](#726)
+ Decreased test size for a few device tests. [#742](#742)
+ Fix multiple issues with our CMake HIP and RPATH setup.
  [#712](#712), [#745](#745), [#709](#709)
+ Cleanup our CMake installation step. [#713](#713)
+ Various simplification and fixes to the Windows CMake setup. [#720](#720), [#785](#785)
+ Simplify third-party integration. [#786](#786)
+ Improve Ginkgo device arch flags management. [#696](#696)
+ Other fixes and improvements to the CMake setup.
  [#685](#685), [#792](#792), [#705](#705), [#836](#836)
+ Clarification of dense norm documentation [#784](#784)
+ Various development tools fixes and improvements [#738](#738), [#830](#830), [#840](#840)
+ Make multiple operators/constructors explicit. [#650](#650), [#761](#761)
+ Fix some issues, memory leaks and warnings found by MSVC.
  [#666](#666), [#731](#731)
+ Improved solver memory estimates and consistent iteration counts [#691](#691)
+ Various logger improvements and fixes [#728](#728), [#743](#743), [#754](#754)
+ Fix for ForwardIterator requirements in iterator_factory. [#665](#665)
+ Various benchmark fixes. [#647](#647), [#673](#673), [#722](#722)
+ Various CI fixes and improvements. [#642](#642), [#641](#641), [#795](#795), [#783](#783), [#793](#793), [#852](#852)

Related PR: #866
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