Fix scalar jacobi generate on different precision and add DiagonalLinOpExtractable #834

yhmtsai · 2021-07-14T13:13:22Z

This pr adds DiagonalLinOpExtractable and fix scalar Jacobi issue on generation on different precision matrix.

the failed test can be found in https://gitlab.com/ginkgo-project/ginkgo-public-ci/-/pipelines/336902618

codecov · 2021-07-14T16:43:28Z

Codecov Report

Merging #834 (28e8097) into develop (2bfa1ef) will increase coverage by 1.15%.
The diff coverage is 94.11%.

@@             Coverage Diff             @@
##           develop     #834      +/-   ##
===========================================
+ Coverage    93.38%   94.54%   +1.15%     
===========================================
  Files          411      411              
  Lines        33123    33132       +9     
===========================================
+ Hits         30931    31323     +392     
+ Misses        2192     1809     -383

Impacted Files	Coverage Δ
include/ginkgo/core/matrix/diagonal.hpp	`100.00% <ø> (ø)`
reference/test/preconditioner/jacobi.cpp	`95.45% <83.33%> (-0.70%)`	⬇️
core/preconditioner/jacobi.cpp	`99.06% <87.50%> (-0.94%)`	⬇️
core/matrix/diagonal.cpp	`80.35% <100.00%> (+1.30%)`	⬆️
include/ginkgo/core/base/lin_op.hpp	`90.74% <100.00%> (+0.08%)`	⬆️
reference/test/matrix/diagonal_kernels.cpp	`91.71% <100.00%> (+0.63%)`	⬆️
core/base/extended_float.hpp	`91.26% <0.00%> (-0.98%)`	⬇️
core/test/utils/value_generator.hpp	`100.00% <0.00%> (ø)`
reference/factorization/par_ilut_kernels.cpp	`100.00% <0.00%> (+0.49%)`	⬆️
core/test/utils/assertions.hpp	`72.84% <0.00%> (+1.35%)`	⬆️
... and 2 more

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yhmtsai · 2021-07-15T16:08:46Z

rebase!

pratikvn · 2021-07-15T16:16:40Z

I think the omp/debug/shared job was failing ?

yhmtsai · 2021-07-15T16:23:14Z

yes

pratikvn

Some questions.

CMakeLists.txt

pratikvn · 2021-07-19T08:14:41Z

core/matrix/diagonal.cpp

+std::unique_ptr<LinOp> DiagonalExtractable<ValueType>::extract_diagonal_linop()
+    const
+{
+    auto diag = this->extract_diagonal();


Am I missing something here ? Diagonal does not have a extract_diagonal method, right ?

no, but the DiagonalExtractable use as or dynamic_cast to convert the output from Diagonal to Linop.
then it requires Diagonal be a complete class.
I put the implementation into Diagonal and instantiate it.

pratikvn · 2021-07-19T08:19:17Z

core/preconditioner/jacobi.cpp

+        auto diag = share(as<DiagonalLinOpExtractable>(system_matrix)
+                              ->extract_diagonal_linop());


Is there some reason we need the extract_diagonal_linop and extract_diagonal will not work ?

extract_diagonal require the valuetype, but extract_diagonal_linop does not. we can get the diag first and then check which precision to work.
I am thinking do the dynamic_cast to check the matrix type and use corresponding extract_diag.

An alternative to DiagonalLinOpExtractable would be using

auto csr_mtx = convert_to_with_sorting<csr_type>(exec, system_matrix, /*skip_sorting=*/true);

from the non-scalar branch and extracting from that. That would also make for more consistent behavior between scalar and block Jacobi.

I would like to avoid the converting matrix self because we need to pay more for it against converting diag

Could we also not convert the extracted diagonal after calling the extract_diagonal. Currently, Diagonal does not have mixed precision convert methods, but we could easily add those

Oh yes, I think I like that idea best of all suggestions yet :)

but the issue is different, right?
you still need to dynamic_cast to the correct type first.
and it is the same impl in the current one if we use default behavior
if we use direct mapping, we need to add all matrix to support it (at least csr)

Actually, i am a bit confused as to how the current implementation works. extract_diagonal_linop should return a Diagonal of ValueType right ? Because it is just calling the extract_diagonal of Csr<ValueType, IndexType>. So, the else if branch in line 243, will never be taken ?

extract_diagonal_linop returns LinOp (it is Diaonal<system_matrix ValueType>), the valuetype is from system matrix not from the jacobi.
I realize we miss the precision convert in Diagonal, but I guess I can not use it directly here?

You could use something similar to make_temporary_conversion. The framework also supports other matrix types, you only need to wrap it like you see in that function. Precision conversions in Diagonal is definitely a good idea, I forgot to suggest those when we added the format.

upsj · 2021-07-19T10:30:59Z

core/preconditioner/jacobi.cpp

+        if (auto diag_dense =
+                std::dynamic_pointer_cast<matrix::Diagonal<ValueType>>(diag)) {
+            auto temp = gko::Array<ValueType>::view(
+                exec, system_matrix->get_size()[0], diag_dense->get_values());


The source of the segfault we just saw:

Suggested change

exec, system_matrix->get_size()[0], diag_dense->get_values());

diag_dense->get_executor(), system_matrix->get_size()[0], diag_dense->get_values());

good catch!

Could you add a test for this behavior (generating with block size 1 on CUDA from a Reference/OMP matrix)?

Good catch. I think we should think about having a better way to test these cross-executor combinations ? I think we hardly test any cross-executor functionality now.

for easy test, we can use get_test_executor() in random or in order to avoid specify the executor in each place

Yeah, I think you can do a specific test for this and we can think discuss a more generic approach later.

core/preconditioner/jacobi.cpp

upsj

LGTM! Thanks also for fixing the wrong executor in the array view :)

core/matrix/diagonal.cpp

core/preconditioner/jacobi.cpp

upsj · 2021-08-02T15:38:21Z

core/preconditioner/jacobi.cpp

+        auto diag = share(as<DiagonalLinOpExtractable>(system_matrix)
+                              ->extract_diagonal_linop());
+        auto diag_vt =
+            ::gko::detail::temporary_conversion<matrix::Diagonal<ValueType>>::


same here: Is the fully qualified name necessary?

Suggested change

::gko::detail::temporary_conversion<matrix::Diagonal<ValueType>>::

detail::temporary_conversion<matrix::Diagonal<ValueType>>::

I need to put gko::, or I will get the error
error: 'temporary_conversion' is not a member of 'gko::preconditioner::detail'
it look for the function in gko::preconditioner::detail not gko::detail

tcojean

LGTM.

include/ginkgo/core/base/lin_op.hpp

Co-authored-by: Tobias Ribizel <ribizel@kit.edu>

Co-authored-by: Pratik Nayak <pratikvn@protonmail.com> Co-authored-by: Tobias Ribizel <ribizel@kit.edu>

Co-authored-by: Terry Cojean <terry.cojean@kit.edu> Co-authored-by: Tobias Ribizel <ribizel@kit.edu>

sonarcloud · 2021-08-03T17:04:56Z

Kudos, SonarCloud Quality Gate passed!

0 Bugs
0 Vulnerabilities
0 Security Hotspots
1 Code Smell

94.4% Coverage
0.0% Duplication

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

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

yhmtsai added the 1:ST:ready-for-review This PR is ready for review label Jul 14, 2021

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ginkgo-bot added mod:core This is related to the core module. mod:reference This is related to the reference module. reg:testing This is related to testing. type:matrix-format This is related to the Matrix formats type:preconditioner This is related to the preconditioners labels Jul 14, 2021

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pratikvn mentioned this pull request Jul 20, 2021

Generalize testing of cross-executor functionality #839

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include/ginkgo/core/base/lin_op.hpp Show resolved Hide resolved

yhmtsai and others added 6 commits August 3, 2021 12:11

scalar_jacobi on diff precision csr

47312f1

scalarjacobi allows diff system matrix and add DiagonalLinOpExtractable

d9392ee

move diagonalextrable impl with instantiate

76b3881

fix executor issue

824e7e4

Co-authored-by: Tobias Ribizel <ribizel@kit.edu>

use template_conversion, add diagonal precision convert

914d0f4

Co-authored-by: Pratik Nayak <pratikvn@protonmail.com> Co-authored-by: Tobias Ribizel <ribizel@kit.edu>

review update

28e8097

Co-authored-by: Terry Cojean <terry.cojean@kit.edu> Co-authored-by: Tobias Ribizel <ribizel@kit.edu>

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Fix scalar jacobi generate on different precision and add DiagonalLinOpExtractable #834

Fix scalar jacobi generate on different precision and add DiagonalLinOpExtractable #834

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		auto diag = share(as<DiagonalLinOpExtractable>(system_matrix)
		->extract_diagonal_linop());

	exec, system_matrix->get_size()[0], diag_dense->get_values());
	diag_dense->get_executor(), system_matrix->get_size()[0], diag_dense->get_values());

	::gko::detail::temporary_conversion<matrix::Diagonal<ValueType>>::
	detail::temporary_conversion<matrix::Diagonal<ValueType>>::

Fix scalar jacobi generate on different precision and add DiagonalLinOpExtractable #834

Fix scalar jacobi generate on different precision and add DiagonalLinOpExtractable #834

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