General isai #690

fritzgoebel · 2021-01-05T17:29:39Z

This PR extends ISAI from triangular to general and spd matrices.
When generated for a general or spd matrix, it can be used as a preconditioner on its own, i.e. outside of a factorization.
In the spd case, an ISAI L is computed on the lower triangular half of A, a diagonal scaling step makes the result equivalent to an FSPAI preconditioner, so an approximation on the exact Cholesky factorization. In this case, the preconditioner is no longer a single CSR matrix but a composition of L^T and L.

For rows of size up to 32, the corresponding small dense systems are solved directly, for larger rows an excess system is built up as it is for triangular systems. The excess system is solved using GMRES with a block-Jacobi preconditioner.
A new factory parameter excess_limit sets a threshold to the size of the excess system. If set, the excess system gets solved as soon as this threshold is exceeded, the solution scattered to the result matrix and a new excess system is built up for the remaining rows, and so on.

fritzgoebel · 2021-01-05T17:33:28Z

format!

upsj

LGTM - great job! I didn't look over the (especially reference) tests in too much detail, but the rest looked solid.

core/preconditioner/isai_kernels.hpp

include/ginkgo/core/preconditioner/isai.hpp

common/components/warp_blas.hpp.inc

upsj · 2021-01-07T18:44:34Z

core/preconditioner/isai.cpp

+ trs_factory =
+ Gmres::build()
+ .with_preconditioner(
+ bj::build().with_max_block_size(32u).on(exec))


Two comments here:

we could make the solver factory a factory parameter of ISAI that defaults to this choice, then people can also use GMRES with BJ for triangular systems if they like, similar to what the ILU preconditioner does.

Since we already have block structure information, why not pass this on to the preconditioner as block pointers clamped at 32? That might significantly improve the preconditioner quality.

omp/preconditioner/isai_kernels.cpp

reference/preconditioner/isai_kernels.cpp

omp/preconditioner/isai_kernels.cpp

hartwiganzt · 2021-01-20T19:57:01Z

hip/preconditioner/isai_kernels.hip.cpp

@@ -168,6 +168,24 @@ GKO_INSTANTIATE_FOR_EACH_VALUE_AND_INDEX_TYPE(
 GKO_DECLARE_ISAI_GENERATE_EXCESS_SYSTEM_KERNEL);


+template <typename ValueType, typename IndexType>
+void scale_excess_solution(std::shared_ptr<const DefaultExecutor>,


Do you anywhere document your strategy, like what you do with the long-row systems?

codecov · 2021-03-01T22:31:16Z

Codecov Report

Merging #690 (94b48f3) into develop (4e5dcca) will increase coverage by 0.13%.
The diff coverage is 97.05%.

❗ Current head 94b48f3 differs from pull request most recent head c0ec5ef. Consider uploading reports for the commit c0ec5ef to get more accurate results

@@             Coverage Diff             @@
##           develop     #690      +/-   ##
===========================================
+ Coverage    92.41%   92.54%   +0.13%     
===========================================
  Files          380      380              
  Lines        27493    28153     +660     
===========================================
+ Hits         25407    26055     +648     
- Misses        2086     2098      +12

Impacted Files	Coverage Δ
core/device_hooks/common_kernels.inc.cpp	`0.00% <0.00%> (ø)`
omp/preconditioner/isai_kernels.cpp	`87.50% <76.00%> (-6.50%)`	⬇️
reference/test/preconditioner/isai_kernels.cpp	`96.35% <96.69%> (+1.54%)`	⬆️
core/preconditioner/isai.cpp	`96.09% <97.87%> (+2.06%)`	⬆️
reference/preconditioner/isai_kernels.cpp	`98.90% <98.68%> (+1.32%)`	⬆️
core/test/preconditioner/isai.cpp	`96.05% <100.00%> (+8.95%)`	⬆️
core/test/utils/matrix_generator.hpp	`100.00% <100.00%> (ø)`
include/ginkgo/core/preconditioner/isai.hpp	`100.00% <100.00%> (ø)`
omp/test/preconditioner/isai_kernels.cpp	`99.71% <100.00%> (+0.40%)`	⬆️
... and 4 more

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hartwiganzt

LGTM

yhmtsai

Miss free memory in one lambda function

core/preconditioner/isai_kernels.hpp

cuda/test/preconditioner/isai_kernels.cpp

reference/test/preconditioner/isai_kernels.cpp

include/ginkgo/core/preconditioner/isai.hpp

yhmtsai · 2021-03-16T07:09:54Z

omp/preconditioner/isai_kernels.cpp

+ rhs[rhs_one_idx] = one<ValueType>();
+
+ // fill in transposed system
+ ValueType *transposed_system = new ValueType[size * size];


Miss the deconstructor. Can it use the gko::Array?
gko::matrix can not be used here due to circular dependence. Does it also apply to gko::Array?

Good catch! No, you can use gko::Array, since it is a header-only type. I preallocated temporary storage for the triangular solve, you should be able to use the same approach here.

omp/preconditioner/isai_kernels.cpp

fritzgoebel · 2021-03-16T19:01:26Z

LGTM in general, only 3 points:

1. Do you test the excess_limit behavior somewhere? It looks to me like all tests use `0, num_rows` as the limit parameters in generate_excess_system.

2. Did you try out explicitly specifying the block pointers for Block Jacobi?

3. Could you try addressing the concerns about memory movement in the excess block pointers for excess limit handling?

1: good catch!
2: Not yet, I plan on doing that in a different pr later.
3: Please check if that's okay :)

thoasm

LGTM!
Some small nits, but nothing major.

common/components/warp_blas.hpp.inc

core/preconditioner/isai.cpp

core/test/utils/matrix_generator.hpp

Co-authored-by: fritzgoebel <[email protected]>

Co-authored-by: Yuhsiang M. Tsai <[email protected]>

yhmtsai

LGTM. some minor format and documentation suggestion

include/ginkgo/core/preconditioner/isai.hpp

omp/test/preconditioner/isai_kernels.cpp

cuda/test/preconditioner/isai_kernels.cpp

hip/test/preconditioner/isai_kernels.hip.cpp

Co-authored-by: Yuhsiang M. Tsai <[email protected]>

sonarcloud · 2021-03-18T22:27:54Z

SonarCloud Quality Gate failed.

0 Bugs
0 Vulnerabilities
0 Security Hotspots
31 Code Smells

76.7% Coverage
11.3% 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

fritzgoebel requested review from upsj, pratikvn, Slaedr, thoasm, yhmtsai, hartwiganzt and tcojean January 5, 2021 17:29

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ginkgo-bot added mod:all This touches all Ginkgo modules. reg:benchmarking This is related to benchmarking. reg:build This is related to the build system. reg:testing This is related to testing. type:preconditioner This is related to the preconditioners labels Jan 5, 2021

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Mixed Precision ISAI #719

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First working reference of general isai

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initialize initial guess for gmres

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Write tests for general ISAI

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adjust variable names to general setting

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Co-authored-by: fritzgoebel <[email protected]>

Adjust tolerance type in gmres

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