Amgx parallel graph match reference and fill_seq_array #528

yhmtsai · 2020-05-11T12:21:54Z

This PR adds MultigridLevel and UseComposition class and AmgxPgm (AMGX parallel graph match) class.

Any class to represent one level of Multigrid should inherit from Multigrid class.
MultigridLevel contains fine_op and a composition of (prolong, coarse, restrict) by set_multigrid_level.
It also provides get_*_op() to get the component of MultigridLevel.
UseComposition considers end-class to contain composition not be a composition, which gives the end-class full linop functionalities.
fill_seq_array fills array from 0 to size-1

AmgxPgm only contains size=2 implementation which is shown on the paper.

need to extract diag from csr, which is fully implemented in the #563

Merge Add extract_diagonal for all matrix formats #563
Merge Add absolute of matrix format #636 to use absolute function

codecov · 2020-05-11T14:15:32Z

Codecov Report

Merging #528 (1fdfd83) into develop (481fa71) will decrease coverage by 0.03%.
The diff coverage is 90.11%.

@@             Coverage Diff             @@
##           develop     #528      +/-   ##
===========================================
- Coverage    92.47%   92.44%   -0.04%     
===========================================
  Files          362      369       +7     
  Lines        27078    27520     +442     
===========================================
+ Hits         25041    25440     +399     
- Misses        2037     2080      +43

Impacted Files	Coverage Δ
core/device_hooks/common_kernels.inc.cpp	`0.00% <0.00%> (ø)`
include/ginkgo/core/base/types.hpp	`92.59% <ø> (ø)`
include/ginkgo/core/matrix/csr.hpp	`47.72% <ø> (ø)`
omp/multigrid/amgx_pgm_kernels.cpp	`0.00% <0.00%> (ø)`
core/test/multigrid/amgx_pgm.cpp	`84.61% <84.61%> (ø)`
include/ginkgo/core/multigrid/multigrid_level.hpp	`87.50% <87.50%> (ø)`
include/ginkgo/core/multigrid/amgx_pgm.hpp	`91.30% <91.30%> (ø)`
reference/multigrid/amgx_pgm_kernels.cpp	`94.87% <94.87%> (ø)`
core/multigrid/amgx_pgm.cpp	`95.16% <95.16%> (ø)`
reference/test/multigrid/amgx_pgm_kernels.cpp	`98.17% <98.17%> (ø)`
... and 13 more

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hip/matrix/csr_kernels.hip.cpp

upsj

LGTM so far, I still need to look at the tests

It might be helpful to move the symmetrization and normalization used in find_strongest_neighbor to another kernel. Then we might be able to take advantage of existing csr_transpose and spgeam kernels, which is especially important for GPU implementation.
Fritz's most recent PR contains an extract_diag kernel, so you can probably remove that one after it is merged.

upsj · 2020-06-12T11:32:15Z

include/ginkgo/core/multigrid/amgx_pgm.hpp

+ /**
+ * Returns the aggregate group.
+ *
+ * @return the aggregate group.
+ */


Can you maybe provide a more detailed explanation of what this aggregate group stores? What is its size, what does a value stand for? (I am guessing a mapping row -> coarse row ID?)

upsj · 2020-06-12T11:34:30Z

include/ginkgo/core/multigrid/restrict_prolong.hpp

+ *
+ * @return this
+ */
+ RestrictProlong *prolong_applyadd(const LinOp *b, LinOp *x)


Just a side-node: In Coo, we called this operation apply2, but I am not really suggesting this here. Do I remember correctly that P = R^T?

I think applyadd should be more precise information on this behavior than apply2?
P = R^T in general, but some cases they can be different.

upsj · 2020-06-12T11:47:48Z

core/multigrid/amgx_pgm.cpp

+ auto coarse = matrix::Csr<ValueType, IndexType>::create(
+ exec, dim<2>{num_agg, num_agg}, 0, source->get_strategy());
+ exec->run(amgx_pgm::make_amgx_pgm_generate(source, agg, coarse.get()));
+ matrix::CsrBuilder<ValueType, IndexType> builder(coarse.get());


Suggested change

matrix::CsrBuilder<ValueType, IndexType> builder(coarse.get());

This should already be handled in the kernel

upsj · 2020-06-12T12:16:42Z

core/multigrid/amgx_pgm.cpp

+ // copy the agg to intermediate_agg
+ intermediate_agg = agg_;
+ }
+ while (num_unagg != 0) {


Can it happen that we need to call assign_to_exist_agg multiple times? To me, it looks like every unassigned row will be assigned after the kernel finishes.

You are right. it will assign the unagg to one agg group or itself.

Co-authored-by: Tobias Ribizel <[email protected]>

Co-authored-by: Pratik Nayak <[email protected]>

Co-authored-by: Terry Cojean <[email protected]>

Co-authored-by: Pratik Nayak <[email protected]> Co-authored-by: Terry Cojean <[email protected]> Co-authored-by: Tobias Ribizel <[email protected]>

- use consistent indextype of num_agg/num_unagg - format - use read to build the csr - improve document Co-authored-by: Pratik Nayak <[email protected]> Co-authored-by: Terry Cojean <[email protected]> Co-authored-by: Tobias Ribizel <[email protected]>

Co-authored-by: Pratik Nayak <[email protected]>

Co-authored-by: Aditya Kashi <[email protected]> Co-authored-by: Pratik Nayak <[email protected]> Co-authored-by: Terry Cojean <[email protected]>

- MultigridLevel as interface without ValueType information - EnableMultigridLevel gives the default impl with composition

pratikvn

LGTM!

sonarcloud · 2021-03-11T20:02:47Z

Kudos, SonarCloud Quality Gate passed!

0 Bugs
0 Vulnerabilities
0 Security Hotspots
54 Code Smells

82.2% 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 is:new-feature A request or implementation of a feature that does not exist yet. mod:core This is related to the core module. mod:reference This is related to the reference module. 1:ST:do-not-merge Please do not merge PR this yet. labels May 11, 2020

yhmtsai self-assigned this May 11, 2020

yhmtsai mentioned this pull request May 11, 2020

Multigrid CoarseFine structure #529

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pratikvn added the type:multigrid This is related to multigrid label May 12, 2020

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yhmtsai requested review from upsj, tcojean, thoasm, fritzgoebel, hartwiganzt and pratikvn June 5, 2020 13:55

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yhmtsai and others added 20 commits March 11, 2021 17:09

fix control flow and use fill_array

c856a6e

fix amg_pgm_kernel determinstic problem

0b8b39d

converting to csr if the system matrix is not csr

1300930

remove unneeded code, add description of agg_group

bdbf0b7

Co-authored-by: Tobias Ribizel <[email protected]>

use AbsoluteComputable and update documentation

89876a2

Co-authored-by: Pratik Nayak <[email protected]>

update focumentation and fix format

bb213cf

Co-authored-by: Terry Cojean <[email protected]>

review update

6b6525b

Co-authored-by: Pratik Nayak <[email protected]> Co-authored-by: Terry Cojean <[email protected]> Co-authored-by: Tobias Ribizel <[email protected]>

add multigrid related interface and move to linop

a249373

add multigrid_level default apply and test

e8682c7

Rename the multigrid interface, improve doc

ac20215

Co-authored-by: Pratik Nayak <[email protected]>

WIP: add UseComposition and ApplyAddable interface

29aca61

WIP

122b349

mapping, multigridlevel, usecomp, applyaddable

4c3f785

implement fill_seq_array and add test

629004d

use csr to represent restrict/prolong

85449b8

remove unused func, add documentation

8b140dd

Add fill_array TypedTest and update documentation

9d4f8bc

Co-authored-by: Aditya Kashi <[email protected]> Co-authored-by: Pratik Nayak <[email protected]> Co-authored-by: Terry Cojean <[email protected]>

RestrictProlong -> MgLevel, rstr_prlg -> mg_level

599d740

split MultigridLevel EnableMultigridLevel

1fdfd83

- MultigridLevel as interface without ValueType information - EnableMultigridLevel gives the default impl with composition

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Multigrid solver (V, W, F and K cycle) and example #542

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Amgx parallel graph match reference and fill_seq_array #528

Amgx parallel graph match reference and fill_seq_array #528

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