Add a Ell batched matrix format, kernels and tests #1416

pratikvn · 2023-10-07T22:16:26Z

This PR adds a batched ELL matrix format, kernels and test. In a similar fashion to batch::matrix::Dense, it only applies to batch::MultiVector for now and has only the minimal required functionality.

TODO

Add CUDA kernels and tests
Add HIP kernels and tests
Add DPCPP kernels and tests
Update docs

pratikvn · 2023-10-08T10:15:30Z

format!

pratikvn · 2023-10-08T10:49:24Z

format!

MarcelKoch

I think the discrepancy between high-level and kernel-level representation of Ell needs to be addressed.

Regarding the tests, lot of constructor test seem to just test gko::batch::initialize. Maybe that could be combined with the tests from dense and only test the ell-specific stuff.

include/ginkgo/core/matrix/batch_ell.hpp

core/matrix/batch_ell.cpp

core/test/utils/batch_helpers.hpp

common/cuda_hip/matrix/batch_ell_kernels.hpp.inc

core/matrix/batch_struct.hpp

reference/test/matrix/batch_ell_kernels.cpp

common/cuda_hip/matrix/batch_ell_kernels.hpp.inc

yhmtsai · 2023-10-11T08:58:16Z

core/device_hooks/common_kernels.inc.cpp

+#define GKO_STUB_VALUE_AND_INT32_TYPE(_macro)                       \
+    template <typename ValueType, typename IndexType>               \
+    _macro(ValueType, IndexType) GKO_NOT_COMPILED(GKO_HOOK_MODULE); \
+    GKO_INSTANTIATE_FOR_EACH_VALUE_AND_INT32_TYPE(_macro)


does it still need to be templated when IndexType is only for int32?

I think it is good to keep the public interface more general and instantiate later for int64 or other int variants if necessary.

I think 64 bit indices is very unrealistic. 2^31 rows easily saturate an entire GPU (for the forseeable future), all the advantages of batching (fast local memory, fast synchronization, load balancing via oversubscription) don't seem to apply.

But it might make sense to use int16, int8 etc. I think keeping the option open makes sense. We instantiate only for int32, so we dont have any downsides such as larger library objects etc, but the public interface remains extensible.

I think it is very useful to have all batch objects interpretable as sets of regular Ginkgo objects via views. Adding more index types would break that, since these index types don't make sense for normal linear problems. It may be useful to use small indices internally, but I don't think we should expose it in the interface.

You are implying you will add another IndexType in the future. That would break the aforementioned interoperability with matrix::Ell

I think if we add another IndexType in the future, we would add it to matrix::Ell as well.

Which other index type do you have in mind? int16 doesn't make sense for the scales we are looking at in Ginkgo, int128 is way beyond even future system capabilities.

int16 definitely makes sense for batched atleast. To ensure interoperability with batched, we can instantiate non-batched for int16 as well.

Adding another index type is a huge increase in build time in Ginkgo, and int16 just doesn't make sense for the scales of sparse linear algebra we look at. If you want to use 16 bit indexing inside a solver kernel, a simple solution is to convert the indices on-the-fly in temporary memory - the sparsity pattern is tiny compared to the rest.

core/matrix/batch_struct.hpp

reference/matrix/batch_ell_kernels.hpp.inc

core/base/batch_utilities.hpp

Co-authored-by: Aditya Kashi <[email protected]>

Co-authored-by: Phuong Nguyen <[email protected]>

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

Co-authored-by: Marcel Koch <[email protected]> Co-authored-by: Yu-Hsiang Tsai <[email protected]>

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

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

Co-authored-by: Marcel Koch <[email protected]> Co-authored-by: Yu-Hsiang Tsai <[email protected]>

Co-authored-by: Yu-Hsiang Tsai <[email protected]> Co-authored-by: Marcel Koch <[email protected]>

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

Co-authored-by: Marcel Koch <[email protected]>

ginkgo-bot · 2023-10-17T13:43:05Z

Error: PR already merged!

sonarqubecloud · 2023-10-18T02:02:06Z

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Release 1.7.0 to master The Ginkgo team is proud to announce the new Ginkgo minor release 1.7.0. This release brings new features such as: - Complete GPU-resident sparse direct solvers feature set and interfaces, - Improved Cholesky factorization performance, - A new MC64 reordering, - Batched iterative solver support with the BiCGSTAB solver with batched Dense and ELL matrix types, - MPI support for the SYCL backend, - Improved ParILU(T)/ParIC(T) preconditioner convergence, and more! If you face an issue, please first check our [known issues page](https://github.com/ginkgo-project/ginkgo/wiki/Known-Issues) and the [open issues list](https://github.com/ginkgo-project/ginkgo/issues) and if you do not find a solution, feel free to [open a new issue](https://github.com/ginkgo-project/ginkgo/issues/new/choose) or ask a question using the [github discussions](https://github.com/ginkgo-project/ginkgo/discussions). Supported systems and requirements: + For all platforms, CMake 3.16+ + C++14 compliant compiler + Linux and macOS + GCC: 5.5+ + clang: 3.9+ + Intel compiler: 2019+ + Apple Clang: 14.0 is tested. Earlier versions might also work. + NVHPC: 22.7+ + Cray Compiler: 14.0.1+ + CUDA module: CMake 3.18+, and CUDA 10.1+ or NVHPC 22.7+ + HIP module: ROCm 4.5+ + DPC++ module: Intel oneAPI 2022.1+ with oneMKL and oneDPL. Set the CXX compiler to `dpcpp` or `icpx`. + MPI: standard version 3.1+, ideally GPU Aware, for best performance + Windows + MinGW: GCC 5.5+ + Microsoft Visual Studio: VS 2019+ + CUDA module: CUDA 10.1+, Microsoft Visual Studio + OpenMP module: MinGW. ### Version support changes + CUDA 9.2 is no longer supported and 10.0 is untested [#1382](#1382) + Ginkgo now requires CMake version 3.16 (and 3.18 for CUDA) [#1368](#1368) ### Interface changes + `const` Factory parameters can no longer be modified through `with_*` functions, as this breaks const-correctness [#1336](#1336) [#1439](#1439) ### New Deprecations + The `device_reset` parameter of CUDA and HIP executors no longer has an effect, and its `allocation_mode` parameters have been deprecated in favor of the `Allocator` interface. [#1315](#1315) + The CMake parameter `GINKGO_BUILD_DPCPP` has been deprecated in favor of `GINKGO_BUILD_SYCL`. [#1350](#1350) + The `gko::reorder::Rcm` interface has been deprecated in favor of `gko::experimental::reorder::Rcm` based on `Permutation`. [#1418](#1418) + The Permutation class' `permute_mask` functionality. [#1415](#1415) + Multiple functions with typos (`set_complex_subpsace()`, range functions such as `conj_operaton` etc). [#1348](#1348) ### Summary of previous deprecations + `gko::lend()` is not necessary anymore. + The classes `RelativeResidualNorm` and `AbsoluteResidualNorm` are deprecated in favor of `ResidualNorm`. + The class `AmgxPgm` is deprecated in favor of `Pgm`. + Default constructors for the CSR `load_balance` and `automatical` strategies + The PolymorphicObject's move-semantic `copy_from` variant + The templated `SolverBase` class. + The class `MachineTopology` is deprecated in favor of `machine_topology`. + Logger constructors and create functions with the `executor` parameter. + The virtual, protected, Dense functions `compute_norm1_impl`, `add_scaled_impl`, etc. + Logger events for solvers and criterion without the additional `implicit_tau_sq` parameter. + The global `gko::solver::default_krylov_dim`, use instead `gko::solver::gmres_default_krylov_dim`. ### Added features + Adds a batch::BatchLinOp class that forms a base class for batched linear operators such as batched matrix formats, solver and preconditioners [#1379](#1379) + Adds a batch::MultiVector class that enables operations such as dot, norm, scale on batched vectors [#1371](#1371) + Adds a batch::Dense matrix format that stores batched dense matrices and provides gemv operations for these dense matrices. [#1413](#1413) + Adds a batch::Ell matrix format that stores batched Ell matrices and provides spmv operations for these batched Ell matrices. [#1416](#1416) [#1437](#1437) + Add a batch::Bicgstab solver (class, core, and reference kernels) that enables iterative solution of batched linear systems [#1438](#1438). + Add device kernels (CUDA, HIP, and DPCPP) for batch::Bicgstab solver. [#1443](#1443). + New MC64 reordering algorithm which optimizes the diagonal product or sum of a matrix by permuting the rows, and computes additional scaling factors for equilibriation [#1120](#1120) + New interface for (non-symmetric) permutation and scaled permutation of Dense and Csr matrices [#1415](#1415) + LU and Cholesky Factorizations can now be separated into their factors [#1432](#1432) + New symbolic LU factorization algorithm that is optimized for matrices with an almost-symmetric sparsity pattern [#1445](#1445) + Sorting kernels for SparsityCsr on all backends [#1343](#1343) + Allow passing pre-generated local solver as factory parameter for the distributed Schwarz preconditioner [#1426](#1426) + Add DPCPP kernels for Partition [#1034](#1034), and CSR's `check_diagonal_entries` and `add_scaled_identity` functionality [#1436](#1436) + Adds a helper function to create a partition based on either local sizes, or local ranges [#1227](#1227) + Add function to compute arithmetic mean of dense and distributed vectors [#1275](#1275) + Adds `icpx` compiler supports [#1350](#1350) + All backends can be built simultaneously [#1333](#1333) + Emits a CMake warning in downstream projects that use different compilers than the installed Ginkgo [#1372](#1372) + Reordering algorithms in sparse_blas benchmark [#1354](#1354) + Benchmarks gained an `-allocator` parameter to specify device allocators [#1385](#1385) + Benchmarks gained an `-input_matrix` parameter that initializes the input JSON based on the filename [#1387](#1387) + Benchmark inputs can now be reordered as a preprocessing step [#1408](#1408) ### Improvements + Significantly improve Cholesky factorization performance [#1366](#1366) + Improve parallel build performance [#1378](#1378) + Allow constrained parallel test execution using CTest resources [#1373](#1373) + Use arithmetic type more inside mixed precision ELL [#1414](#1414) + Most factory parameters of factory type no longer need to be constructed explicitly via `.on(exec)` [#1336](#1336) [#1439](#1439) + Improve ParILU(T)/ParIC(T) convergence by using more appropriate atomic operations [#1434](#1434) ### Fixes + Fix an over-allocation for OpenMP reductions [#1369](#1369) + Fix DPCPP's common-kernel reduction for empty input sizes [#1362](#1362) + Fix several typos in the API and documentation [#1348](#1348) + Fix inconsistent `Threads` between generations [#1388](#1388) + Fix benchmark median condition [#1398](#1398) + Fix HIP 5.6.0 compilation [#1411](#1411) + Fix missing destruction of rand_generator from cuda/hip [#1417](#1417) + Fix PAPI logger destruction order [#1419](#1419) + Fix TAU logger compilation [#1422](#1422) + Fix relative criterion to not iterate if the residual is already zero [#1079](#1079) + Fix memory_order invocations with C++20 changes [#1402](#1402) + Fix `check_diagonal_entries_exist` report correctly when only missing diagonal value in the last rows. [#1440](#1440) + Fix checking OpenMPI version in cross-compilation settings [#1446](#1446) + Fix false-positive deprecation warnings in Ginkgo, especially for the old Rcm (it doesn't emit deprecation warnings anymore as a result but is still considered deprecated) [#1444](#1444) ### Related PR: #1451

Release 1.7.0 to develop The Ginkgo team is proud to announce the new Ginkgo minor release 1.7.0. This release brings new features such as: - Complete GPU-resident sparse direct solvers feature set and interfaces, - Improved Cholesky factorization performance, - A new MC64 reordering, - Batched iterative solver support with the BiCGSTAB solver with batched Dense and ELL matrix types, - MPI support for the SYCL backend, - Improved ParILU(T)/ParIC(T) preconditioner convergence, and more! If you face an issue, please first check our [known issues page](https://github.com/ginkgo-project/ginkgo/wiki/Known-Issues) and the [open issues list](https://github.com/ginkgo-project/ginkgo/issues) and if you do not find a solution, feel free to [open a new issue](https://github.com/ginkgo-project/ginkgo/issues/new/choose) or ask a question using the [github discussions](https://github.com/ginkgo-project/ginkgo/discussions). Supported systems and requirements: + For all platforms, CMake 3.16+ + C++14 compliant compiler + Linux and macOS + GCC: 5.5+ + clang: 3.9+ + Intel compiler: 2019+ + Apple Clang: 14.0 is tested. Earlier versions might also work. + NVHPC: 22.7+ + Cray Compiler: 14.0.1+ + CUDA module: CMake 3.18+, and CUDA 10.1+ or NVHPC 22.7+ + HIP module: ROCm 4.5+ + DPC++ module: Intel oneAPI 2022.1+ with oneMKL and oneDPL. Set the CXX compiler to `dpcpp` or `icpx`. + MPI: standard version 3.1+, ideally GPU Aware, for best performance + Windows + MinGW: GCC 5.5+ + Microsoft Visual Studio: VS 2019+ + CUDA module: CUDA 10.1+, Microsoft Visual Studio + OpenMP module: MinGW. ### Version support changes + CUDA 9.2 is no longer supported and 10.0 is untested [#1382](#1382) + Ginkgo now requires CMake version 3.16 (and 3.18 for CUDA) [#1368](#1368) ### Interface changes + `const` Factory parameters can no longer be modified through `with_*` functions, as this breaks const-correctness [#1336](#1336) [#1439](#1439) ### New Deprecations + The `device_reset` parameter of CUDA and HIP executors no longer has an effect, and its `allocation_mode` parameters have been deprecated in favor of the `Allocator` interface. [#1315](#1315) + The CMake parameter `GINKGO_BUILD_DPCPP` has been deprecated in favor of `GINKGO_BUILD_SYCL`. [#1350](#1350) + The `gko::reorder::Rcm` interface has been deprecated in favor of `gko::experimental::reorder::Rcm` based on `Permutation`. [#1418](#1418) + The Permutation class' `permute_mask` functionality. [#1415](#1415) + Multiple functions with typos (`set_complex_subpsace()`, range functions such as `conj_operaton` etc). [#1348](#1348) ### Summary of previous deprecations + `gko::lend()` is not necessary anymore. + The classes `RelativeResidualNorm` and `AbsoluteResidualNorm` are deprecated in favor of `ResidualNorm`. + The class `AmgxPgm` is deprecated in favor of `Pgm`. + Default constructors for the CSR `load_balance` and `automatical` strategies + The PolymorphicObject's move-semantic `copy_from` variant + The templated `SolverBase` class. + The class `MachineTopology` is deprecated in favor of `machine_topology`. + Logger constructors and create functions with the `executor` parameter. + The virtual, protected, Dense functions `compute_norm1_impl`, `add_scaled_impl`, etc. + Logger events for solvers and criterion without the additional `implicit_tau_sq` parameter. + The global `gko::solver::default_krylov_dim`, use instead `gko::solver::gmres_default_krylov_dim`. ### Added features + Adds a batch::BatchLinOp class that forms a base class for batched linear operators such as batched matrix formats, solver and preconditioners [#1379](#1379) + Adds a batch::MultiVector class that enables operations such as dot, norm, scale on batched vectors [#1371](#1371) + Adds a batch::Dense matrix format that stores batched dense matrices and provides gemv operations for these dense matrices. [#1413](#1413) + Adds a batch::Ell matrix format that stores batched Ell matrices and provides spmv operations for these batched Ell matrices. [#1416](#1416) [#1437](#1437) + Add a batch::Bicgstab solver (class, core, and reference kernels) that enables iterative solution of batched linear systems [#1438](#1438). + Add device kernels (CUDA, HIP, and DPCPP) for batch::Bicgstab solver. [#1443](#1443). + New MC64 reordering algorithm which optimizes the diagonal product or sum of a matrix by permuting the rows, and computes additional scaling factors for equilibriation [#1120](#1120) + New interface for (non-symmetric) permutation and scaled permutation of Dense and Csr matrices [#1415](#1415) + LU and Cholesky Factorizations can now be separated into their factors [#1432](#1432) + New symbolic LU factorization algorithm that is optimized for matrices with an almost-symmetric sparsity pattern [#1445](#1445) + Sorting kernels for SparsityCsr on all backends [#1343](#1343) + Allow passing pre-generated local solver as factory parameter for the distributed Schwarz preconditioner [#1426](#1426) + Add DPCPP kernels for Partition [#1034](#1034), and CSR's `check_diagonal_entries` and `add_scaled_identity` functionality [#1436](#1436) + Adds a helper function to create a partition based on either local sizes, or local ranges [#1227](#1227) + Add function to compute arithmetic mean of dense and distributed vectors [#1275](#1275) + Adds `icpx` compiler supports [#1350](#1350) + All backends can be built simultaneously [#1333](#1333) + Emits a CMake warning in downstream projects that use different compilers than the installed Ginkgo [#1372](#1372) + Reordering algorithms in sparse_blas benchmark [#1354](#1354) + Benchmarks gained an `-allocator` parameter to specify device allocators [#1385](#1385) + Benchmarks gained an `-input_matrix` parameter that initializes the input JSON based on the filename [#1387](#1387) + Benchmark inputs can now be reordered as a preprocessing step [#1408](#1408) ### Improvements + Significantly improve Cholesky factorization performance [#1366](#1366) + Improve parallel build performance [#1378](#1378) + Allow constrained parallel test execution using CTest resources [#1373](#1373) + Use arithmetic type more inside mixed precision ELL [#1414](#1414) + Most factory parameters of factory type no longer need to be constructed explicitly via `.on(exec)` [#1336](#1336) [#1439](#1439) + Improve ParILU(T)/ParIC(T) convergence by using more appropriate atomic operations [#1434](#1434) ### Fixes + Fix an over-allocation for OpenMP reductions [#1369](#1369) + Fix DPCPP's common-kernel reduction for empty input sizes [#1362](#1362) + Fix several typos in the API and documentation [#1348](#1348) + Fix inconsistent `Threads` between generations [#1388](#1388) + Fix benchmark median condition [#1398](#1398) + Fix HIP 5.6.0 compilation [#1411](#1411) + Fix missing destruction of rand_generator from cuda/hip [#1417](#1417) + Fix PAPI logger destruction order [#1419](#1419) + Fix TAU logger compilation [#1422](#1422) + Fix relative criterion to not iterate if the residual is already zero [#1079](#1079) + Fix memory_order invocations with C++20 changes [#1402](#1402) + Fix `check_diagonal_entries_exist` report correctly when only missing diagonal value in the last rows. [#1440](#1440) + Fix checking OpenMPI version in cross-compilation settings [#1446](#1446) + Fix false-positive deprecation warnings in Ginkgo, especially for the old Rcm (it doesn't emit deprecation warnings anymore as a result but is still considered deprecated) [#1444](#1444) ### Related PR: #1454

pratikvn added this to the Release 1.7.0 milestone Oct 7, 2023

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pratikvn changed the title ~~WIP: Add a Ell batched matrix format, kernels and tests~~ Add a Ell batched matrix format, kernels and tests Oct 8, 2023

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pratikvn force-pushed the new-batch-ell branch from 1496c41 to edc0e79 Compare October 17, 2023 08:14

pratikvn and others added 18 commits October 17, 2023 15:30

Add batch::matrix::Ell class and core

75ed67f

Co-authored-by: Aditya Kashi <[email protected]>

Add ref, omp kernels and scaffold

d3b8ad1

Co-authored-by: Aditya Kashi <[email protected]>

Use only int32

658a5af

Generalize and rewrite batch utils

1c3c5ff

Add OMP, CUDA, HIP kernels and tests

c5a14c0

Co-authored-by: Aditya Kashi <[email protected]>

Add DPCPP kernels and tests

174c3fd

Co-authored-by: Phuong Nguyen <[email protected]>

Update docs

f4168f9

Format files

7cb4c51

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

Some general fixes.

88b6e3a

Kernel updates and batch_random_matrix gen

b78c0cc

Review updates

4179654

Co-authored-by: Marcel Koch <[email protected]> Co-authored-by: Yu-Hsiang Tsai <[email protected]>

Add apply temp clone, review updates

6d16d3b

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

Format files

40741cb

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

Fix sparsity issues and review updates

b15308f

Co-authored-by: Marcel Koch <[email protected]> Co-authored-by: Yu-Hsiang Tsai <[email protected]>

vector mat data with duplication

4b1fbc1

Review updates

d231ca6

Co-authored-by: Yu-Hsiang Tsai <[email protected]> Co-authored-by: Marcel Koch <[email protected]>

Format files

48e94bb

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

Review updates

0949431

Co-authored-by: Marcel Koch <[email protected]>

pratikvn force-pushed the new-batch-ell branch from edc0e79 to 0949431 Compare October 17, 2023 13:30

pratikvn merged commit 0b5eaf8 into develop Oct 17, 2023
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pratikvn deleted the new-batch-ell branch October 17, 2023 13:32

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Add a Ell batched matrix format, kernels and tests #1416

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TODO

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