Fbcsr kernels for Cuda and OpenMP #775

Slaedr · 2021-05-25T22:32:10Z

Implementations of several kernels for the Fbcsr matrix type for the Cuda and OpenMP backends.

Synthesizer lists (compile-time lists) are used for specifying the list of block sizes to compile for, where applicable. Also, the precision dispatch capability is now used in core.

For now, several kernels for the Cuda backend just call a Cusparse wrapper.

codecov · 2021-06-23T01:54:42Z

Codecov Report

Merging #775 (3fa1b4e) into develop (2901950) will increase coverage by 0.07%.
The diff coverage is 96.37%.

@@             Coverage Diff             @@
##           develop     #775      +/-   ##
===========================================
+ Coverage    94.73%   94.80%   +0.07%     
===========================================
  Files          434      436       +2     
  Lines        35708    36008     +300     
===========================================
+ Hits         33827    34137     +310     
+ Misses        1881     1871      -10

Impacted Files	Coverage Δ
core/base/utils.hpp	`100.00% <ø> (ø)`
omp/base/kernel_launch.hpp	`84.61% <ø> (ø)`
omp/base/kernel_launch_reduction.hpp	`96.62% <ø> (ø)`
core/test/utils/fb_matrix_generator.hpp	`90.32% <90.32%> (ø)`
reference/matrix/fbcsr_kernels.cpp	`98.42% <90.90%> (+1.52%)`	⬆️
core/test/utils/fb_matrix_generator_test.cpp	`92.85% <92.85%> (ø)`
omp/matrix/fbcsr_kernels.cpp	`93.06% <96.55%> (+93.06%)`	⬆️
core/matrix/fbcsr.cpp	`97.25% <100.00%> (+0.01%)`	⬆️
omp/test/matrix/fbcsr_kernels.cpp	`100.00% <100.00%> (ø)`
reference/test/matrix/fbcsr_kernels.cpp	`100.00% <100.00%> (ø)`
... and 2 more

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sonarqubecloud · 2021-06-26T21:50:14Z

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sonarqubecloud · 2021-07-27T21:03:30Z

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MarcelKoch

lgtm in general, I've some minor suggestions below.

Also, I've some questions/remarks regarding the precompiled kernels for special block sizes:

Why exactly these sizes? I guess because they were used before, but I think now would be a good chance to see if they still make sense or if others/more should be used.
Perhaps a generic kernel could be added as a fall back if the current block size is not part of the precompiled ones.

(I've also added these remark in a comment somewhere)

omp/matrix/fbcsr_kernels.cpp

omp/test/matrix/fbcsr_kernels.cpp

reference/test/matrix/fbcsr_kernels.cpp

omp/matrix/fbcsr_kernels.cpp

core/matrix/fbcsr.cpp

cuda/matrix/fbcsr_kernels.cu

common/matrix/fbcsr_kernels.hpp.inc

cuda/base/cusparse_block_bindings.hpp

yhmtsai

I think the core/test/utils/fb_matrix... should be under core/test/matrix/fbcsr_matrix...?
should cusparse_block_binding.hpp be separate file agains cusparase_binding.hpp?

common/matrix/fbcsr_kernels.hpp.inc

yhmtsai · 2021-09-08T13:19:31Z

common/matrix/fbcsr_kernels.hpp.inc

+            origblocks[sw_id_in_threadblock * mat_blk_sz_2 + i] =
+                values[ibz * mat_blk_sz_2 + i];
+        }
+        subwarp_grp.sync();


should it be thread_block.sync()?
if the communication is only in subwarp, maybe use warp shuffle to get the data?
(it also depends on how many elements per subwarp)

The communication is only in the warp. Maybe I could use shuffles, but it's an in-place transpose so it would need some work, I guess. This is probably not that performance-critical, so I'll come back to this later if need be.

core/test/utils/fb_matrix_generator.hpp

cuda/test/matrix/fbcsr_kernels.cpp

reference/matrix/fbcsr_kernels.cpp

Slaedr · 2021-10-04T18:34:47Z

format!

Slaedr · 2021-10-05T21:52:47Z

@yhmtsai

core/test/utils/fb_matrix_generator.hpp will be used as a header by all backends, very similar to matrix_generator.hpp.
I thought cusparse_bindings is already quite long, and cusparse_block_bindings has the potential to be pretty long too. It might be better to just keep them separate, maybe? Do you think it's better to merge them?

yhmtsai

one potential gpu race condition, some nit on format, testing.

yhmtsai · 2021-10-13T13:25:21Z

common/cuda_hip/matrix/fbcsr_kernels.hpp.inc

+            values[ibz * mat_blk_sz_2 + i] =
+                origblocks[sw_id_in_threadblock * mat_blk_sz_2 + in_pos];
+        }


it needs additional sync after for. otherwise write may be before read in next loop.

Since threads within a subwarp will not diverge for this kernel, it should not be necessary. But I'll add it anyway, it's probably better that way.

Yes. but you put the sync after read and before write, so it should be the same sync after write before read.
I think the transpose index is reflect between both direction.
Is there bank conflict here?

Kernels can still diverge in execution, even if they all execute the same commands.
Also, for a kernel that reads data once and then writes it once in a permuted fashion, do we really need all of this additional code? Do we expect shared memory to help?

yhmtsai · 2021-10-13T13:29:39Z

core/base/block_sizes.hpp

+
+
+namespace gko {
+namespace fixedblock {


should it be under cuda/hip matrix/fb_csr there?
like jacobi generate stuff.

It will be used by all backends, so it cannot be cuda or hip. Further, any algorithm that uses static fixed-size blocks, like the ParBILU that I was working on, will also use this. So I decided to have a common fixedblock namespace for such common things.

I see, that makes sense.
Is ParBILU for blockCSR or different format?

At least initially, ParBILU will only be for Fbcsr.

yhmtsai · 2021-10-13T13:33:21Z

core/matrix/fbcsr.cpp

    if (auto b_fbcsr = dynamic_cast<const Fbcsr<ValueType, IndexType>*>(b)) {
        // if b is a FBCSR matrix, we need an SpGeMM
        GKO_NOT_SUPPORTED(b_fbcsr);
    } else {


precision_dispatch_real_complex also throw the error when input not dense, so this part is unncessary

But I don't want the spmv kernel to be called when b is an Fbcsr. Fbcsr is convertible to Dense, so I guess I need the check?

I think temporary_conversion is implemented by dynamic_cast. Maybe @upsj can correct me.
when all dynamic_cast<*dense>(fbcsr) are failed, it throws the error

yes, we check against the exact type, not ConvertibleTo

core/test/utils/fb_matrix_generator.hpp

yhmtsai · 2021-10-13T13:48:51Z

core/test/utils/fb_matrix_generator.hpp

+                        gko::test::detail::get_rand_value<ValueType>(
+                            off_diag_dist, rand_engine);
+                }
+                if (col_idxs[ibz] == ibrow) {


When it is not row diag dominated, you use norm_dist. Can it be the off_diag_dist, too?
if it can be, you can merge these two part together if (row_diag_dominant && ...)

Yes, you're right. I removed the norm_dist.

yhmtsai · 2021-10-13T14:06:03Z

cuda/matrix/fbcsr_kernels.cu

+        const auto nnzb = static_cast<IndexType>(a->get_num_stored_blocks());
+        const auto nrhs = static_cast<IndexType>(b->get_size()[1]);
+        assert(nrhs == c->get_size()[1]);
+        if (nrhs == 1) {


it can also be the condition with the line98

There is a bsrmm but it does not work properly. The one on line 98 is for supported value types, I'd rather keep them separate.

does bsrmm with transpose not work?

cuda/test/matrix/fbcsr_kernels.cpp

reference/test/matrix/fbcsr_kernels.cpp

omp/test/matrix/fbcsr_kernels.cpp

…bcsr test

cuda

Co-authored-by: Aditya <aditya.kashi@kit.edu>

Co-authored-by: Marcel Koch <marcel.koch@kit.edu>

Co-authored-by: Yu-hsiang Tsai <yu-hsiang.tsai@kit.edu>

- cusparse block trsm and ilu0 struct create functions now return unique_ptrs. Co-authored-by: Yu-Hsiang Tsai <yhmtsai@gmail.com> Co-authored-by: Thomas Grützmacher <thomas.gruetzmacher@kit.edu>

…h headers

…orted for cuda Fbcsr apply

- cusparse_block_bindings.hpp now includes cusparse_bindings.hpp for things like "not_implemented"

yhmtsai · 2021-10-25T08:49:07Z

@Slaedr about bank conflict, it can not avoid but maybe reduce the amount.
However, you use the register not shared memory now, so we do not need to consider it.

sonarqubecloud · 2021-10-28T17:33:01Z

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Advertise release 1.5.0 and last changes + Add changelog, + Update third party libraries + A small fix to a CMake file See PR: #1195 The Ginkgo team is proud to announce the new Ginkgo minor release 1.5.0. This release brings many important new features such as: - MPI-based multi-node support for all matrix formats and most solvers; - full DPC++/SYCL support, - functionality and interface for GPU-resident sparse direct solvers, - an interface for wrapping solvers with scaling and reordering applied, - a new algebraic Multigrid solver/preconditioner, - improved mixed-precision support, - support for device matrix assembly, and much 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 3.13+ + C++14 compliant compiler + Linux and macOS + GCC: 5.5+ + clang: 3.9+ + Intel compiler: 2018+ + Apple LLVM: 8.0+ + NVHPC: 22.7+ + Cray Compiler: 14.0.1+ + CUDA module: CUDA 9.2+ or NVHPC 22.7+ + HIP module: ROCm 4.0+ + DPC++ module: Intel OneAPI 2021.3 with oneMKL and oneDPL. Set the CXX compiler to `dpcpp`. + Windows + MinGW and Cygwin: GCC 5.5+ + Microsoft Visual Studio: VS 2019 + CUDA module: CUDA 9.2+, Microsoft Visual Studio + OpenMP module: MinGW or Cygwin. Algorithm and important feature additions: + Add MPI-based multi-node for all matrix formats and solvers (except GMRES and IDR). ([#676](#676), [#908](#908), [#909](#909), [#932](#932), [#951](#951), [#961](#961), [#971](#971), [#976](#976), [#985](#985), [#1007](#1007), [#1030](#1030), [#1054](#1054), [#1100](#1100), [#1148](#1148)) + Porting the remaining algorithms (preconditioners like ISAI, Jacobi, Multigrid, ParILU(T) and ParIC(T)) to DPC++/SYCL, update to SYCL 2020, and improve support and performance ([#896](#896), [#924](#924), [#928](#928), [#929](#929), [#933](#933), [#943](#943), [#960](#960), [#1057](#1057), [#1110](#1110), [#1142](#1142)) + Add a Sparse Direct interface supporting GPU-resident numerical LU factorization, symbolic Cholesky factorization, improved triangular solvers, and more ([#957](#957), [#1058](#1058), [#1072](#1072), [#1082](#1082)) + Add a ScaleReordered interface that can wrap solvers and automatically apply reorderings and scalings ([#1059](#1059)) + Add a Multigrid solver and improve the aggregation based PGM coarsening scheme ([#542](#542), [#913](#913), [#980](#980), [#982](#982), [#986](#986)) + Add infrastructure for unified, lambda-based, backend agnostic, kernels and utilize it for some simple kernels ([#833](#833), [#910](#910), [#926](#926)) + Merge different CUDA, HIP, DPC++ and OpenMP tests under a common interface ([#904](#904), [#973](#973), [#1044](#1044), [#1117](#1117)) + Add a device_matrix_data type for device-side matrix assembly ([#886](#886), [#963](#963), [#965](#965)) + Add support for mixed real/complex BLAS operations ([#864](#864)) + Add a FFT LinOp for all but DPC++/SYCL ([#701](#701)) + Add FBCSR support for NVIDIA and AMD GPUs and CPUs with OpenMP ([#775](#775)) + Add CSR scaling ([#848](#848)) + Add array::const_view and equivalent to create constant matrices from non-const data ([#890](#890)) + Add a RowGatherer LinOp supporting mixed precision to gather dense matrix rows ([#901](#901)) + Add mixed precision SparsityCsr SpMV support ([#970](#970)) + Allow creating CSR submatrix including from (possibly discontinuous) index sets ([#885](#885), [#964](#964)) + Add a scaled identity addition (M <- aI + bM) feature interface and impls for Csr and Dense ([#942](#942)) Deprecations and important changes: + Deprecate AmgxPgm in favor of the new Pgm name. ([#1149](#1149)). + Deprecate specialized residual norm classes in favor of a common `ResidualNorm` class ([#1101](#1101)) + Deprecate CamelCase non-polymorphic types in favor of snake_case versions (like array, machine_topology, uninitialized_array, index_set) ([#1031](#1031), [#1052](#1052)) + Bug fix: restrict gko::share to rvalue references (*possible interface break*) ([#1020](#1020)) + Bug fix: when using cuSPARSE's triangular solvers, specifying the factory parameter `num_rhs` is now required when solving for more than one right-hand side, otherwise an exception is thrown ([#1184](#1184)). + Drop official support for old CUDA < 9.2 ([#887](#887)) Improved performance additions: + Reuse tmp storage in reductions in solvers and add a mutable workspace to all solvers ([#1013](#1013), [#1028](#1028)) + Add HIP unsafe atomic option for AMD ([#1091](#1091)) + Prefer vendor implementations for Dense dot, conj_dot and norm2 when available ([#967](#967)). + Tuned OpenMP SellP, COO, and ELL SpMV kernels for a small number of RHS ([#809](#809)) Fixes: + Fix various compilation warnings ([#1076](#1076), [#1183](#1183), [#1189](#1189)) + Fix issues with hwloc-related tests ([#1074](#1074)) + Fix include headers for GCC 12 ([#1071](#1071)) + Fix for simple-solver-logging example ([#1066](#1066)) + Fix for potential memory leak in Logger ([#1056](#1056)) + Fix logging of mixin classes ([#1037](#1037)) + Improve value semantics for LinOp types, like moved-from state in cross-executor copy/clones ([#753](#753)) + Fix some matrix SpMV and conversion corner cases ([#905](#905), [#978](#978)) + Fix uninitialized data ([#958](#958)) + Fix CUDA version requirement for cusparseSpSM ([#953](#953)) + Fix several issues within bash-script ([#1016](#1016)) + Fixes for `NVHPC` compiler support ([#1194](#1194)) Other additions: + Simplify and properly name GMRES kernels ([#861](#861)) + Improve pkg-config support for non-CMake libraries ([#923](#923), [#1109](#1109)) + Improve gdb pretty printer ([#987](#987), [#1114](#1114)) + Add a logger highlighting inefficient allocation and copy patterns ([#1035](#1035)) + Improved and optimized test random matrix generation ([#954](#954), [#1032](#1032)) + Better CSR strategy defaults ([#969](#969)) + Add `move_from` to `PolymorphicObject` ([#997](#997)) + Remove unnecessary device_guard usage ([#956](#956)) + Improvements to the generic accessor for mixed-precision ([#727](#727)) + Add a naive lower triangular solver implementation for CUDA ([#764](#764)) + Add support for int64 indices from CUDA 11 onward with SpMV and SpGEMM ([#897](#897)) + Add a L1 norm implementation ([#900](#900)) + Add reduce_add for arrays ([#831](#831)) + Add utility to simplify Dense View creation from an existing Dense vector ([#1136](#1136)). + Add a custom transpose implementation for Fbcsr and Csr transpose for unsupported vendor types ([#1123](#1123)) + Make IDR random initilization deterministic ([#1116](#1116)) + Move the algorithm choice for triangular solvers from Csr::strategy_type to a factory parameter ([#1088](#1088)) + Update CUDA archCoresPerSM ([#1175](#1116)) + Add kernels for Csr sparsity pattern lookup ([#994](#994)) + Differentiate between structural and numerical zeros in Ell/Sellp ([#1027](#1027)) + Add a binary IO format for matrix data ([#984](#984)) + Add a tuple zip_iterator implementation ([#966](#966)) + Simplify kernel stubs and declarations ([#888](#888)) + Simplify GKO_REGISTER_OPERATION with lambdas ([#859](#859)) + Simplify copy to device in tests and examples ([#863](#863)) + More verbose output to array assertions ([#858](#858)) + Allow parallel compilation for Jacobi kernels ([#871](#871)) + Change clang-format pointer alignment to left ([#872](#872)) + Various improvements and fixes to the benchmarking framework ([#750](#750), [#759](#759), [#870](#870), [#911](#911), [#1033](#1033), [#1137](#1137)) + Various documentation improvements ([#892](#892), [#921](#921), [#950](#950), [#977](#977), [#1021](#1021), [#1068](#1068), [#1069](#1069), [#1080](#1080), [#1081](#1081), [#1108](#1108), [#1153](#1153), [#1154](#1154)) + Various CI improvements ([#868](#868), [#874](#874), [#884](#884), [#889](#889), [#899](#899), [#903](#903), [#922](#922), [#925](#925), [#930](#930), [#936](#936), [#937](#937), [#958](#958), [#882](#882), [#1011](#1011), [#1015](#1015), [#989](#989), [#1039](#1039), [#1042](#1042), [#1067](#1067), [#1073](#1073), [#1075](#1075), [#1083](#1083), [#1084](#1084), [#1085](#1085), [#1139](#1139), [#1178](#1178), [#1187](#1187))

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Slaedr and others added 20 commits October 24, 2021 17:32

added spmv and advanced spmv to omp fbcsr

50d9207

enabled fbcsr cuda advanced apply and added all value types to cuda f…

92d3680

…bcsr test

syn value_list is used for compiling block sizes

d83cc17

reused conj-transpose and max_nnz_per_row kernels from csr for fbcsr in

5ec70fb

cuda

minor fix to omp sorting tests

60569fa

removed dependence of fb_matrix_generator on reference kernels

eff416a

added missing omp test

fd01a57

completed tests

bfc9634

rebased and fixed issues

3113d0f

Format files

037b4d4

Co-authored-by: Aditya <aditya.kashi@kit.edu>

Review comments, improved and fixed a bug in tests

854ca45

Co-authored-by: Marcel Koch <marcel.koch@kit.edu>

review comments

4b57f0c

Co-authored-by: Yu-hsiang Tsai <yu-hsiang.tsai@kit.edu>

fixed pointer mode issue in cuda fbcsr advanced apply

0c30369

Review comments

af36b2c

- cusparse block trsm and ilu0 struct create functions now return unique_ptrs. Co-authored-by: Yu-Hsiang Tsai <yhmtsai@gmail.com> Co-authored-by: Thomas Grützmacher <thomas.gruetzmacher@kit.edu>

added missing semicolons flagged by cuda 10.1 in generic kernel launc…

433fddd

…h headers

semicolon fix in hip common reduction kernels and minor whitespace fixes

88c064d

New algorithm for cuda block transpose, and multiple RHS are now supp…

010279a

…orted for cuda Fbcsr apply

fixed linker issues by removing core calls from fbcsr cuda kernels

3d9312d

- cusparse_block_bindings.hpp now includes cusparse_bindings.hpp for things like "not_implemented"

renamed block transpose functions

079c52c

fixed missing include

3fa1b4e

Slaedr force-pushed the fbcsr-cuda-omp branch from eb2fb0a to 3fa1b4e Compare October 24, 2021 17:18

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