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Enable mixed CUDA/ROCm benchmarks #911

Merged
merged 2 commits into from
Nov 4, 2021
Merged

Enable mixed CUDA/ROCm benchmarks #911

merged 2 commits into from
Nov 4, 2021

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upsj
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@upsj upsj commented Oct 25, 2021

This PR moves the CUDA/HIP benchmark linops and timers into separate libraries, enabling their simultaneous compilation.

@upsj upsj added the 1:ST:ready-for-review This PR is ready for review label Oct 25, 2021
@upsj upsj self-assigned this Oct 25, 2021
@upsj upsj requested a review from a team October 25, 2021 10:03
yhmtsai
yhmtsai approved these changes Oct 25, 2021
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In general, LGTM. There are some comments.
Should we have a test for compiling them at the same time?
It's cool but I wouldn't like to give guarantee on this situation in my opinion.

benchmark/utils/timer.hpp Outdated Show resolved Hide resolved
benchmark/CMakeLists.txt
Comment on lines +59 to +62
if (GINKGO_BUILD_CUDA)
target_link_libraries("${name}" cuda_timer)
endif()
if (GINKGO_BUILD_HIP)
target_link_libraries("${name}" hip_timer)
endif()
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After this change the benchmark will not have HipTimer and CudaTimer declaration, right?
Only able to use them with Timer

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yes, the actual implementations are hidden, since they need CUDA/HIP-specific types as members.

benchmark/utils/formats.hpp Outdated Show resolved Hide resolved
benchmark/CMakeLists.txt Outdated Show resolved Hide resolved
benchmark/CMakeLists.txt
Comment on lines -90 to +94
"${name}" "${use_lib_linops}" "GKO_BENCHMARK_USE_DOUBLE_PRECISION" ${ARGN})
"${name}" "${use_lib_linops}" "GKO_BENCHMARK_USE_DOUBLE_PRECISION" "d" ${ARGN})
ginkgo_add_single_benchmark_executable(
"${name}_single" "${use_lib_linops}" "GKO_BENCHMARK_USE_SINGLE_PRECISION" ${ARGN})
"${name}_single" "${use_lib_linops}" "GKO_BENCHMARK_USE_SINGLE_PRECISION" "s" ${ARGN})
ginkgo_add_single_benchmark_executable(
"${name}_dcomplex" "${use_lib_linops}" "GKO_BENCHMARK_USE_DOUBLE_COMPLEX_PRECISION" ${ARGN})
"${name}_dcomplex" "${use_lib_linops}" "GKO_BENCHMARK_USE_DOUBLE_COMPLEX_PRECISION" "z" ${ARGN})
ginkgo_add_single_benchmark_executable(
"${name}_scomplex" "${use_lib_linops}" "GKO_BENCHMARK_USE_SINGLE_COMPLEX_PRECISION" ${ARGN})
"${name}_scomplex" "${use_lib_linops}" "GKO_BENCHMARK_USE_SINGLE_COMPLEX_PRECISION" "c" ${ARGN})
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Should we keep the same suffix for executable and library?
Use dszc or single, double, ...,

benchmark/utils/cuda_linops.cu Outdated Show resolved Hide resolved
benchmark/utils/cuda_linops.cu Outdated Show resolved Hide resolved
benchmark/utils/cuda_linops.cu Outdated Show resolved Hide resolved
@ginkgo-bot ginkgo-bot added reg:benchmarking This is related to benchmarking. reg:build This is related to the build system. labels Oct 25, 2021
tcojean
tcojean approved these changes Nov 3, 2021
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LGTM.

benchmark/utils/cuda_linops.cu Outdated Show resolved Hide resolved
benchmark/utils/types.hpp
defined(GKO_BENCHMARK_USE_SINGLE_PRECISION) || \
defined(GKO_BENCHMARK_USE_DOUBLE_COMPLEX_PRECISION) || \
defined(GKO_BENCHMARK_USE_SINGLE_COMPLEX_PRECISION)
// separate ifdefs to catch duplicate definitions
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Is this really needed? Anyway, it will be overwritten to one only. The only difference between this style and the other is whether we default to the first matching or the last, no? I would rather we use the previous style as it makes it clearer that only one should be set at once.

benchmark/utils/timer_impl.hpp
/**
* Timer stores the timing information
*/
class Timer {
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This file or at least this class can be kept in the main timer.hpp header I believe. It's not just impl, it is also interface-level as not only do we use this class to define specific timers, but we use the class functions in the benchmark code itself. We need to know that there is tic, toc functions, the getters, etc.

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I needed a header that does not include gflags, so I needed to separate the two parts.

benchmark/utils/sparselib_linops.hpp Show resolved Hide resolved
benchmark/utils/formats.hpp Outdated Show resolved Hide resolved
benchmark/utils/cuda_linops.cu Outdated Show resolved Hide resolved
benchmark/CMakeLists.txt Outdated
endfunction(ginkgo_add_typed_benchmark_executables)


if (GINKGO_BUILD_CUDA)
enable_language(CUDA)
ginkgo_benchmark_cusp_linops(d GKO_BENCHMARK_USE_DOUBLE_PRECISION)
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I'm not too much of a fan of the current setup for multiprecision in general, I believe it should be shortened. But that's probably better in another PR.

@upsj upsj force-pushed the benchmark_linops branch 3 times, most recently from 6b7bfab to 49f9c4e Compare November 3, 2021 20:34
@upsj upsj added 1:ST:ready-to-merge This PR is ready to merge. and removed 1:ST:ready-for-review This PR is ready for review labels Nov 3, 2021
@upsj @yhmtsai @tcojean
review updates
9e01183 
Co-authored-by: Yuhsiang Tsai <yhmtsai@gmail.com>
Co-authored-by: Terry Cojean <terry.cojean@kit.edu>
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sonarcloud bot commented Nov 4, 2021

SonarCloud Quality Gate failed.    Quality Gate failed

Bug A 0 Bugs
Vulnerability A 0 Vulnerabilities
Security Hotspot A 0 Security Hotspots
Code Smell A 8 Code Smells

0.0% 0.0% Coverage
10.4% 10.4% Duplication

@upsj upsj merged commit e0bb433 into develop Nov 4, 2021
@upsj upsj deleted the benchmark_linops branch November 4, 2021 01:43
tcojean added a commit that referenced this pull request Nov 12, 2022
@tcojean
Merge Release 1.5.0 to develop
7c9f530 
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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@yhmtsai yhmtsai yhmtsai approved these changes

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