Add symbolic LU and Cholesky benchmark #1302

upsj · 2023-03-17T12:38:22Z

Together with a generic validation tool for factorizations.
Also removes the strategy parameter (we don't use strategies for sparse_blas algorithms), renames validate_results to validate and allows operations to output their own custom stats (in this case for the amount of fill-in).
Finally, I added prefix_sum benchmarks to BLAS, which highlighted a horrible performance on CUDA. ~~replacing them by Thrust gives us at least 100x speedup even with the overflow check.~~ See #1303

MarcelKoch

LGTM, only two smaller comments.

benchmark/sparse_blas/operations.cpp

codecov · 2023-03-17T22:11:49Z

Codecov Report

Patch coverage has no change and project coverage change: -0.48 ⚠️

Comparison is base (8b4c322) 91.20% compared to head (ccc569b) 90.73%.

❗ Current head ccc569b differs from pull request most recent head fe39890. Consider uploading reports for the commit fe39890 to get more accurate results

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@@             Coverage Diff             @@
##           develop    #1302      +/-   ##
===========================================
- Coverage    91.20%   90.73%   -0.48%     
===========================================
  Files          570      570              
  Lines        48572    48631      +59     
===========================================
- Hits         44301    44125     -176     
- Misses        4271     4506     +235

see 7 files with indirect coverage changes

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stale, added prefix_sum overflow check

upsj · 2023-03-18T12:12:44Z

format-rebase!

ginkgo-bot · 2023-03-18T12:13:49Z

Formatting rebase introduced changes, see Artifacts here to review them

yhmtsai

suggest to move the prefix sum related changes to another pr

cuda/components/prefix_sum_kernels.cu

hip/components/prefix_sum_kernels.hip.cpp

omp/components/prefix_sum_kernels.cpp

reference/components/prefix_sum_kernels.cpp

reference/test/components/prefix_sum_kernels.cpp

yhmtsai · 2023-03-20T09:16:25Z

benchmark/sparse_blas/operations.cpp

+
+    gko::size_type get_flops() const override { return 0; }
+
+    gko::size_type get_memory() const override { return 0; }


could we get the memory?

doesn't make much sense to compute a bandwidth here, since we use both CPU and GPU for different parts of the algorithm

stale

MarcelKoch

LGTM

benchmark/blas/blas_common.hpp

upsj · 2023-03-21T10:07:26Z

rebase!

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

tcojean

LGTM

sonarcloud · 2023-03-21T19:16:43Z

Kudos, SonarCloud Quality Gate passed!

0 Bugs
0 Vulnerabilities
0 Security Hotspots
14 Code Smells

0.0% Coverage
0.0% Duplication

Release 1.6.0 of Ginkgo. The Ginkgo team is proud to announce the new Ginkgo minor release 1.6.0. This release brings new features such as: - Several building blocks for GPU-resident sparse direct solvers like symbolic and numerical LU and Cholesky factorization, ..., - A distributed Schwarz preconditioner, - New FGMRES and GCR solvers, - Distributed benchmarks for the SpMV operation, solvers, ... - Support for non-default streams in the CUDA and HIP backends, - Mixed precision support for the CSR SpMV, - A new profiling logger which integrates with NVTX, ROCTX, TAU and VTune to provide internal Ginkgo knowledge to most HPC profilers! 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 Clang: 14.0 is tested. Earlier versions might also work. + NVHPC: 22.7+ + Cray Compiler: 14.0.1+ + CUDA module: CUDA 9.2+ or NVHPC 22.7+ + HIP module: ROCm 4.5+ + DPC++ module: Intel OneAPI 2021.3+ with oneMKL and oneDPL. Set the CXX compiler to `dpcpp`. + Windows + MinGW: GCC 5.5+ + Microsoft Visual Studio: VS 2019+ + CUDA module: CUDA 9.2+, Microsoft Visual Studio + OpenMP module: MinGW. ### Version Support Changes + ROCm 4.0+ -> 4.5+ after [#1303](#1303) + Removed Cygwin pipeline and support [#1283](#1283) ### Interface Changes + Due to internal changes, `ConcreteExecutor::run` will now always throw if the corresponding module for the `ConcreteExecutor` is not build [#1234](#1234) + The constructor of `experimental::distributed::Vector` was changed to only accept local vectors as `std::unique_ptr` [#1284](#1284) + The default parameters for the `solver::MultiGrid` were improved. In particular, the smoother defaults to one iteration of `Ir` with `Jacobi` preconditioner, and the coarse grid solver uses the new direct solver with LU factorization. [#1291](#1291) [#1327](#1327) + The `iteration_complete` event gained a more expressive overload with additional parameters, the old overloads were deprecated. [#1288](#1288) [#1327](#1327) ### Deprecations + Deprecated less expressive `iteration_complete` event. Users are advised to now implement the function `void iteration_complete(const LinOp* solver, const LinOp* b, const LinOp* x, const size_type& it, const LinOp* r, const LinOp* tau, const LinOp* implicit_tau_sq, const array<stopping_status>* status, bool stopped)` [#1288](#1288) ### Added Features + A distributed Schwarz preconditioner. [#1248](#1248) + A GCR solver [#1239](#1239) + Flexible Gmres solver [#1244](#1244) + Enable Gmres solver for distributed matrices and vectors [#1201](#1201) + An example that uses Kokkos to assemble the system matrix [#1216](#1216) + A symbolic LU factorization allowing the `gko::experimental::factorization::Lu` and `gko::experimental::solver::Direct` classes to be used for matrices with non-symmetric sparsity pattern [#1210](#1210) + A numerical Cholesky factorization [#1215](#1215) + Symbolic factorizations in host-side operations are now wrapped in a host-side `Operation` to make their execution visible to loggers. This means that profiling loggers and benchmarks are no longer missing a separate entry for their runtime [#1232](#1232) + Symbolic factorization benchmark [#1302](#1302) + The `ProfilerHook` logger allows annotating the Ginkgo execution (apply, operations, ...) for profiling frameworks like NVTX, ROCTX and TAU. [#1055](#1055) + `ProfilerHook::created_(nested_)summary` allows the generation of a lightweight runtime profile over all Ginkgo functions written to a user-defined stream [#1270](#1270) for both host and device timing functionality [#1313](#1313) + It is now possible to enable host buffers for MPI communications at runtime even if the compile option `GINKGO_FORCE_GPU_AWARE_MPI` is set. [#1228](#1228) + A stencil matrices generator (5-pt, 7-pt, 9-pt, and 27-pt) for benchmarks [#1204](#1204) + Distributed benchmarks (multi-vector blas, SpMV, solver) [#1204](#1204) + Benchmarks for CSR sorting and lookup [#1219](#1219) + A timer for MPI benchmarks that reports the longest time [#1217](#1217) + A `timer_method=min|max|average|median` flag for benchmark timing summary [#1294](#1294) + Support for non-default streams in CUDA and HIP executors [#1236](#1236) + METIS integration for nested dissection reordering [#1296](#1296) + SuiteSparse AMD integration for fillin-reducing reordering [#1328](#1328) + Csr mixed-precision SpMV support [#1319](#1319) + A `with_loggers` function for all `Factory` parameters [#1337](#1337) ### Improvements + Improve naming of kernel operations for loggers [#1277](#1277) + Annotate solver iterations in `ProfilerHook` [#1290](#1290) + Allow using the profiler hooks and inline input strings in benchmarks [#1342](#1342) + Allow passing smart pointers in place of raw pointers to most matrix functions. This means that things like `vec->compute_norm2(x.get())` or `vec->compute_norm2(lend(x))` can be simplified to `vec->compute_norm2(x)` [#1279](#1279) [#1261](#1261) + Catch overflows in prefix sum operations, which makes Ginkgo's operations much less likely to crash. This also improves the performance of the prefix sum kernel [#1303](#1303) + Make the installed GinkgoConfig.cmake file relocatable and follow more best practices [#1325](#1325) ### Fixes + Fix OpenMPI version check [#1200](#1200) + Fix the mpi cxx type binding by c binding [#1306](#1306) + Fix runtime failures for one-sided MPI wrapper functions observed on some OpenMPI versions [#1249](#1249) + Disable thread pinning with GPU executors due to poor performance [#1230](#1230) + Fix hwloc version detection [#1266](#1266) + Fix PAPI detection in non-implicit include directories [#1268](#1268) + Fix PAPI support for newer PAPI versions: [#1321](#1321) + Fix pkg-config file generation for library paths outside prefix [#1271](#1271) + Fix various build failures with ROCm 5.4, CUDA 12, and OneAPI 6 [#1214](#1214), [#1235](#1235), [#1251](#1251) + Fix incorrect read for skew-symmetric MatrixMarket files with explicit diagonal entries [#1272](#1272) + Fix handling of missing diagonal entries in symbolic factorizations [#1263](#1263) + Fix segmentation fault in benchmark matrix construction [#1299](#1299) + Fix the stencil matrix creation for benchmarking [#1305](#1305) + Fix the additional residual check in IR [#1307](#1307) + Fix the cuSPARSE CSR SpMM issue on single strided vector when cuda >= 11.6 [#1322](#1322) [#1331](#1331) + Fix Isai generation for large sparsity powers [#1327](#1327) + Fix Ginkgo compilation and test with NVHPC >= 22.7 [#1331](#1331) + Fix Ginkgo compilation of 32 bit binaries with MSVC [#1349](#1349)

upsj added the 1:ST:ready-for-review This PR is ready for review label Mar 17, 2023

upsj requested a review from a team March 17, 2023 12:38

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Guard prefix_sum overflows and fix performance issues #1303

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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 Mar 21, 2023

upsj and others added 5 commits March 21, 2023 10:07

add symbolic LU and Cholesky benchmark

ab52c19

review updates

fb5f33a

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

keep benchmarking errors by default

2057aa2

add size information to output

5c4f6fe

output fill-in in symbolic factorization benchmark

b492d04

keep IO errors in benchmarks

fe39890

ginkgo-bot force-pushed the symbolic_cholesky_benchmark branch from b57c30e to fe39890 Compare March 21, 2023 10:07

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upsj merged commit b176c0b into develop Mar 21, 2023

upsj deleted the symbolic_cholesky_benchmark branch March 21, 2023 16:44

upsj mentioned this pull request Mar 23, 2023

Avoid transpose in symbolic Cholesky benchmark #1312

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Add symbolic LU and Cholesky benchmark #1302

Add symbolic LU and Cholesky benchmark #1302

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		gko::size_type get_flops() const override { return 0; }

		gko::size_type get_memory() const override { return 0; }

Add symbolic LU and Cholesky benchmark #1302

Add symbolic LU and Cholesky benchmark #1302

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