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Add METIS integration #1296
Add METIS integration #1296
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format-rebase! |
Formatting rebase introduced changes, see Artifacts here to review them |
format-rebase! |
Formatting rebase introduced changes, see Artifacts here to review them |
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LGTM!
At some point, we should maybe think of having one interface for METIS along with ParMETIS, which has MPI support which might be useful for distributed matrix partitioning.
core/reorder/nested_dissection.cpp
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vector<idx_t> tmp_perm(num_rows, {host_exec}); | ||
vector<idx_t> tmp_iperm(num_rows, {host_exec}); | ||
auto result = METIS_NodeND(&nvtxs, tmp_row_ptrs.data(), tmp_col_idxs.data(), | ||
nullptr, const_cast<idx_t*>(options.data()), |
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Can we have the option of providing weights as well ? It might be interesting to see if we can provide weights here such a way that we minimize row interchanges when doing partial pivoting ?
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Just a random idea, not sure if it makes a lot of sense. :)
+1 for ParMETIS, but not in this PR. |
But I believe they also had a multi-threaded version of METIS, which might be useful to speedup the reordering on one node as well |
Codecov ReportPatch coverage:
Additional details and impacted files@@ Coverage Diff @@
## develop #1296 +/- ##
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+ Coverage 90.73% 91.34% +0.60%
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Files 570 575 +5
Lines 48631 48666 +35
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+ Hits 44125 44453 +328
+ Misses 4506 4213 -293
... and 7 files with indirect coverage changes Help us with your feedback. Take ten seconds to tell us how you rate us. Have a feature suggestion? Share it here. ☔ View full report in Codecov by Sentry. |
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the reference test and core test is missing.
Also, whether is NestedDissection only from METIS?
for (auto nz = begin; nz < end; nz++) { | ||
if (col_idxs[nz] == row) { | ||
count++; | ||
} | ||
} |
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doesn't repeated diagonal elements destroy sparsity CSR property? because the repeated elements does not have the same value as the others
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yes, I just wanted to make the algorithm robust to broken inputs. METIS crashes with diagonal elements, so we should avoid that at all costs 😆
TypenameNameGenerator); | ||
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TYPED_TEST(NestedDissection, ResultIsEquivalentToRef) |
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It tests with reference results, but the reference test is missing
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Building a ground-truth for METIS is pretty hard, since there are many orderings that may produce equivalent fill-in. What we could do is test that the permutation reduces Cholesky fill-in in a pathological case?
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do they provide some example matrix with the result?
if they have, we can use it to ensure we indeed run through METIS
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no, and graph partitioning has many degrees of freedom that produce equivalent results, which is why I used the simple star graph as an unambiguous example.
rebase! |
rebase! |
- fix failing tests - move to experimental
- output unknown METIS error IDs - fail when users provide invalid NUMBERING option - add core and reference tests Co-authored-by: Yuhsiang M. Tsai <[email protected]>
Co-authored-by: Yuhsiang M. Tsai <[email protected]>
Kudos, SonarCloud Quality Gate passed! |
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)
This adds a METIS CMake find module and a simple integration. It also serves as an example for how to use LinOpFactory to represent a reordering algorithm by creating Permutation matrix from a Csr matrix, which might be a potential future interface for reorderings.
I guess we also need to add METIS to a handful of containers to test this.Since METIS gets hiccups from diagonal entries, I added
sparsity_csr::remove_diagonal_elements
kernels for all backends.