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The Latest in Tpetra: Trilinos' Parallel Sparse Linear Algebra

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The Latest in Tpetra: Trilinos' Parallel Sparse Linear Algebra Photos placed in horizontal position with even amount of white space between photos The latest in Tpetra: Trilinos’ and header parallel sparse linear algebra Photos placed in horizontal position with even amount of white space Mark Hoemmen between photos and header Sandia National Laboratories 23 Apr 2019 SAND2019-4556 C (UUR) Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International, Inc. for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525. Tpetra: parallel sparse linear algebra § “Parallel”: MPI + Kokkos § Tpetra implements § Sparse graphs & matrices, & dense vecs § Parallel kernels for solving Ax=b & Ax=λx § MPI communication & (re)distribution § Tpetra supports many linear solvers § Key Tpetra features § Can manage > 2 billion (10^9) unknowns § Can pick the type of values: § Real, complex, extra precision § Automatic differentiation § Types for stochastic PDE discretizations 2 Over 15 years of Tpetra 2004-5: Stage 1 rewrite of Tpetra to use Paul 2005-10: Mid 2010: Assemble Deprecate & Sexton ill-tested I start Kokkos 2.0 new Tpetra purge for starts research- work on Kokkos team; gather Trilinos 13 Tpetra ware Tpetra 2.0 requirements 2005 2007 2009 2011 2013 2015 2017 2019 Trilinos 10.0 Trilinos 11.0 Trilinos 12.0 Trilinos 13.0 ??? Trilinos 9.0 2006 2008 2010 2012 2014 2016 2018 2020 2008: Chris Baker Fix bugs; rewrite Stage 2: purge Improve GPU+MPI redesigns Tpetra; kernels; deploy old interface, performance & remove 2009: Kokkos 1.0 Tpetra-based more kernels, sequential bottlenecks multigrid in app some thread- (ongoing) (no threads) parallel fill 3 Tpetra is a team now § Current team (2017-) § Plus contributions by § Karen Devine § Victor Brunini § Geoff Davidson § James Elliott § Tim Fuller § Si Hammond § Mark Hoemmen § Jonathan Hu § & code reviews by § Kyungjoo Kim many (thanks!) § Chris Luchini § William McLendon § Chris Siefert § Christian Trott 4 How does Tpetra use Kokkos? § Tpetra uses most of Kokkos § Data structures § View: 1-3 D, scalars & integers, device & host § KokkosSparse::CrsMatrix (developed 2013 for Tpetra) § DualView, StaticCrsGraph, UnorderedMap, UniqueToken § Algorithms: sort, random § Computational kernels (kokkos-kernels) § Dense BLAS & key sparse kernels § Kokkos-kernels started as part of Tpetra § Many parallel loops (for, reduce, & scan) § Set up sparse data structures; (un)pack MPI buffers § “Long tail” of thread parallelization 5 Tpetra performance improvements § GPU + MPI performance § SPARC linear solver communication 7x faster on multiple GPUs § Aria AFF test ~10x faster on 2 GPUs than ~1 year before § Highlights of some issues fixed § 4626: Avoid slow CUDA allocations in iterative linear solvers § Reuse allocation via static memory pool (eventually: write allocator?) § 4630: Make sure that local kernels use cuBLAS where possible § Aria AFF solve 2x faster overall; MultiVector X^T * Y time 23.7s to 1.2s § Made P100 np1 sliGhtly faster than Broadwell np36 for full nonlinear iteration (76s vs. 84.5s at the time) § 4417,4418: Avoid unneeded atomic updates in MultiVector unpack § Improved Tpetra solve times on multiple GPUs § Good idea for any parallel Kokkos back-end 6 GPUAria AFFUpdate performance AFF Performance Test Comparison 80 Results as of Sierra 4.51.8 (13 Apr 2019); 70 GPU solves about 10x slower before 60 50 40 Runtime (s) Runtime 30 20 10 0 Nonlinear Iteration Assemble Solve Radiosity Solve Broadwell np72 P100 + x86 np2 V100 + Power9 np2 7 23 Lessons learned § CUDA, UVM, & host-pinned View allocations are SLOW § 100-20,000x (1 ms!!!) slower than HostSpace View allocations § (HostSpace only ~ 5x slower than raw new / delete) § ~30x slower than pairwise MPI_I{recv,send} exchanges on node § è we already keep static allocations; considering memory pool § Overexposing implementation hinders optimization § If users can get Kokkos::DualView out of MultiVector, then MultiVector can’t lazily allocate device memory (see above) § MPI: “CUDA-aware” != fast § Most benefit: Careful measurement & tedious details § e.g., “Are you actually calling cuBLAS?” § Kokkos Profiling immensely helpful 8 Upcoming deprecations § LO, GO, & Node (3 of 4) template parameters § Simplify Trilinos’ build logic § Improve build times & sizes § DynamicProfile (in favor of StaticProfile) § {E,T}petra’s sparse graphs & matrices had 3 options historically: § DynamicProfile: Expand storage per row on demand, unboundedly § StaticProfile: Strict upper bound on # entries / row, at constructor § Const graph: No structure changes post construction § We will remove DynamicProfile § Reduce code complexity (inherited from Epetra) § Remove impediments to thread parallelization § CrsGraph Export / Import will resize as needed 9 Tpetra’s next steps § GPU performance improvements § Minimize device-host data movement § Minimize device & host-pinned memory allocations § Ensure correctness w/ CUDA_LAUNCH_BLOCKING off § Improve asynchrony & latency hiding § Interface to let users exploit CUDA streams § Overlap MPI communication with other work § Simplify access to Tpetra objects’ local data § Easy parallel iteration over all (rows, entries, etc.) § Scope-based model of accessing local data 10 Kokkos wish list § Finish execution space instance (CUDA stream) support § See Christian’s intro talk this morning § Interfaces often exist in Kokkos, but ignore stream argument § Improve build times & sizes § 2-10 (!) hours for all of Trilinos § Decade-long running battle § Help w/ trade-offs: When is it worth… § … sync’ing data to device? (CUDA) § … launching parallel kernel? (CUDA, OpenMP) § Is this an abstraction on top of Kokkos? § Team sort 11 Thanks! 12 Extra slides 13 Obstacles § Finding & making time to measure performance § Increasing pressure for solver features § Fragile build & multiplying configurations § Trilinos & application build times 14.
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