Release Notes for 1.5.0

[tcojean]

Release 1.5.0 of Ginkgo.

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 and the open issues list and if you do not find a solution, feel free to open a new issue or ask a question using the github 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, #908, #909, #932, #951, #961, #971, #976, #985, #1007, #1030, #1054, #1100, #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, #924, #928, #929, #933, #943, #960, #1057, #1110, #1142)
• Add a Sparse Direct interface supporting GPU-resident numerical LU factorization, symbolic Cholesky factorization, improved triangular solvers, and more (#957, #1058, #1072, #1082)
• Add a ScaleReordered interface that can wrap solvers and automatically apply reorderings and scalings (#1059)
• Add a Multigrid solver and improve the aggregation based PGM coarsening scheme (#542, #913, #980, #982, #986)
• Add infrastructure for unified, lambda-based, backend agnostic, kernels and utilize it for some simple kernels (#833, #910, #926)
• Merge different CUDA, HIP, DPC++ and OpenMP tests under a common interface (#904, #973, #1044, #1117)
• Add a device_matrix_data type for device-side matrix assembly (#886, #963, #965)
• Add support for mixed real/complex BLAS operations (#864)
• Add a FFT LinOp for all but DPC++/SYCL (#701)
• Add FBCSR support for NVIDIA and AMD GPUs and CPUs with OpenMP (#775)
• Add CSR scaling (#848)
• Add array::const_view and equivalent to create constant matrices from non-const data (#890)
• Add a RowGatherer LinOp supporting mixed precision to gather dense matrix rows (#901)
• Add mixed precision SparsityCsr SpMV support (#970)
• Allow creating CSR submatrix including from (possibly discontinuous) index sets (#885, #964)
• Add a scaled identity addition (M <- aI + bM) feature interface and impls for Csr and Dense (#942)

Deprecations and important changes:

• Deprecate AmgxPgm in favor of the new Pgm name. (#1149).
• Deprecate specialized residual norm classes in favor of a common ResidualNorm class (#1101)
• Deprecate CamelCase non-polymorphic types in favor of snake_case versions (like array, machine_topology, uninitialized_array, index_set) (#1031, #1052)
• Bug fix: restrict gko::share to rvalue references (possible interface break) (#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).
• Drop official support for old CUDA < 9.2 (#887)

Improved performance additions:

• Reuse tmp storage in reductions in solvers and add a mutable workspace to all solvers (#1013, #1028)
• Add HIP unsafe atomic option for AMD (#1091)
• Prefer vendor implementations for Dense dot, conj_dot and norm2 when available (#967).
• Tuned OpenMP SellP, COO, and ELL SpMV kernels for a small number of RHS (#809)

Fixes:

• Fix various compilation warnings (#1076, #1183, #1189)
• Fix issues with hwloc-related tests (#1074)
• Fix include headers for GCC 12 (#1071)
• Fix for simple-solver-logging example (#1066)
• Fix for potential memory leak in Logger (#1056)
• Fix logging of mixin classes (#1037)
• Improve value semantics for LinOp types, like moved-from state in cross-executor copy/clones (#753)
• Fix some matrix SpMV and conversion corner cases (#905, #978)
• Fix uninitialized data (#958)
• Fix CUDA version requirement for cusparseSpSM (#953)
• Fix several issues within bash-script (#1016)
• Fixes for NVHPC compiler support (#1194)

Other additions:

• Simplify and properly name GMRES kernels (#861)
• Improve pkg-config support for non-CMake libraries (#923, #1109)
• Improve gdb pretty printer (#987, #1114)
• Add a logger highlighting inefficient allocation and copy patterns (#1035)
• Improved and optimized test random matrix generation (#954, #1032)
• Better CSR strategy defaults (#969)
• Add move_from to PolymorphicObject (#997)
• Remove unnecessary device_guard usage (#956)
• Improvements to the generic accessor for mixed-precision (#727)
• Add a naive lower triangular solver implementation for CUDA (#764)
• Add support for int64 indices from CUDA 11 onward with SpMV and SpGEMM (#897)
• Add a L1 norm implementation (#900)
• Add reduce_add for arrays (#831)
• Add utility to simplify Dense View creation from an existing Dense vector (#1136).
• Add a custom transpose implementation for Fbcsr and Csr transpose for unsupported vendor types (#1123)
• Make IDR random initilization deterministic (#1116)
• Move the algorithm choice for triangular solvers from Csr::strategy_type to a factory parameter (#1088)
• Update CUDA archCoresPerSM (#1175)
• Add kernels for Csr sparsity pattern lookup (#994)
• Differentiate between structural and numerical zeros in Ell/Sellp (#1027)
• Add a binary IO format for matrix data (#984)
• Add a tuple zip_iterator implementation (#966)
• Simplify kernel stubs and declarations (#888)
• Simplify GKO_REGISTER_OPERATION with lambdas (#859)
• Simplify copy to device in tests and examples (#863)
• More verbose output to array assertions (#858)
• Allow parallel compilation for Jacobi kernels (#871)
• Change clang-format pointer alignment to left (#872)
• Various improvements and fixes to the benchmarking framework (#750, #759, #870, #911, #1033, #1137)
• Various documentation improvements (#892, #921, #950, #977, #1021, #1068, #1069, #1080, #1081, #1108, #1153, #1154)
• Various CI improvements (#868, #874, #884, #889, #899, #903, #922, #925, #930, #936, #937, #958, #882, #1011, #1015, #989, #1039, #1042, #1067, #1073, #1075, #1083, #1084, #1085, #1139, #1178, #1187)

[hartwiganzt]

The first paragraph is hard to digest (grammatically) How about:

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,
• a ScaleReordered interface,
• a new algebraic Multigrid solver,
• improved mixed-precision support,
• support for device matrix assembly,

and much more.

[tcojean]

I integrated this, thanks!

[pratikvn]

Should we say something about the nvhpc compiler suite ? I think in general, IIRC, we had openmp issues, but other things mostly worked

[tcojean]

I am trying out both Cray and nvhpc on Perlmutter.

[tcojean]

Locking with the final version.