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FGMRES #1244

Merged
merged 2 commits into from
Feb 8, 2023
Merged

FGMRES #1244

merged 2 commits into from
Feb 8, 2023

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yhmtsai
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@yhmtsai yhmtsai commented Dec 15, 2022 •
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This PR adds the FGMRES. I am not sure whether to create FGMRES like FCG
reference https://www-users.cse.umn.edu/~saad/Calais/PREC.pdf and the paper A Flexible Inner-Outer Preconditioned GMRES Algorithm
TODO:

  • create FGMRES? (using with_flexible(true))
  • avoid the unnecessary allocation (partially, it only avoids the allocation of flexible in normal. It may be possible to reduce some allocation in flexible, too)

@ginkgo-bot ginkgo-bot added mod:core This is related to the core module. type:solver This is related to the solvers labels Dec 15, 2022
upsj
upsj approved these changes Dec 16, 2022
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Really nice work, I didn't consider we can just add this to GMRES instead of adding an entirely different solver. Related, should we at some point merge FCG and CG the same way?

Is there an easy way to construct an example where variable-preconditioned GMRES breaks down?

upsj
upsj reviewed Dec 16, 2022
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yhmtsai commented Feb 8, 2023

@upsj it avoids some additional allocation from FGMRES in GMRES now.

@yhmtsai yhmtsai self-assigned this Feb 8, 2023
@yhmtsai yhmtsai added the 1:ST:ready-for-review This PR is ready for review label Feb 8, 2023
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upsj commented Feb 8, 2023

Awesome, thanks for the update!

@yhmtsai yhmtsai 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 Feb 8, 2023
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yhmtsai commented Feb 8, 2023

rebase!

yhmtsai and others added 2 commits February 8, 2023 13:22
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fgmres
52f41bf
@yhmtsai @upsj
reduce create_submatri, avoid unused allocation
a3b9699 
Co-authored-by: Tobias Ribizel <ribizel@kit.edu>
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Note: This PR changes the Ginkgo ABI:

Functions changes summary: 0 Removed, 16 Changed (500 filtered out), 4 Added functions
Variables changes summary: 0 Removed, 0 Changed, 4 Added variables

For details check the full ABI diff under Artifacts here

@yhmtsai yhmtsai merged commit bcf212e into develop Feb 8, 2023
@yhmtsai yhmtsai deleted the fgmres branch February 8, 2023 21:46
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sonarcloud bot commented Feb 8, 2023

Kudos, SonarCloud Quality Gate passed!    Quality Gate passed

Bug A 0 Bugs
Vulnerability A 0 Vulnerabilities
Security Hotspot A 0 Security Hotspots
Code Smell A 4 Code Smells

41.0% 41.0% Coverage
1.1% 1.1% Duplication

tcojean added a commit that referenced this pull request Jun 16, 2023
@tcojean
Merge Release 1.6.0
1f1ed46 
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)
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@upsj upsj upsj approved these changes

@MarcelKoch MarcelKoch MarcelKoch approved these changes

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