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Add sort and lookup sparse BLAS benchmarks #1219

Merged
merged 6 commits into from
Feb 1, 2023
Merged

Add sort and lookup sparse BLAS benchmarks #1219

merged 6 commits into from
Feb 1, 2023

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upsj
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@upsj upsj commented Nov 30, 2022

This PR adds benchmarking functionality for the CSR sorting and lookup structures (generation and lookup).

Closes #1155

@upsj upsj added the 1:ST:ready-for-review This PR is ready for review label Nov 30, 2022
@upsj upsj self-assigned this Nov 30, 2022
@ginkgo-bot ginkgo-bot added mod:core This is related to the core module. mod:cuda This is related to the CUDA module. mod:hip This is related to the HIP module. mod:reference This is related to the reference module. reg:benchmarking This is related to benchmarking. reg:build This is related to the build system. reg:testing This is related to testing. type:matrix-format This is related to the Matrix formats labels Nov 30, 2022
@upsj upsj requested a review from a team November 30, 2022 22:03
MarcelKoch
MarcelKoch approved these changes Dec 1, 2022
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lgtm, only smaller comments.

benchmark/sparse_blas/sparse_blas.cpp Outdated Show resolved Hide resolved
reference/matrix/csr_kernels.cpp Outdated Show resolved Hide resolved
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benchmark/sparse_blas/operations.cpp

gko::size_type get_memory() const override
{
// read and write everything only once, read row pointers once
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I feel this is a bit misleading. We use black box algorithms to sort each row, so we can't say with certainty how many reads we are actually doing. I would prefer if this function returns some invalid value.

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I think for this kind of segmented sorting, assuming a simple model (no extremely long rows, infinitely fast local memory), I would consider this a fairly good approximation - load the data once (into cache or shared memory doesn't matter much), sort it locally and store the sorted results back. For sorting algorithms, common metrics usually involve either elements/s or runtime/n log n (as the theoretical optimum), the elements/s metric is pretty close to what we are measuring here.

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@MarcelKoch MarcelKoch requested a review from a team December 1, 2022 11:23
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sonarcloud bot commented Dec 8, 2022 •
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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 25 Code Smells

0.0% 0.0% Coverage
0.0% 0.0% Duplication

@fritzgoebel fritzgoebel self-requested a review January 31, 2023 13:13
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LGTM

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upsj and others added 6 commits February 1, 2023 12:11
@upsj
add sort and lookup sparse BLAS benchmarks
ec88b34
@upsj @MarcelKoch
review updates
6950ab8 
- use make_temporary_clone where possible
- remove magic numbers
- improve string formatting
- remove unused header

Co-authored-by: Marcel Koch <marcel.koch@kit.edu>
@upsj
add operations breakdown output
c9aabfb
@upsj
add fixme for mutating operations
142395a
@upsj @MarcelKoch
simplify handling of host data
24c78e7 
Co-authored-by: Marcel Koch <marcel.koch@kit.edu>
@upsj
fix formatting
bf46c06
@upsj upsj added the 1:ST:ready-to-merge This PR is ready to merge. label Feb 1, 2023
@upsj upsj removed the 1:ST:ready-for-review This PR is ready for review label Feb 1, 2023
@upsj upsj merged commit fe142fa into develop Feb 1, 2023
@upsj upsj deleted the sparse_benchmarks branch February 1, 2023 16:17
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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 25 Code Smells

0.0% 0.0% Coverage
0.0% 0.0% 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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@MarcelKoch MarcelKoch MarcelKoch approved these changes

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Benchmarking sparse functionality
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