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https://github.com/JuliaLinearAlgebra/Octavian.jl
Multi-threaded BLAS-like library that provides pure Julia matrix multiplication
https://github.com/JuliaLinearAlgebra/Octavian.jl
Last synced: 3 months ago
JSON representation
Multi-threaded BLAS-like library that provides pure Julia matrix multiplication
- Host: GitHub
- URL: https://github.com/JuliaLinearAlgebra/Octavian.jl
- Owner: JuliaLinearAlgebra
- License: other
- Created: 2020-12-26T21:24:35.000Z (almost 4 years ago)
- Default Branch: master
- Last Pushed: 2024-08-02T03:13:34.000Z (3 months ago)
- Last Synced: 2024-08-02T04:25:17.679Z (3 months ago)
- Language: Julia
- Homepage: https://julialinearalgebra.github.io/Octavian.jl/stable/
- Size: 1.59 MB
- Stars: 223
- Watchers: 10
- Forks: 18
- Open Issues: 22
-
Metadata Files:
- Readme: README.md
- License: LICENSE.md
Awesome Lists containing this project
- awesome-sciml - JuliaLinearAlgebra/Octavian.jl: Multi-threaded BLAS-like library that provides pure Julia matrix multiplication
README
# Octavian
[![Documentation (stable)][docs-stable-img]][docs-stable-url]
[![Documentation (dev)][docs-dev-img]][docs-dev-url]
[![Continuous Integration][ci-img]][ci-url]
[![Continuous Integration (Julia nightly)][ci-julia-nightly-img]][ci-julia-nightly-url]
[![Code Coverage][codecov-img]][codecov-url]
[docs-stable-url]: https://octavian.JuliaLinearAlgebra.org/stable
[docs-dev-url]: https://octavian.JuliaLinearAlgebra.org/dev
[ci-url]: https://github.com/JuliaLinearAlgebra/Octavian.jl/actions?query=workflow%3ACI
[ci-julia-nightly-url]: https://github.com/JuliaLinearAlgebra/Octavian.jl/actions?query=workflow%3A%22CI+%28Julia+nightly%29%22
[codecov-url]: https://codecov.io/gh/JuliaLinearAlgebra/Octavian.jl
[docs-stable-img]: https://img.shields.io/badge/docs-stable-blue.svg "Documentation (stable)"
[docs-dev-img]: https://img.shields.io/badge/docs-dev-blue.svg "Documentation (dev)"
[ci-img]: https://github.com/JuliaLinearAlgebra/Octavian.jl/workflows/CI/badge.svg "Continuous Integration"
[ci-julia-nightly-img]: https://github.com/JuliaLinearAlgebra/Octavian.jl/workflows/CI%20(Julia%20nightly)/badge.svg "Continuous Integration (Julia nightly)"
[codecov-img]: https://codecov.io/gh/JuliaLinearAlgebra/Octavian.jl/branch/master/graph/badge.svg "Code Coverage"
To make sure CPUSummary 1.11 and newer are using `Hwloc`, you may want to run
```julia
julia> using CPUSummary
julia> CPUSummary.use_hwloc(true);
```
which will hopefully enable accurate hardware information. This is the default,
so it should typically be unnecessary.
Octavian.jl is a multi-threaded BLAS-like library that provides pure Julia
matrix multiplication on the CPU, built on top of
[LoopVectorization.jl](https://github.com/chriselrod/LoopVectorization.jl).
Please see the
[Octavian documentation](https://octavian.JuliaLinearAlgebra.org/stable).
Octavian dropped 32bit Julia support. See [PR#157](https://github.com/JuliaLinearAlgebra/Octavian.jl/pull/157). If you're interested in restoring it, please file a PR to fix failing tests.
## Benchmarks
You can run benchmarks using [BLASBenchmarksCPU.jl](https://github.com/JuliaLinearAlgebra/BLASBenchmarksCPU.jl):
```julia
julia> @time using BLASBenchmarksCPU
7.278954 seconds (17.59 M allocations: 1.107 GiB, 6.22% gc time)
julia> rb = runbench(sizes = logspace(10, 1_000, 200)); plot(rb, displayplot = false);
Progress: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| Time: 2:25:04
Size: (1000, 1000, 1000)
BLIS: (MedianGFLOPS = 1051.0, MaxGFLOPS = 1476.0)
Gaius: (MedianGFLOPS = 765.8, MaxGFLOPS = 941.7)
MKL: (MedianGFLOPS = 1348.0, MaxGFLOPS = 1589.0)
Octavian: (MedianGFLOPS = 1816.0, MaxGFLOPS = 1895.0)
OpenBLAS: (MedianGFLOPS = 1254.0, MaxGFLOPS = 1385.0)
Tullio: (MedianGFLOPS = 1102.0, MaxGFLOPS = 1196.0)
LoopVectorization: (MedianGFLOPS = 1552.0, MaxGFLOPS = 1721.0)
julia> versioninfo()
Julia Version 1.7.0-DEV.1124
Commit d18cf93bac* (2021-05-19 16:11 UTC)
Platform Info:
OS: Linux (x86_64-generic-linux)
CPU: Intel(R) Core(TM) i9-10980XE CPU @ 3.00GHz
WORD_SIZE: 64
LIBM: libopenlibm
LLVM: libLLVM-11.0.1 (ORCJIT, cascadelake)
Environment:
JULIA_NUM_THREADS = 36
```
Resulted in the following:
## Related Packages
| Julia Package | CPU | GPU |
| ---------------------------------------------------------------- | --- | --- |
| [Gaius.jl](https://github.com/MasonProtter/Gaius.jl) | Yes | No |
| [GemmKernels.jl](https://github.com/JuliaGPU/GemmKernels.jl) | No | Yes |
| [Octavian.jl](https://github.com/JuliaLinearAlgebra/Octavian.jl) | Yes | No |
| [Tullio.jl](https://github.com/mcabbott/Tullio.jl) | Yes | Yes |
In general:
- Octavian has the fastest CPU performance.
- GemmKernels has the fastest GPU performance.
- Tullio is the most flexible.