https://github.com/chriscummins/paper-autotuning-opencl-wgsize
"Autotuning OpenCL Workgroup Size for Stencil Patterns" (ADAPT 2016)
https://github.com/chriscummins/paper-autotuning-opencl-wgsize
academic-publication autotuning machine-learning opencl paper performance-tuning stencil-patterns workgroup-sizes
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"Autotuning OpenCL Workgroup Size for Stencil Patterns" (ADAPT 2016)
- Host: GitHub
- URL: https://github.com/chriscummins/paper-autotuning-opencl-wgsize
- Owner: ChrisCummins
- Created: 2016-11-12T22:07:39.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2018-12-06T23:12:26.000Z (over 6 years ago)
- Last Synced: 2025-01-28T10:45:16.866Z (4 months ago)
- Topics: academic-publication, autotuning, machine-learning, opencl, paper, performance-tuning, stencil-patterns, workgroup-sizes
- Language: TeX
- Homepage:
- Size: 7.82 MB
- Stars: 2
- Watchers: 3
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Autotuning OpenCL Workgroup Size for Stencil Patterns
[Chris Cummins](http://chriscummins.cc/),
[Pavlos Petoumenos](http://homepages.inf.ed.ac.uk/ppetoume/),
[Michel Steuwer](http://homepages.inf.ed.ac.uk/msteuwer/),
[Hugh Leather](http://homepages.inf.ed.ac.uk/hleather/).
**Abstract**
> Selecting an appropriate workgroup size is critical for the performance of
> OpenCL kernels, and requires knowledge of the underlying hardware, the data
> being operated on, and the implementation of the kernel. This makes portable
> performance of OpenCL programs a challenging goal, since simple heuristics and
> statically chosen values fail to exploit the available performance. To address
> this, we propose the use of machine learning-enabled autotuning to
> automatically predict workgroup sizes for stencil patterns on CPUs and multi-
> GPUs.
>
> We present three methodologies for predicting workgroup sizes. The first,
> using classifiers to select the optimal workgroup size. The second and third
> proposed methodologies employ the novel use of regressors for performing
> classification by predicting the runtime of kernels and the relative
> performance of different workgroup sizes, respectively. We evaluate the
> effectiveness of each technique in an empirical study of 429 combinations of
> architecture, kernel, and dataset, comparing an average of 629 different
> workgroup sizes for each. We find that autotuning provides a median 3.79x
> speedup over the best possible fixed workgroup size, achieving 94% of the
> maximum performance.
**Presented** The 6th International Workshop on Adaptive Self-tuning Computing
Systems. Prague, Czech Republic, Monday, Jan 18th 2016. Co-Located with HiPEAC
2016.
```
@inproceedings{cummins2016a,
author = "Cummins, Chris and Petoumenos, Pavlos and Steuwer, Michel and Leather, Hugh",
title = "Autotuning OpenCL Workgroup Size for Stencil Patterns",
booktitle = "The 6th International Workshop on Adaptive Self-tuning Computing Systems (ADAPT)",
arxivId = "1511.02490v3",
year = "2016",
}
```