https://github.com/chriscummins/paper-towards-collaborative-performance-tuning
"Towards Collaborative Performance Tuning of Algorithmic Skeletons" (HLPGPU 2016)
https://github.com/chriscummins/paper-towards-collaborative-performance-tuning
academic-publication algorithmic-skeletons autotuning opencl paper performance-tuning stencil-patterns
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"Towards Collaborative Performance Tuning of Algorithmic Skeletons" (HLPGPU 2016)
- Host: GitHub
- URL: https://github.com/chriscummins/paper-towards-collaborative-performance-tuning
- Owner: ChrisCummins
- Created: 2016-11-12T22:06:58.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2018-12-06T23:12:13.000Z (over 6 years ago)
- Last Synced: 2025-01-28T10:45:16.219Z (4 months ago)
- Topics: academic-publication, algorithmic-skeletons, autotuning, opencl, paper, performance-tuning, stencil-patterns
- Language: TeX
- Homepage:
- Size: 2.03 MB
- Stars: 1
- Watchers: 3
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Towards Collaborative Performance Tuning of Algorithmic Skeletons
[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**
> The physical limitations of microprocessor design have forced the industry
> towards increasingly heterogeneous designs to extract performance. This trend
> has not been matched with adequate software tools, leading to a growing
> disparity between the availability of parallelism and the ability for
> application developers to exploit it.
>
> Algorithmic skeletons simplify parallel programming by providing high-level,
> reusable patterns of computation. Achieving performant skeleton
> implementations is a difficult task; skeleton authors must attempt to
> anticipate and tune for a wide range of architectures and use cases. This
> results in implementations that target the general case and cannot provide the
> performance advantages that are gained from tuning low level optimization
> parameters. Autotuning combined with machine learning offers promising
> performance benefits in these situations, but the high cost of training and
> lack of available tools limits the practicality of autotuning for real world
> programming. We believe that performing autotuning at the level of the
> skeleton library can overcome these issues.
>
> In this work, we present OmniTune - an extensible and distributed framework
> for dynamic autotuning of optimization parameters at runtime. OmniTune uses a
> client-server model with a flexible API to support machine learning enabled
> autotuning. Training data is shared across a network of cooperating systems,
> using a collective approach to performance tuning.
>
> We demonstrate the practicality of OmniTune in a case study using the
> algorithmic skeleton library SkelCL. By automatically tuning the workgroup
> size of OpenCL Stencil skeleton kernels, we show that that static tuning
> across a range of GPUs and programs can achieve only 26% of the optimal
> performance, while OmniTune achieves 92% of this maximum, equating to an
> average 5.65x speedup. OmniTune achieves this without introducing a
> significant runtime overhead, and enables portable, cross-device and cross-
> program tuning.
**Presented** High-Level Programming for Heterogeneous and Hierarchical Parallel Systems.
Prague, Czech Republic, Tuesday, Jan 19th 2016. Co-Located with HiPEAC 2016.
```
@inproceedings{cummins2016b,
author = "Cummins, Chris and Petoumenos, Pavlos and Steuwer, Michel and Leather, Hugh",
title = "Towards Collaborative Performance Tuning of Algorithmic Skeletons",
booktitle = "High-Level Programming for Heterogeneous and Hierarchical Parallel Systems (HLPGPU)",
year = "2016",
}
```