Machine learning‐based auto‐tuning for enhanced performance portability of OpenCL applications. (22nd December 2016)

• Record Type: Journal Article
• Authors: Falch, Thomas L.; Elster, Anne C.
• Is Part Of: Concurrency and computation. Volume 29:Number 8(2017)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2016-12-22
• Subjects: artificial neural networks -- auto‐tuning -- heterogeneous computing -- machine learning -- OpenCL -- performance portability; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.4029
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: Heterogeneous computing, combining devices with different architectures such as CPUs and GPUs, is rising in popularity and promises increased performance combined with reduced energy consumption. OpenCL has been proposed as a standard for programming such systems and offers functional portability. However, it suffers from poor performance portability, because applications must be retuned for every new device. In this paper, we use machine learning‐based auto‐tuning to address this problem. Benchmarks are run on a random subset of the tuning parameter spaces, and the results are used to build a machine learning‐based performance model. The model can then be used to find interesting subspaces for further search. We evaluate our method using five image processing benchmarks, with tuning parameter space sizes up to 2.3 M, using different input sizes, on several devices, including an Intel i7 4771 (Haswell) CPU, an Nvidia Tesla K40 GPU, and an AMD Radeon HD 7970 GPU. We compare different machine learning algorithms for the performance model. Our model achieves a mean relative error as low as 3.8% and is able to find solutions on average only 0.29% slower than the best configuration in some cases, evaluating less than 1.1% of the search space. The source code of our framework is available athttps://github.com/acelster/ML‐autotuning .