MaSiF: Machine Learning Guided Auto-tuning of Parallel Skeletons

Alexander Collins, Christian Fensch, Hugh Leather

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We present MaSiF, a novel tool to auto-tune parallelization parameters of skeleton parallel programs. It reduces the cost of searching the optimization space using a combination of machine learning and linear dimensionality reduction. To auto-tune a new program, a set of program features is determined statically and used to compute k nearest neighbors from a set of training programs. Previously collected performance data for the nearest neighbors is used to reduce the size of the search space using Principal Components Analysis. This results in a set of eigenvectors that are used to search the reduced space. MaSiF achieves 88% of the performance of the oracle, which searches a random set of 10,000 parameter values. MaSiF searches just 45 points, or 0.45% of the optimization space, to achieve this performance. MaSiF provides an average speedup of 1.18x over parallelization parameters chosen by a human expert.
Original languageEnglish
Title of host publicationProceedings of the 21st International Conference on Parallel Architectures and Compilation Techniques
Place of PublicationNew York, NY, USA
PublisherACM
Pages437-438
Number of pages2
DOIs
Publication statusPublished - 2012

Publication series

NamePACT '12
PublisherACM

Keywords

  • auto-tuning, fastflow, machine learning, multi-core, parallel skeletons

Fingerprint Dive into the research topics of 'MaSiF: Machine Learning Guided Auto-tuning of Parallel Skeletons'. Together they form a unique fingerprint.

Cite this