research-article

Performance Embeddings: A Similarity-Based Transfer Tuning Approach to Performance Optimization

Authors:

Lukas Trümper,

Philipp Schaad,

Alexandru Calotoiu,

Torsten HoeflerAuthors Info & Claims

ICS '23: Proceedings of the 37th International Conference on Supercomputing

Pages 50 - 62

https://doi.org/10.1145/3577193.3593714

Published: 21 June 2023 Publication History

Abstract

Performance optimization is an increasingly challenging but often repetitive task. While each platform has its quirks, the underlying code transformations rely on data movement and computational characteristics that recur across applications. This paper proposes to leverage those similarities by constructing an embedding space for subprograms. The continuous space captures both static and dynamic properties of loop nests via symbolic code analysis and performance profiling, respectively. Performance embeddings enable direct knowledge transfer of performance tuning between applications, which can result from autotuning or tailored improvements. We demonstrate this transfer tuning approach on case studies in deep neural networks, dense and sparse linear algebra compositions, and numerical weather prediction stencils. Transfer tuning reduces the search complexity by up to four orders of magnitude and outperforms the MKL library in sparse-dense matrix multiplication. The results exhibit clear correspondences between program characteristics and optimizations, outperforming prior specialized state-of-the-art approaches and generalizing beyond their capabilities.

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Cited By

Kim DOh ASalakhutdinov RKolter ZHeller KWeller AOliver NScarlett JBerkenkamp F(2024)Translating subgraphs to nodes makes simple GNNs strong and efficient for subgraph representation learningProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3693049(24415-24436)Online publication date: 21-Jul-2024
https://dl.acm.org/doi/10.5555/3692070.3693049
Gong JChen T(2024)Deep Configuration Performance Learning: A Systematic Survey and TaxonomyACM Transactions on Software Engineering and Methodology10.1145/370298634:1(1-62)Online publication date: 5-Nov-2024
https://dl.acm.org/doi/10.1145/3702986
Li LFlynn THoisie A(2024)Learning Generalizable Program and Architecture Representations for Performance ModelingProceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis10.1109/SC41406.2024.00072(1-15)Online publication date: 17-Nov-2024
https://dl.acm.org/doi/10.1109/SC41406.2024.00072

Index Terms

Performance Embeddings: A Similarity-Based Transfer Tuning Approach to Performance Optimization
1. Computing methodologies
  1. Machine learning
2. Software and its engineering
  1. Software notations and tools
    1. Compilers

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cover image ACM Conferences

ICS '23: Proceedings of the 37th ACM International Conference on Supercomputing

June 2023

505 pages

ISBN:9798400700569

DOI:10.1145/3577193

Chair:
Kyle Gallivan,
Co-chair:
Efstratios Gallopoulos,
Program Co-chairs:
Dimitrios S. Nikolopoulos,
Ramon Beivide

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Published: 21 June 2023

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Cited By

Kim DOh ASalakhutdinov RKolter ZHeller KWeller AOliver NScarlett JBerkenkamp F(2024)Translating subgraphs to nodes makes simple GNNs strong and efficient for subgraph representation learningProceedings of the 41st International Conference on Machine Learning10.5555/3692070.3693049(24415-24436)Online publication date: 21-Jul-2024
https://dl.acm.org/doi/10.5555/3692070.3693049
Gong JChen T(2024)Deep Configuration Performance Learning: A Systematic Survey and TaxonomyACM Transactions on Software Engineering and Methodology10.1145/370298634:1(1-62)Online publication date: 5-Nov-2024
https://dl.acm.org/doi/10.1145/3702986
Li LFlynn THoisie A(2024)Learning Generalizable Program and Architecture Representations for Performance ModelingProceedings of the International Conference for High Performance Computing, Networking, Storage, and Analysis10.1109/SC41406.2024.00072(1-15)Online publication date: 17-Nov-2024
https://dl.acm.org/doi/10.1109/SC41406.2024.00072

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