research-article

EigenCFA: accelerating flow analysis with GPUs

Authors:

Shreyas Ramalingam,

Mary HallAuthors Info & Claims

POPL '11: Proceedings of the 38th annual ACM SIGPLAN-SIGACT symposium on Principles of programming languages

Pages 511 - 522

https://doi.org/10.1145/1926385.1926445

Published: 26 January 2011 Publication History

Abstract

We describe, implement and benchmark EigenCFA, an algorithm for accelerating higher-order control-flow analysis (specifically, 0CFA) with a GPU. Ultimately, our program transformations, reductions and optimizations achieve a factor of 72 speedup over an optimized CPU implementation.

We began our investigation with the view that GPUs accelerate high-arithmetic, data-parallel computations with a poor tolerance for branching. Taking that perspective to its limit, we reduced Shivers's abstract-interpretive 0CFA to an algorithm synthesized from linear-algebra operations. Central to this reduction were "abstract" Church encodings, and encodings of the syntax tree and abstract domains as vectors and matrices.

A straightforward (dense-matrix) implementation of EigenCFA performed slower than a fast CPU implementation. Ultimately, sparse-matrix data structures and operations turned out to be the critical accelerants. Because control-flow graphs are sparse in practice (up to 96% empty), our control-flow matrices are also sparse, giving the sparse matrix operations an overwhelming space and speed advantage.

We also achieved speedups by carefully permitting data races. The monotonicity of 0CFA makes it sound to perform analysis operations in parallel, possibly using stale or even partially-updated data.

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Index Terms

EigenCFA: accelerating flow analysis with GPUs
1. Software and its engineering
  1. Software notations and tools
    1. Compilers

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cover image ACM Conferences

POPL '11: Proceedings of the 38th annual ACM SIGPLAN-SIGACT symposium on Principles of programming languages

January 2011

652 pages

ISBN:9781450304900

DOI:10.1145/1926385

General Chair:
Thomas Ball
Microsoft Research, USA
,
Program Chair:
Mooly Sagiv
Tel Aviv University, Israel

ACM SIGPLAN Notices Volume 46, Issue 1
POPL '11
January 2011
624 pages
ISSN:0362-1340
EISSN:1558-1160
DOI:10.1145/1925844
Issue’s Table of Contents

Copyright © 2011 ACM.

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Published: 26 January 2011

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Cited By

Shobaki GMuyan-Özçelik PHutton JLinck BMalyshenko VKerbow ARamirez-Ortega RGordon V(2024)Instruction Scheduling for the GPU on the GPU2024 IEEE/ACM International Symposium on Code Generation and Optimization (CGO)10.1109/CGO57630.2024.10444869(435-447)Online publication date: 2-Mar-2024
https://doi.org/10.1109/CGO57630.2024.10444869
Stiévenart QVan Es NVan der Plas JDe Roover C(2021)A parallel worklist algorithm and its exploration heuristics for static modular analysesJournal of Systems and Software10.1016/j.jss.2021.111042181:COnline publication date: 1-Nov-2021
https://dl.acm.org/doi/10.1016/j.jss.2021.111042
Shi QZhang CRothermel GBae D(2020)Pipelining bottom-up data flow analysisProceedings of the ACM/IEEE 42nd International Conference on Software Engineering10.1145/3377811.3380425(835-847)Online publication date: 27-Jun-2020
https://dl.acm.org/doi/10.1145/3377811.3380425
Van Es NStievenart QVan der Plas JDe Roover C(2020)A Parallel Worklist Algorithm for Modular Analyses2020 IEEE 20th International Working Conference on Source Code Analysis and Manipulation (SCAM)10.1109/SCAM51674.2020.00006(1-12)Online publication date: Sep-2020
https://doi.org/10.1109/SCAM51674.2020.00006
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