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The Design and Implementation of a Verification Technique for GPU Kernels

Authors:

Alastair F. Donaldson,

John WickersonAuthors Info & Claims

ACM Transactions on Programming Languages and Systems (TOPLAS), Volume 37, Issue 3

Article No.: 10, Pages 1 - 49

https://doi.org/10.1145/2743017

Published: 22 May 2015 Publication History

Abstract

We present a technique for the formal verification of GPU kernels, addressing two classes of correctness properties: data races and barrier divergence. Our approach is founded on a novel formal operational semantics for GPU kernels termed <i>synchronous, delayed visibility (SDV)</i> semantics, which captures the execution of a GPU kernel by multiple groups of threads. The SDV semantics provides operational definitions for barrier divergence and for both inter- and intra-group data races. We build on the semantics to develop a method for reducing the task of verifying a massively parallel GPU kernel to that of verifying a sequential program. This completely avoids the need to reason about thread interleavings, and allows existing techniques for sequential program verification to be leveraged. We describe an efficient encoding of data race detection and propose a method for automatically inferring the loop invariants that are required for verification. We have implemented these techniques as a practical verification tool, GPUVerify, that can be applied directly to OpenCL and CUDA source code. We evaluate GPUVerify with respect to a set of 162 kernels drawn from public and commercial sources. Our evaluation demonstrates that GPUVerify is capable of efficient, automatic verification of a large number of real-world kernels.

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Cited By

Liew DCogumbreiro TLange J(2024)Sound and Partially-Complete Static Analysis of Data-Races in GPU ProgramsProceedings of the ACM on Programming Languages10.1145/36897978:OOPSLA2(2434-2461)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3689797
Cogumbreiro TLange JLiew DZicarelli H(2024)Memory access protocols: certified data-race freedom for GPU kernelsFormal Methods in System Design10.1007/s10703-023-00415-063:1-3(134-171)Online publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1007/s10703-023-00415-0
Joshi SMuduganti G(2023)GPURepair: Automated Repair of GPU Kernels (Extended Version)Sādhanā10.1007/s12046-023-02291-049:1Online publication date: 22-Dec-2023
https://doi.org/10.1007/s12046-023-02291-0
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Index Terms

The Design and Implementation of a Verification Technique for GPU Kernels
1. Theory of computation
  1. Logic
  2. Semantics and reasoning
    1. Program reasoning

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OOPSLA '12: Proceedings of the ACM international conference on Object oriented programming systems languages and applications

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GPUVerify: a verifier for GPU kernels
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We present a technique for verifying race- and divergence-freedom of GPU kernels that are written in mainstream kernel programming languages such as OpenCL and CUDA. Our approach is founded on a novel formal operational semantics for GPU programming ...
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Published In

cover image ACM Transactions on Programming Languages and Systems

ACM Transactions on Programming Languages and Systems Volume 37, Issue 3

June 2015

134 pages

ISSN:0164-0925

EISSN:1558-4593

DOI:10.1145/2785583

Editor:
Jens Palsberg
University of California, Los Angeles, USA

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Copyright © 2015 ACM.

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Publication History

Published: 22 May 2015

Accepted: 01 March 2015

Revised: 01 November 2014

Received: 01 April 2014

Published in TOPLAS Volume 37, Issue 3

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EU FP7 STREP project CARP (project number 287767)
EPSRC-funded PhD studentships
EPSRC project EP/G051100/2

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Cited By

Liew DCogumbreiro TLange J(2024)Sound and Partially-Complete Static Analysis of Data-Races in GPU ProgramsProceedings of the ACM on Programming Languages10.1145/36897978:OOPSLA2(2434-2461)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3689797
Cogumbreiro TLange JLiew DZicarelli H(2024)Memory access protocols: certified data-race freedom for GPU kernelsFormal Methods in System Design10.1007/s10703-023-00415-063:1-3(134-171)Online publication date: 1-Oct-2024
https://dl.acm.org/doi/10.1007/s10703-023-00415-0
Joshi SMuduganti G(2023)GPURepair: Automated Repair of GPU Kernels (Extended Version)Sādhanā10.1007/s12046-023-02291-049:1Online publication date: 22-Dec-2023
https://doi.org/10.1007/s12046-023-02291-0
Wu WHückelheim JHovland PLuo ZSiegel S(2023)Model Checking Race-Freedom When “Sequential Consistency for Data-Race-Free Programs” is GuaranteedComputer Aided Verification10.1007/978-3-031-37703-7_13(265-287)Online publication date: 17-Jul-2023
https://dl.acm.org/doi/10.1007/978-3-031-37703-7_13
Liew DCogumbreiro TLange J(2022)Provable GPU Data-Races in Static Race DetectionElectronic Proceedings in Theoretical Computer Science10.4204/EPTCS.356.4356(36-45)Online publication date: 24-Mar-2022
https://doi.org/10.4204/EPTCS.356.4
Horga ARezine AChattopadhyay SEles PPeng Z(2022)Symbolic identification of shared memory based bank conflicts for GPUsJournal of Systems Architecture: the EUROMICRO Journal10.1016/j.sysarc.2022.102518127:COnline publication date: 1-Jun-2022
https://dl.acm.org/doi/10.1016/j.sysarc.2022.102518
Sorensen TSalvador LRaval HEvrard HWickerson JMartonosi MDonaldson A(2021)Specifying and testing GPU workgroup progress modelsProceedings of the ACM on Programming Languages10.1145/34855085:OOPSLA(1-30)Online publication date: 15-Oct-2021
https://dl.acm.org/doi/10.1145/3485508
Kamath ABasu A(2021)iGUARDProceedings of the ACM SIGOPS 28th Symposium on Operating Systems Principles10.1145/3477132.3483545(49-65)Online publication date: 26-Oct-2021
https://dl.acm.org/doi/10.1145/3477132.3483545
Alur RDevietti JLeija OSinghania N(2021)Static detection of uncoalesced accesses in GPU programsFormal Methods in System Design10.1007/s10703-021-00362-860:1(1-32)Online publication date: 5-Mar-2021
https://dl.acm.org/doi/10.1007/s10703-021-00362-8
Cogumbreiro TLange JRong DZicarelli H(2021)Checking Data-Race Freedom of GPU Kernels, CompositionallyComputer Aided Verification10.1007/978-3-030-81685-8_19(403-426)Online publication date: 20-Jul-2021
https://dl.acm.org/doi/10.1007/978-3-030-81685-8_19
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