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GPUs are progressively being integrated into modern society, playing a pivotal role in Artificial Intelligence and High-Performance Computing. Programmers need a deep understanding of the GPU programming model to avoid subtle data-races in their codes. Static verification that is sound and incomplete can guarantee data-race freedom, but the alarms it raises may be spurious and need to be validated. In this paper, we establish a True Positive Theorem for a static data-race detector for GPU programs, i.e., a result that identifies a class of programs for which our technique only raises true alarms. Our work builds on the formalism of memory access protocols, that models the concurrency operations of CUDA programs. The crux of our approach is an approximation analysis that can correctly identify true alarms, and pinpoint the conditions that make an alarm imprecise. Our approximation analysis detects when the reported locations are reachable (control independence, or CI), and when the reported locations are precise (data independence, or DI), as well identify inexact values in an alarm. In addition to a True Positive result for programs that are CI and DI, we establish the root causes of spurious alarms depending on whether CI or DI are present. We apply our theory to introduce FaialAA, the first sound and partially complete data-race detector. We evaluate FaialAA in three experiments. First, in a comparative study with the state-of-the-art tools, we show that FaialAA confirms more DRF programs than others while emitting 1.9× fewer potential alarms. Importantly, the approximation analysis of FaialAA detects 10 undocumented data-races. Second, in an experiment studying 6 commits of data-race fixes in open source projects OpenMM and Nvidia’s MegaTron, FaialAA confirmed the buggy and fixed versions of 5 commits, while others were only able to confirm 2. Third, we show that 59.5% of 2,770 programs are CI and DI, quantifying when the approximation analysis of FaialAA is complete. This paper is accompanied by the mechanized proofs of the theoretical results presented therein and a tool (FaialAA) implementing of our theory.
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Hidden Assumptions in Static Verification of Data-race Free GPU Programs
GPUs are massively parallel devices that promise a great return of investment at a cost: GPUs are notably difficult to get right. We discuss a static analysis tool for GPU programs, called Faial, that can detect data-races and data-race freedom. We studied a dataset of 191 data-race free programs and found that 98% needs specific thread configuration to be analyzable, and that 27% needs user-provided assertions to be analyzable. We also report that Faial was able to find data-races in at least 92% of the kernels with missing assumptions.
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- Award ID(s):
- 2204986
- PAR ID:
- 10668319
- Publisher / Repository:
- Springer Nature Switzerland
- Date Published:
- Page Range / eLocation ID:
- 55 to 63
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Book Chapter:
- https://doi.org/10.1007/978-3-031-97492-2_6
