GT-Race: Graph Traversal Based Data Race Detection for Asynchronous Many-Task Parallelism

摘要

Asynchronous Many-Task (AMT) parallelism is growing in popularity because of its promise to support future platforms with new heterogeneity and resiliency requirements. It supports the construction of parallel programs with fine-grained tasks, thereby enabling portability across a wide range of platforms. However, applications written for AMT parallelism still remain vulnerable to data races, and existing data race detection tools are unsuitable for AMT programs because they either incur intractably large overheads or are limited to restricted task structures such as fork-join parallelism. In this paper, we propose GT-Race, a new graph-traversal based data race detector for AMT parallelism. It leverages the computation graph data structure, which encodes the general happens-before structures in AMT programs. After introducing a baseline algorithm for data race detection, we propose key optimizations to reduce its time and space complexity, including the epoch adjacency list to compress the computation graph representation, the reachability cache combined with depth filtering to reduce the number of unnecessary traversals, and bounded race detection to limit the range of data that is monitored. The impact of these optimizations is demonstrated for nine benchmark programs written for the Open Community Runtime (OCR), an open source AMT runtime that supports point-to-point synchronization and disjoint data blocks.