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Parallel determinacy race detection for futures

Yifan Xu, Kyle Singer, I-Ting Angelina Lee

2020Year
11Citations
3Top-tier citations

Abstract

The use of futures can generate arbitrary dependences in the computation, making it difficult to detect races efficiently. Algorithms proposed by prior work to detect races on programs with futures all have to execute the program sequentially. We propose F-Order, the first known parallel race detection algorithm that detects races on programs that use futures. Given a computation with work T1 and span T∞, our algorithm detects races in time O((T1 lg k + k2)/P + T∞(k + lg r lg k)) processors, where k is the number of future operations, r is the maximum number of readers per memory location, and k is the maximum number of future operations done by a single future task, which is typically small. We have also implemented a prototype system based on the proposed algorithm and empirically demonstrates its practical efficiency and scalability.

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