RelaxReplay: record and replay for relaxed-consistency multiprocessors

N. Honarmand, J. Torrellas
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引用次数: 20

Abstract

Record and Deterministic Replay (RnR) of multithreaded programs on relaxed-consistency multiprocessors has been a long-standing problem. While there are designs that work for Total Store Ordering (TSO), finding a general solution that is able to record the access reordering allowed by any relaxed-consistency model has proved challenging. This paper presents the first complete solution for hard-ware-assisted memory race recording that works for any relaxed-consistency model of current processors. With the scheme, called RelaxReplay, we can build an RnR system for any relaxed-consistency model and coherence protocol. RelaxReplay's core innovation is a new way of capturing memory access reordering. Each memory instruction goes through a post-completion in-order counting step that detects any reordering, and efficiently records it. We evaluate RelaxReplay with simulations of an 8-core release-consistent multicore running SPLASH-2 programs. We observe that RelaxReplay induces negligible overhead during recording. In addition, the average size of the log produced is comparable to the log sizes reported for existing solutions, and still very small compared to the memory bandwidth of modern machines. Finally, deterministic replay is efficient and needs minimal hardware support.
松弛一致性多处理器的记录和重放
多线程程序在松弛一致性多处理器上的记录和确定性重放(RnR)一直是一个长期存在的问题。虽然有一些设计适用于总存储排序(TSO),但找到一种能够记录任何松弛一致性模型允许的访问重新排序的通用解决方案被证明是具有挑战性的。本文提出了硬件辅助内存竞争记录的第一个完整解决方案,适用于当前处理器的任何松弛一致性模型。利用这个名为RelaxReplay的方案,我们可以为任何松弛一致性模型和一致性协议构建RnR系统。RelaxReplay的核心创新是一种捕捉内存访问重新排序的新方法。每个内存指令都经过一个完成后的顺序计数步骤,该步骤检测到任何重新排序,并有效地记录它。我们通过模拟8核版本一致的多核运行SPLASH-2程序来评估RelaxReplay。我们观察到,在记录期间,RelaxReplay的开销可以忽略不计。此外,生成的日志的平均大小与现有解决方案报告的日志大小相当,但与现代机器的内存带宽相比仍然非常小。最后,确定性重放是高效的,并且需要最少的硬件支持。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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