LD: Low-Overhead GPU Race Detection Without Access Monitoring
LD: Low-Overhead GPU Race Detection Without Access Monitoring
复制标题
LD：无需访问监控的低开销 GPU 竞争检测
DOI：
10.1145/3046678
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发表时间：
2017
影响因子：
1.6
通讯作者：
Ding, Chen
中科院分区：
文献类型：
--
作者：
Li, Pengcheng;Hu, Xiaoyu;Chen, Dong;Brock, Jacob;Luo, Hao;Zhang, Eddy Z.;Ding, Chen
数据竞争检测已经成为GPU编程中的一个重要问题。针对以往设计的CPU竞争检测工具主要是任务并行的问题，特别是在监控GPU程序经常使用的数千个线程时，由于访问插装，导致GPU开销较大，提出了一种针对GPU架构设计和优化的新型数据并行解决方案。它包括编译器支持和一组运行时技术。它使用基于值的检查，检测以前工作中报告的竞争，发现新的竞争，并支持无竞争的确定性GPU执行。更重要的是，竞争检查是大规模数据并行的，不会引入发散分支或原子同步。对于超过一半的测试，它的速度低于5倍，平均为10倍，比NVIDIA的Cuda-Memcheck工具和使用细粒度Access工具的方法高出一个数量级。
Data race detection has become an important problem in GPU programming. Previous designs of CPU race-checking tools are mainly task parallel and incur high overhead on GPUs due to access instrumentation, especially when monitoring many thousands of threads routinely used by GPU programs.This article presents a novel data-parallel solution designed and optimized for the GPU architecture. It includes compiler support and a set of runtime techniques. It uses value-based checking, which detects the races reported in previous work, finds new races, and supports race-free deterministic GPU execution. More important, race checking is massively data parallel and does not introduce divergent branching or atomic synchronization. Its slowdown is less than 5 × for over half of the tests and 10 × on average, which is orders of magnitude more efficient than the cuda-memcheck tool by Nvidia and the methods that use fine-grained access instrumentation.