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MC: A Software Solution to Reduce Errors Caused by Incoherent Caches on GPUs in Unstructured Graphic Algorithm

机译:M&C:一个软件解决方案,以减少非结构化图形算法在GPU上的不连贯高速缓存引起的错误

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Recently, researchers have focused on addressing incoherent caches on GPUs as current GPUs lack hardware to support that. Moreover, the support for inter-block communication also lacks which limits the scalability of parallel programming especially in the unstructured algorithm in which program would share data between different threads. Barrier synchronization can be a solution but it becomes invalid because of incoherent caches. In this paper, we propose a set of rules for programming on current GPUs to avoid the errors caused by incoherent caches when applying barrier synchronization. We also leverage these rules into an unstructured graphic algorithm - constrained Delaunay triangulation. In comparison with traditional ways such as (1) disabling L1 cache and (2) using keyword volatile, we find that when vertices is over 300K the error rate is lesser than (1) by 91.19% and than (2) by 84.2% on average.
机译:最近,研究人员专注于解决GPU上的不连贯的高速缓存,因为目前的GPU缺乏硬件来支持这一点。此外,对块间通信的支持也缺乏,其限制了并行编程的可扩展性,尤其是在非结构化算法中,程序将在不同线程之间共享数据。屏障同步可以是一个解决方案,但由于缓存不连贯的缓存,它变得无效。在本文中,我们提出了一组规则,用于编程当前GPU,以避免在应用屏障同步时由不连贯高速缓存引起的错误。我们还将这些规则利用到一个非结构化的图形算法 - 约束的Delaunay三角测量。与传统方式(如1)禁用L1缓存和(2)使用关键字易失性,我们发现当顶点超过300k时,错误率小于(1)×91.19%,而不是(2)达84.2%平均数。

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