A Case for Scoped Persist Barriers in GPUs
A Case for Scoped Persist Barriers in GPUs
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GPU 中范围持久性障碍的案例
DOI：
10.1145/3180270.3180275
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Mitesh R. Meswani
中科院分区：
文献类型：
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作者：
Dibakar Gope;Arkaprava Basu;Sooraj Puthoor;Mitesh R. Meswani
计算的两个主要趋势是显而易见的-GPU作为一流计算元件的出现和作为DRAM补充的字节可寻址非易失性存储器技术(NVRAM)的出现。GPU和NVRAM很可能在未来的系统中共存。然而，以前的工作要么专注于GPU，要么孤立地关注NVRAM。在这项工作中，我们研究了GPU有效和正确地操作驻留在NVRAM上的持久数据结构所必需的增强。具体地说，我们发现以前提出的以CPU为中心的持久化障碍不适用于GPU。因此，我们引入了作用域持久化障碍的概念，它与GPU的分层编程框架保持一致。作用域持久化障碍使GPU程序员能够表达给定持久化障碍适用于哪个执行组(也称为作用域)。我们证明：1使用比算法要求的作用域更窄的作用域会导致持久数据结构的不一致，2使用比必要的作用域更大的作用域会导致显著的性能损失(例如，25%或更多)。因此，未来的GPU可以受益于不同作用域的持久化障碍。
Two key trends in computing are evident --- emergence of GPU as a first-class compute element and emergence of byte-addressable nonvolatile memory technologies (NVRAM) as DRAM-supplement. GPUs and NVRAMs are likely to coexist in future systems. However, previous works have either focused on GPUs or on NVRAMs, in isolation. In this work, we investigate the enhancements necessary for a GPU to efficiently and correctly manipulate NVRAM-resident persistent data structures. Specifically, we find that previously proposed CPU-centric persist barriers fall short for GPUs. We thus introduce the concept of scoped persist barriers that aligns with the hierarchical programming framework of GPUs. Scoped persist barriers enable GPU programmers to express which execution group (a.k.a., scope) a given persist barrier applies to. We demonstrate that: 1 use of narrower scope than algorithmically-required can lead to inconsistency of persistent data structure, and 2 use of wider scope than necessary leads to significant performance loss (e.g., 25% or more). Therefore, a future GPU can benefit from persist barriers with different scopes.