QuickRelease: A throughput-oriented approach to release consistency on GPUs
QuickRelease: A throughput-oriented approach to release consistency on GPUs
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QuickRelease：一种以吞吐量为导向的方法来释放 GPU 上的一致性
DOI：
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发表时间：
2014
期刊：
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通讯作者：
D. Wood
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文献类型：
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作者：
Blake A. Hechtman;Shuai Che;Derek Hower;Yingying Tian;Bradford M. Beckmann;M. Hill;S. Reinhardt;D. Wood
图形处理单元(GPU)具有专门的面向吞吐量的内存系统，这些内存系统针对使用便签式存储器显式捕获局部性的流式写入进行了优化。将GPU的用途扩展到图形之外，鼓励简化编程的设计(例如，使用高速缓存而不是便签)，并通过更精细的同步更好地支持不规则应用程序。我们的假设是，与CPU一样，GPU将从缓存和一致性中受益，但CPU风格的“读入所有权”(RFO)一致性不适合维持对常规流工作负载的支持。本文提出了QuickRelease(QR)，它在两个方面对传统的GPU存储系统进行了改进。首先，QR使用FIFO强制执行写入的部分顺序，以便在不频繁刷新缓存的情况下完成同步操作。因此，即使其他线程正在执行同步，QR中的非同步线程也可以重用缓存的数据。其次，QR对读取和写入所需的资源进行分区，以减少写入对读取性能的影响。对各种通用GPU工作负载的模拟结果表明，与传统的GPU存储系统相比，QR实现了7%的平均性能提升。此外，对于具有更细粒度同步的新兴工作负载，与传统的GPU内存系统相比，QR实现了高达42%的性能提升，而不存在RFO一致性的可扩展性挑战。为此，QR提供了面向吞吐量的解决方案，以在GPU上提供细粒度同步。
Graphics processing units (GPUs) have specialized throughput-oriented memory systems optimized for streaming writes with scratchpad memories to capture locality explicitly. Expanding the utility of GPUs beyond graphics encourages designs that simplify programming (e.g., using caches instead of scratchpads) and better support irregular applications with finer-grain synchronization. Our hypothesis is that, like CPUs, GPUs will benefit from caches and coherence, but that CPU-style “read for ownership” (RFO) coherence is inappropriate to maintain support for regular streaming workloads. This paper proposes QuickRelease (QR), which improves on conventional GPU memory systems in two ways. First, QR uses a FIFO to enforce the partial order of writes so that synchronization operations can complete without frequent cache flushes. Thus, non-synchronizing threads in QR can re-use cached data even when other threads are performing synchronization. Second, QR partitions the resources required by reads and writes to reduce the penalty of writes on read performance. Simulation results across a wide variety of general-purpose GPU workloads show that QR achieves a 7% average performance improvement compared to a conventional GPU memory system. Furthermore, for emerging workloads with finer-grain synchronization, QR achieves up to 42% performance improvement compared to a conventional GPU memory system without the scalability challenges of RFO coherence. To this end, QR provides a throughput-oriented solution to provide fine-grain synchronization on GPUs.