DSM中科学码数据一致性的硬件预测

Jean-Thomas Acquaviva, W. Jalby
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引用次数: 2

摘要

本文提出了一种硬件机制来降低DSM系统中科学计算中的相干开销。第一阶段的目标是在地址空间中检测一致性事件的常规模式(称为流)(例如独占、共享或无效请求)。一旦在循环级别检测到流,就可以在循环级别(空间局部性)以及循环之间(时间局部性)利用数据访问的规律性。我们提出了一种能够有效检测和利用这些规则模式的硬件机制。讨论了预期的好处和硬件复杂性,并揭示了有限的缺点和潜在的开销。对于典型科学应用程序的基准测试套件,从一致性流和优化的有效性两方面来看,结果都非常有希望。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Hardware Prediction for Data Coherency of Scientific Codes on DSM
This paper proposes a hardware mechanism for reducing coherency overhead occurring in scientific computations within DSM systems. A first phase aims at detecting, in the address space regular patterns (called streams) of coherency events (such as requests for exclusive, shared or invalidation). Once a stream is detected at a loop level, regularity of data access can be exploited at the loop level (spatial locality) but also between loops (temporal locality). We present a hardware mechanism capable of detecting and exploiting efficiently these regular patterns. Expectable benefits as well as hardware complexity are discussed and the limited drawbacks and potential over-heads are exposed. For a benchmarks suite of typical scientific applications results are very promising both in terms of coherency streams and the effectiveness of our optimizations.
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