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A hybrid approach to cache management in heterogeneous CPU-FPGA platforms

机译:异构CPU-FPGA平台中缓存管理的混合方法

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Heterogenous computing is gaining increasing attention due to its promise of high performance with low power. Shared coherent cache based CPU-FPGA platforms, like Intel HARP, are a particularly promising example of such systems with enhanced efficiency and high flexibility. In this work, we propose a hybrid strategy that relies on both static analysis of applications and dynamic control of cache based on static analysis to minimize the contention on the FPGA cache in the emerging CPU-FPGA platforms with shared coherent caches. In the static analysis, we analyze memory access patterns of the accelerated kernels on FPGA using reuse distance theory and generate kernel characteristics called Key values. Thereafter, a dynamic scheme for cache bypassing and partitioning control based on these Key values is developed to increase the cache hit rate and improve the performance. We validate our proposed strategy using a system-level architectural simulator for CPU-FPGA heterogeneous computing systems. Experiments show that the proposed strategy can increase the cache hit rate by 22.90% on average and speed up the application by up to 12.52% with negligible area overhead.
机译:异构计算由于其高性能和低功耗的前景而日益受到关注。基于共享一致性缓存的CPU-FPGA平台(如Intel HARP)是此类系统特别有希望的示例,具有增强的效率和高度的灵活性。在这项工作中,我们提出了一种混合策略,该策略既依赖于应用程序的静态分析,又基于基于静态分析的缓存的动态控制,以最大程度地减少具有共享一致性缓存的新兴CPU-FPGA平台中FPGA缓存的争用。在静态分析中,我们使用重用距离理论分析了FPGA上加速内核的内存访问模式,并生成称为键值的内核特征。此后,开发了一种基于这些Key值的缓存绕过和分区控制的动态方案,以提高缓存命中率并提高性能。我们使用针对CPU-FPGA异构计算系统的系统级体系结构模拟器验证了我们提出的策略。实验表明,所提出的策略可以将缓存命中率平均提高22.90%,并在不增加区域开销的情况下将应用程序的速度提高高达12.52%。

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