Fg-STP:多核上的细粒度单线程分区

Rakesh Ranjan, Fernando Latorre, P. Marcuello, Antonio González
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引用次数: 7

摘要

功率和复杂性问题导致微处理器行业转向芯片多处理器,以便能够更好地利用摩尔定律所保证的额外晶体管。虽然并行程序将能够利用这些cmp的大部分优势,但单线程应用程序无法从中受益。在本文中,我们提出了细粒度单线程分区(Fg-STP),这是一种利用CMP设计来加速单线程应用程序的纯硬件方案。我们的建议通过重新配置两个核心来提高单线程性能,目的是在指令的获取和执行上进行协作。这些核心基本上是传统的乱序核心,其中的执行使用专用硬件进行编排,对核心的原始设计产生最小的局部影响。这种方法在指令粒度上划分代码,不同于之前广泛使用依赖推测、复制和通信的建议。这些特性与在大型指令窗口上查找并行性的能力相结合,而无需任何软件干预(不需要重新编译或分析提示)。这些特性使得Fg-STP在2006年Spec基准测试中,在中型和小型2核CMP上,单线程速度比类似的纯硬件方法(如Core Fusion)平均提高18%和7%。
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Fg-STP: Fine-Grain Single Thread Partitioning on Multicores
Power and complexity issues have led the microprocessor industry to shift to Chip Multiprocessors in order to be able to better utilize the additional transistors ensured by Moore's law. While parallel programs are going to be able to take most of the advantage of these CMPs, single thread applications are not equipped to benefit from them. In this paper we propose Fine-Grain Single-Thread Partitioning (Fg-STP), a hardware-only scheme that takes advantage of CMP designs to speedup single-threaded applications. Our proposal improves single thread performance by reconfiguring two cores with the aim of collaborating on the fetching and execution of the instructions. These cores are basically conventional out-of-order cores in which execution is orchestrated using a dedicated hardware that has minimum and localized impact on the original design of the cores. This approach partitions the code at instruction granularity and differs from previous proposals on the extensive use of dependence speculation, replication and communication. These features are combined with the ability to look for parallelism on large instruction windows without any software intervention (no re-compilation or profiling hints are needed). These characteristics allow Fg-STP to speedup single thread by 18% and 7% on average over similar hardware-only approaches like Core Fusion, on medium sized and small sized 2-core CMP respectively for Spec 2006 benchmarks.
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Safe and efficient supervised memory systems Keynote address II: How's the parallel computing revolution going? A case for guarded power gating for multi-core processors Fg-STP: Fine-Grain Single Thread Partitioning on Multicores A quantitative performance analysis model for GPU architectures
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