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Filtering superfluous prefetches using density vectors

机译:使用密度向量过滤多余的预取

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A previous evaluation of scheduled region prefetching showed that this technique eliminates the bulk of main-memory stall time for applications with spatial locality. The downside to that aggressive prefetching scheme is that, even when it successfully improves performance, it increases enormously the amount of superfluous memory traffic generated by a program. In this paper, we measure the predictability of spatial locality using density vectors, bit vectors that track the block-level access pattern within a region of memory. We evaluate a number of policies that use density vector information to filter out prefetches that are unlikely to be useful. We show that, across our benchmarks, an average of 70% of useless prefetches can be eliminated with virtually no overall performance loss from reduced coverage. Thanks to the increase in prefetch accuracy, a few benchmarks show performance improvements as high as 35% over the base region prefetching scheme.
机译:先前对调度区域预取的评估表明,该技术消除了具有空间局部性的应用的大量主存储器失速时间。这种激进预取方案的缺点是,即使它成功提高了性能,它也会增加由程序产生的多余内存流量的量。在本文中,我们使用浓度向量,在存储区域内跟踪块级访问模式的比特向量来测量空间局部的可预测性。我们评估使用密度矢量信息的许多策略来过滤掉不太有用的预取。我们表明,在我们的基准中,平均占70%的无用预取量可以消除,并且几乎没有减少覆盖率的整体性能损失。由于预取准确度的增加,少数基准测试显示在基区预取方案上高达35%的性能提升。

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