将数据从HDD流式传输到GPU，以保持峰值性能
Streaming Data from HDD to GPUs for Sustained Peak Performance
摘要：
在全基因组关联研究（GWAS）的背景下，人们必须解决长序列的广义最小二乘问题;这样的任务有两个限制因素：执行时间-通常在几天或几周的范围内-和数据管理-数据集在TB级。我们提出了一个算法，避免这两个问题。通过流水线计算，并感谢一个复杂的传输策略，我们流数据从硬盘到主内存到GPU，并实现持续的峰值性能;相对于一个高度优化的CPU实现，我们的算法显示了2.6倍的加速比。此外，该方法适用于多个GPU，并获得几乎完美的可扩展性。当使用4个GPU时，我们观察到上述实现的加速比为9倍，而在广泛的生物学库中为488倍。
英文摘要：
In the context of the genome-wide association studies (GWAS), one has to solve long sequences of generalized least-squares problems; such a task has two limiting factors: execution time --often in the range of days or weeks-- and data management --data sets in the order of Terabytes. We present an algorithm that obviates both issues. By pipelining the computation, and thanks to a sophisticated transfer strategy, we stream data from hard disk to main memory to GPUs and achieve sustained peak performance; with respect to a highly-optimized CPU implementation, our algorithm shows a speedup of 2.6x. Moreover, the approach lends itself to multiple GPUs and attains almost perfect scalability. When using 4 GPUs, we observe speedups of 9x over the aforementioned implementation, and 488x over a widespread biology library.
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