CPU和/或GPU：重新审视GPU与CPU的神话
CPU and/or GPU: Revisiting the GPU Vs. CPU Myth
摘要：
使用加速器的并行计算在过去几年中获得了广泛的研究关注。特别是，使用GPU进行通用计算已经在时间、成本、功耗和其他指标方面取得了一些成功。然而，基于加速器的计算已经显著地降低了CPU在计算中的作用。随着CPU的发展，也提供了匹配的计算资源，在计算中也包括CPU是很重要的。我们称之为混合计算模型。实际上，当代的大多数计算机系统提供一定程度的异质性，因此这样的模型是相当自然的。我们重新评估的索赔最近的一份文件，李等人。(ISCA 2010年）。我们认为Lee等人（ISCA 2010）提出的正确问题应该是如何有效地使用CPU+GPU平台，而不是应该专门使用CPU还是GPU。为此，我们实验了一组13个不同的工作负载，从数据库，图像处理，稀疏矩阵内核和图形。我们使用两种不同的混合平台进行实验：一种由6核Intel i7- 980 X CPU和NVidia Tesla T10 GPU组成，另一种由Intel E7400双核CPU和NVidia GT 520 GPU组成。在这两个平台上，我们表明，混合解决方案提供了良好的优势，CPU或GPU单独的解决方案。在这两个平台上，我们还证明了我们的解决方案平均具有90%的资源效率。因此，我们的工作表明，混合计算不仅可以在研究规模的平台上提供巨大的优势，而且可以在更现实的规模系统中为大规模用户社区提供显着的性能增益和资源效率。
英文摘要：
Parallel computing using accelerators has gained widespread research attention in the past few years. In particular, using GPUs for general purpose computing has brought forth several success stories with respect to time taken, cost, power, and other metrics. However, accelerator based computing has signifi- cantly relegated the role of CPUs in computation. As CPUs evolve and also offer matching computational resources, it is important to also include CPUs in the computation. We call this the hybrid computing model. Indeed, most computer systems of the present age offer a degree of heterogeneity and therefore such a model is quite natural. We reevaluate the claim of a recent paper by Lee et al.(ISCA 2010). We argue that the right question arising out of Lee et al. (ISCA 2010) should be how to use a CPU+GPU platform efficiently, instead of whether one should use a CPU or a GPU exclusively. To this end, we experiment with a set of 13 diverse workloads ranging from databases, image processing, sparse matrix kernels, and graphs. We experiment with two different hybrid platforms: one consisting of a 6-core Intel i7-980X CPU and an NVidia Tesla T10 GPU, and another consisting of an Intel E7400 dual core CPU with an NVidia GT520 GPU. On both these platforms, we show that hybrid solutions offer good advantage over CPU or GPU alone solutions. On both these platforms, we also show that our solutions are 90% resource efficient on average. Our work therefore suggests that hybrid computing can offer tremendous advantages at not only research-scale platforms but also the more realistic scale systems with significant performance gains and resource efficiency to the large scale user community.
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