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引用次数: 2
摘要
现代微处理器设计的复杂性涉及数十亿个晶体管在越来越密集的尺度上,特别是在设计可靠性和可预测的产量方面带来了许多挑战。IBM Austin Research Lab的研究人员越来越依赖于基于软件的设计和制造过程各个方面的模拟来帮助解决这些挑战。这些模拟的计算复杂性和规模导致了高性能混合计算集群应用的探索,以加速设计过程。目前,使用的混合集群主要由商用工作站和服务器组成,这些工作站和服务器结合了基于nvidia的商用GPU图形卡和TESLA GPU计算加速器。我们还试验了由通用服务器和PowerXcell加速器组成的刀片集群,利用Cell处理器的计算吞吐量。在本文中,我们将详细介绍在这些混合集群平台上加速工作负载的经验。我们将讨论将混合运行时(如CUDA)与MPI相结合以解决集群计算的初始方法。我们还将描述我们正在开发的自定义集群混合基础设施,以解决MPI和其他传统集群工具在处理混合计算环境时存在的一些明显缺点。
The complexity of modern microprocessor design involving billions of transistors at increasingly denser scales creates many challenges particularly in the area of design reliability and predictable yields. Researchers at IBM's Austin Research Lab have increasingly depended on software based simulation of various aspects of the design and manufacturing process to help address these challenges. The computational complexity and sheer scale of these simulations have lead to the exploration of the application of high-performance hybrid computing clusters to accelerate the design process. Currently, the hybrid clusters in use are composed primarily of commodity workstations and servers incorporating commodity NVIDIA-based GPU graphics cards and TESLA GPU computational accelerators. We have also been experimenting with blade clusters composed of both general purpose servers and PowerXcell accelerators leveraging the computational throughput of the Cell processor. In this paper we will detail our experiences with accelerating our workloads on these hybrid cluster platforms. We will discuss our initial approach of combining hybrid runtimes such as CUDA with MPI to address cluster computation. We will also describe a custom cluster hybrid infrastructure we are developing to deal with some of the perceived shortcomings of MPI and other traditional cluster tools when dealing with hybrid computing environments.