PEPSC:用于科学计算的高能效处理器

Ganesh S. Dasika, Ankit Sethia, T. Mudge, S. Mahlke
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引用次数: 19

摘要

图形处理单元(GPU)计算能力的快速进步,以及这些设备的通用编程模型的部署,使台式超级计算机的愿景成为现实。现在有可能组装一个系统,在科学应用中提供几个TFLOPs的性能,而成本只有一台高端笔记本电脑。虽然这些设备已经明显地改变了计算的格局,但也出现了两个核心问题。首先,gpu是为图形应用程序设计和优化的,因此交付的性能远远低于更一般的科学和数学应用程序的峰值。其次,gpu是耗电设备,通常消耗100-300瓦,这限制了解决方案的可扩展性,并且需要昂贵的冷却。为了应对这些挑战,本文提出了PEPSC体系结构——一种为数据并行科学应用领域定制的体系结构,其中功率效率是中心焦点。PEPSC结合了二维单指令多数据(SIMD)数据路径、智能动态预取机制和可配置的SIMD控制方法,以提高传统gpu的执行效率。在执行现代科学应用时,单个PEPSC核心的峰值性能为120 GFLOPs,而功耗为2W,与现有gpu相比,计算效率提高了10倍以上。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
PEPSC: A Power-Efficient Processor for Scientific Computing
The rapid advancements in the computational capabilities of the graphics processing unit (GPU) as well as the deployment of general programming models for these devices have made the vision of a desktop supercomputer a reality. It is now possible to assemble a system that provides several TFLOPs of performance on scientific applications for the cost of a high-end laptop computer. While these devices have clearly changed the landscape of computing, there are two central problems that arise. First, GPUs are designed and optimized for graphics applications resulting in delivered performance that is far below peak for more general scientific and mathematical applications. Second, GPUs are power hungry devices that often consume 100-300 watts, which restricts the scalability of the solution and requires expensive cooling. To combat these challenges, this paper presents the PEPSC architecture -- an architecture customized for the domain of data parallel scientific applications where power-efficiency is the central focus. PEPSC utilizes a combination of a two-dimensional single-instruction multiple-data (SIMD) data path, an intelligent dynamic prefetching mechanism, and a configurable SIMD control approach to increase execution efficiency over conventional GPUs. A single PEPSC core has a peak performance of 120 GFLOPs while consuming 2W of power when executing modern scientific applications, which represents an increase in computation efficiency of more than 10X over existing GPUs.
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