硅光子学使机架级多核系统成为可能

Peng Yang, Zhehui Wang, Zhifei Wang, Xuanqi Chen, Luan H. K. Duong, Jiang Xu
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引用次数: 0

摘要

科学计算、大数据处理和深度学习对计算能力的要求越来越高,推动了百亿亿次计算系统的出现。成千上万甚至更多的核心节点被连接起来构建这样的系统。它对系统的不同方面提出了巨大的性能和功率挑战。作为高性能计算系统的基础模块,模块化机架将在解决这些挑战方面发挥重要作用。在本章中,我们介绍了机架级光网络(RSON),这是一种用于机架级多核系统的硅光子学芯片间/芯片内网络。RSON利用了大多数流量在机架内的事实,高带宽和低延迟的机架级光网络可以提高性能和能源效率。我们共同设计了片内和片间光网络以及光节点间接口,为本地存储器和远程存储器提供均衡的数据访问,使机架内的节点能够有效地协同工作。评价结果表明,RSON可显著提高系统的综合性能和能效。具体来说,与传统的InfiniBand连接机架相比,在相同的能耗下,RSON可以提供高达5.4倍的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Silicon photonics enabled rack-scale many-core systems
The increasingly higher demands on computing power from scientific computations, big data processing and deep learning are pushing the emergence of exascale computing systems. Tens of thousands of or even more manycore nodes are connected to build such systems. It imposes huge performance and power challenges on different aspects of the systems. As a basic block in high-performance computing systems, modularized rack will play a significant role in addressing these challenges. In this chapter, we introduce rack-scale optical networks (RSON), a silicon photonics enabled inter/intra-chip network for rack-scale many-core systems. RSON leverages the fact that most traffic is within rack and the high bandwidth and low-latency rack-scale optical network can improve both performance and energy efficiency. We codesign the intra-chip and inter-chip optical networks together with optical internode interface to provide balanced data access to both local memory and remote note's memory, making the nodes within rack cooperate effectively. The evaluations show that RSON can improve the overall performance and energy efficiency dramatically. Specifically, RSON can deliver as much as 5.4x more performance under the same energy consumption compared to traditional InfiniBand connected rack.
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