4.7 Article

Silicon Photonic Flex-LIONS for Reconfigurable Multi-GPU Systems

期刊

JOURNAL OF LIGHTWAVE TECHNOLOGY
卷 39, 期 4, 页码 1212-1220

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JLT.2021.3052713

关键词

Multi-GPU systems; optical reconfiguration; optical switching; silicon photonics

资金

  1. ARO [W911NF1910470]
  2. DoD [H98230-19-C-0209]
  3. NSF ECCS [1611560]
  4. DoE UAI Consortium [DE-SC0019582, DE-SC0019526, DE-SC0019692]
  5. U.S. Department of Defense (DOD) [W911NF1910470] Funding Source: U.S. Department of Defense (DOD)
  6. U.S. Department of Energy (DOE) [DE-SC0019582, DE-SC0019692, DE-SC0019526] Funding Source: U.S. Department of Energy (DOE)

向作者/读者索取更多资源

This study proposes a solution to interconnect multiple GPUs using Flex-LIONS optical technology, which can adapt the optical connectivity based on traffic demands, resulting in a reduction in execution time.
The rapid increases in data-intensive applications demand for more powerful parallel computing systems capable of parallel processing a large amount of data more efficiently and effectively. While GPU-based systems are commonly used in such parallel processing, the exponentially rising data volume can easily saturate the capacity of the largest possible GPU processor. One possible solution is to exploit multi-GPU systems. In a multi-GPU system, the main bottleneck is the interconnect, which is currently based on PCIe or NVLink technologies. In this study, we propose to optically interconnect multiple GPUs using Flex-LIONS, an optical all-to-all reconfigurable interconnect. By exploiting the multiple free spectral ranges (FSRs) of Flex-LIONS, it is passible to adapt (or steer) the inter-GPU connectivity to the traffic demands by reconfiguring the optical connectivity of one FSR while maintaining fixed all-to-all connectivity of another FSR. Simulation results show the benefits of the proposed reconfigurable bandwidth-steering interconnect solution under various traffic patterns of different applications. Execution time reductions by up to 5x have been demonstrated in this study including two applications of convolution and maxpooling.

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