Gengchen Liu, R. Proietti, Marjan Fariborz, P. Fotouhi, Xian Xiao, S. Yoo
{"title":"Architecture and Performance Studies of 3D-Hyper-FleX-LION for Reconfigurable All-to-All HPC Networks","authors":"Gengchen Liu, R. Proietti, Marjan Fariborz, P. Fotouhi, Xian Xiao, S. Yoo","doi":"10.1109/SC41405.2020.00030","DOIUrl":null,"url":null,"abstract":"While the Fat-Tree network topology represents the dominant state-of-art solution for large-scale HPC networks, its scalability in terms of power, latency, complexity, and cost is significantly challenged by the ever-increasing communication bandwidth among tens of thousands of heterogeneous computing nodes. We propose 3D-Hyper-FleX-LION, a flat hybrid electronic-photonic interconnect network that leverages the multichannel nature of modern multi-terabit switch ASICs (with 100 Gb/s granularity) and a reconfigurable all-to-all photonic fabric called Flex-LIONS. Compared to a Fat-Tree network interconnecting the same number of nodes and with the same oversubscription ratio, the proposed 3D-Hyper-FleX-LION offers a 20% smaller diameter, $3\\times$ lower power consumption, $10 \\times$ fewer cable connections, and $4 \\times$ reduction in the number of transceivers. When bandwidth reconfiguration capabilities of Flex-LIONS are exploited for non-uniform traffic workloads, simulation results indicate that 3D-Hyper-FleX-LION can achieve up to $4 \\times$ improvement in energy efficiency for synthetic traffic workloads with high locality compared to Fat-Tree.","PeriodicalId":424429,"journal":{"name":"SC20: International Conference for High Performance Computing, Networking, Storage and Analysis","volume":"4 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"13","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"SC20: International Conference for High Performance Computing, Networking, Storage and Analysis","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SC41405.2020.00030","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 13
Abstract
While the Fat-Tree network topology represents the dominant state-of-art solution for large-scale HPC networks, its scalability in terms of power, latency, complexity, and cost is significantly challenged by the ever-increasing communication bandwidth among tens of thousands of heterogeneous computing nodes. We propose 3D-Hyper-FleX-LION, a flat hybrid electronic-photonic interconnect network that leverages the multichannel nature of modern multi-terabit switch ASICs (with 100 Gb/s granularity) and a reconfigurable all-to-all photonic fabric called Flex-LIONS. Compared to a Fat-Tree network interconnecting the same number of nodes and with the same oversubscription ratio, the proposed 3D-Hyper-FleX-LION offers a 20% smaller diameter, $3\times$ lower power consumption, $10 \times$ fewer cable connections, and $4 \times$ reduction in the number of transceivers. When bandwidth reconfiguration capabilities of Flex-LIONS are exploited for non-uniform traffic workloads, simulation results indicate that 3D-Hyper-FleX-LION can achieve up to $4 \times$ improvement in energy efficiency for synthetic traffic workloads with high locality compared to Fat-Tree.