{"title":"Hypersort:在支持hbm的FPGA上进行高性能并行排序","authors":"Soundarya Jayaraman, Bingyi Zhang, V. Prasanna","doi":"10.1109/ICFPT56656.2022.9974209","DOIUrl":null,"url":null,"abstract":"Accelerating sorting on FPGA has been extensively studied by leveraging the fine-grained data parallelism of FPGAs. However, with the optimized hardware pipelines, the performance of sorting algorithms is bounded by the off-chip memory band-width. The integration of high-bandwidth memory (HBM) on FPGAs offers significantly more off-chip memory bandwidth compared with traditional DDR memory, which enables new opportunities for accelerating sorting. In this paper, we develop Hypersort, a hardware accelerator to accelerate sorting on HBM-enabled FPGA. We use columnsort to merge HBM channels. To support the data communication pat-terns of Columnsort, we propose several optimizations to reduce external memory (HBM) traffic and hide data communication latency to further improve the overall throughput. We implement our accelerator on a state-of-the-art HBM-enabled FPGA. Ex-perimental results show that our implementation achieves overall sorting throughput of 34 GB/s, which is up to 14.8×, 4.73× and 2.18 ×faster than the state-of-the-art implementations on CPU, FPGA with external DDR and HBM-enabled FPGA, respectively. The proposed approach demonstrates higher efficiency for merging sorted arrays in HBM channels compared with the state-of-the-art implementation on HBM-enabled FPGA.","PeriodicalId":239314,"journal":{"name":"2022 International Conference on Field-Programmable Technology (ICFPT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Hypersort: High-performance Parallel Sorting on HBM-enabled FPGA\",\"authors\":\"Soundarya Jayaraman, Bingyi Zhang, V. Prasanna\",\"doi\":\"10.1109/ICFPT56656.2022.9974209\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Accelerating sorting on FPGA has been extensively studied by leveraging the fine-grained data parallelism of FPGAs. However, with the optimized hardware pipelines, the performance of sorting algorithms is bounded by the off-chip memory band-width. The integration of high-bandwidth memory (HBM) on FPGAs offers significantly more off-chip memory bandwidth compared with traditional DDR memory, which enables new opportunities for accelerating sorting. In this paper, we develop Hypersort, a hardware accelerator to accelerate sorting on HBM-enabled FPGA. We use columnsort to merge HBM channels. To support the data communication pat-terns of Columnsort, we propose several optimizations to reduce external memory (HBM) traffic and hide data communication latency to further improve the overall throughput. We implement our accelerator on a state-of-the-art HBM-enabled FPGA. Ex-perimental results show that our implementation achieves overall sorting throughput of 34 GB/s, which is up to 14.8×, 4.73× and 2.18 ×faster than the state-of-the-art implementations on CPU, FPGA with external DDR and HBM-enabled FPGA, respectively. The proposed approach demonstrates higher efficiency for merging sorted arrays in HBM channels compared with the state-of-the-art implementation on HBM-enabled FPGA.\",\"PeriodicalId\":239314,\"journal\":{\"name\":\"2022 International Conference on Field-Programmable Technology (ICFPT)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-12-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 International Conference on Field-Programmable Technology (ICFPT)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICFPT56656.2022.9974209\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 International Conference on Field-Programmable Technology (ICFPT)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICFPT56656.2022.9974209","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Hypersort: High-performance Parallel Sorting on HBM-enabled FPGA
Accelerating sorting on FPGA has been extensively studied by leveraging the fine-grained data parallelism of FPGAs. However, with the optimized hardware pipelines, the performance of sorting algorithms is bounded by the off-chip memory band-width. The integration of high-bandwidth memory (HBM) on FPGAs offers significantly more off-chip memory bandwidth compared with traditional DDR memory, which enables new opportunities for accelerating sorting. In this paper, we develop Hypersort, a hardware accelerator to accelerate sorting on HBM-enabled FPGA. We use columnsort to merge HBM channels. To support the data communication pat-terns of Columnsort, we propose several optimizations to reduce external memory (HBM) traffic and hide data communication latency to further improve the overall throughput. We implement our accelerator on a state-of-the-art HBM-enabled FPGA. Ex-perimental results show that our implementation achieves overall sorting throughput of 34 GB/s, which is up to 14.8×, 4.73× and 2.18 ×faster than the state-of-the-art implementations on CPU, FPGA with external DDR and HBM-enabled FPGA, respectively. The proposed approach demonstrates higher efficiency for merging sorted arrays in HBM channels compared with the state-of-the-art implementation on HBM-enabled FPGA.