W. Qiao, Zhenman Fang, Mau-Chung Frank Chang, J. Cong
{"title":"An FPGA-Based BWT Accelerator for Bzip2 Data Compression","authors":"W. Qiao, Zhenman Fang, Mau-Chung Frank Chang, J. Cong","doi":"10.1109/FCCM.2019.00023","DOIUrl":null,"url":null,"abstract":"The Burrows-Wheeler Transform (BWT) has played an important role in lossless data compression algorithms. To achieve a good compression ratio, the BWT block size needs to be several hundreds of kilobytes, which requires a large amount of on-chip memory resources and limits effective hardware implementations. In this paper, we analyze the bottleneck of the BWT acceleration and present a novel design to map the anti-sequential suffix sorting algorithm to FPGAs. Our design can perform BWT with a block size of up to 500KB (i.e., bzip2 level 5 compression) on the Xilinx Virtex UltraScale+ VCU1525 board, while the state-of-art FPGA implementation can only support 4KB block size. Experiments show our FPGA design can achieve ~2x speedup compared to the best CPU implementation using standard large Corpus benchmarks.","PeriodicalId":116955,"journal":{"name":"2019 IEEE 27th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"13","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE 27th Annual International Symposium on Field-Programmable Custom Computing Machines (FCCM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/FCCM.2019.00023","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
The Burrows-Wheeler Transform (BWT) has played an important role in lossless data compression algorithms. To achieve a good compression ratio, the BWT block size needs to be several hundreds of kilobytes, which requires a large amount of on-chip memory resources and limits effective hardware implementations. In this paper, we analyze the bottleneck of the BWT acceleration and present a novel design to map the anti-sequential suffix sorting algorithm to FPGAs. Our design can perform BWT with a block size of up to 500KB (i.e., bzip2 level 5 compression) on the Xilinx Virtex UltraScale+ VCU1525 board, while the state-of-art FPGA implementation can only support 4KB block size. Experiments show our FPGA design can achieve ~2x speedup compared to the best CPU implementation using standard large Corpus benchmarks.