High-Throughput Lossless Compression on Tightly Coupled CPU-FPGA Platforms

Weikang Qiao, Jieqiong Du, Zhenman Fang, Michael Lo, Mau-Chung Chang, Jason Cong

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

19 Scopus citations

Abstract

Data compression techniques have been widely used to reduce data storage and movement overhead, especially in the big data era. While FPGAs are well suited to accelerate the computation-intensive lossless compression algorithms, big data compression with parallel requests intrinsically poses two challenges to the overall system throughput. First, scaling existing single-engine FPGA compression accelerator designs already encounters bottlenecks which will result in lower clock frequency, saturated throughput and lower area efficiency. Second, when such FPGA compression accelerators are integrated with the processors, the overall system throughput is typically limited by the communication between a CPU and an FPGA. We propose a novel multi-way parallel and fully pipelined architecture to achieve high-throughput lossless compression on modern Intel-Altera HARPv2 platforms. To compensate for the compression ratio loss in a multi-way design, we implement novel techniques, such as a better data feeding method and a hash chain to increase the hash dictionary history. Our accelerator kernel itself can achieve a compression throughput of 12.8 GB/s (2.3x better than the current record throughput) and a comparable compression ratio of 2.03 for standard benchmark data. Our approach enables design scalability without a reduction in clock frequency and also improves the performance per area efficiency (up to 1.5x). Moreover, we exploit the high CPU-FPGA communication bandwidth of HARPv2 platforms to improve the compression throughput of the overall system, which can achieve an average practical end-to-end throughput of 10.0 GB/s (up to 12 GB/s for larger input files) on HARPv2.

Original languageEnglish
Title of host publicationProceedings - 26th IEEE International Symposium on Field-Programmable Custom Computing Machines, FCCM 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages37-44
Number of pages8
ISBN (Electronic)9781538655221
DOIs
StatePublished - 7 Sep 2018
Event26th Annual IEEE International Symposium on Field-Programmable Custom Computing Machines, FCCM 2018 - Boulder, United States
Duration: 29 Apr 20181 May 2018

Publication series

NameProceedings - 26th IEEE International Symposium on Field-Programmable Custom Computing Machines, FCCM 2018

Conference

Conference26th Annual IEEE International Symposium on Field-Programmable Custom Computing Machines, FCCM 2018
CountryUnited States
CityBoulder
Period29/04/181/05/18

Keywords

  • Deflate
  • End to end Communication
  • Lossless Compression

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