Accelerating re-pair compression using FPGAs

Lasch, Robert; Demirsoy, S.S.; May, Norman; Ramamurthy, V.; Färber, C.; Sattler, Kai-Uwe

doi:10.1145/3399666.3399931

Cited by 2 publications

(1 citation statement)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Key-value stores also benefit from FPGAs with new memcached architectures [11], native computational storage [74], and hardware-managed transactions [36]. Kara et al [41] show how FPGAs can be used for coupling column-store ML algorithms with on-the-fly data transformation, such as decryption and delta-encoding decompression, while Lasch et al [43] accelerate Re-Pair compression algorithm using FPGAs.…”

Section: Hardware Acceleration For Databasesmentioning

confidence: 99%

Hardware acceleration of compression and encryption in SAP HANA

et al. 2022

Self Cite

View full text Add to dashboard Cite

With the advent of cloud computing, where computational resources are expensive and data movement needs to be secured and minimized, database management systems need to reconsider their architecture to accommodate such requirements. In this paper, we present our analysis, design and evaluation of an FPGA-based hardware accelerator for offloading compression and encryption for SAP HANA, SAP's Software-as-a-Service (SaaS) in-memory database. Firstly, we identify expensive data-transformation operations in the I/O path. Then we present the design details of a system consisting of compression followed by different types of encryption to accommodate different security levels, and identify which combinations maximize performance. We also analyze the performance benefits of offloading decryption to the FPGA followed by decompression on the CPU. The experimental evaluation using SAP HANA traces shows that analytical engines can benefit from FPGA hardware offloading. The results identify a number of important trade-offs (e.g., the system can accommodate low-latency secured transactions to high-performance use cases or offer lower storage cost by also compressing payloads for less critical use cases), and provide valuable information to researchers and practitioners exploring the nascent space of hardware accelerators for database engines.

show abstract

Section: Hardware Acceleration For Databasesmentioning

confidence: 99%