GOLAP: A GPU-in-Data-Path Architecture for High-Speed OLAP
In this paper, we suggest a novel GPU-in-data-path architecture that leverages a GPU to accelerate the I/O path and thus can achieve almost in-memory bandwidth using SSDs. In this architecture, the main idea is to stream data in heavy-weight compressed blocks from SSDs directly into the GPU and decompress it on-the-fly as part of the table scan to inflate data before processing it by downstream query operators. Furthermore, we employ novel GPU-optimized pruning techniques that help us further inflate the perceived read bandwidth. In our evaluation, we show that the GPU-in-data-path architecture can achieve an effective bandwidth of up to 100 GiB/s, surpassing existing in-memory systems' capabilities.
Authors
- Nils Boeschen (ORCID: https://orcid.org/0000-0001-8654-5738)
- Carsten Binnig (ORCID: https://orcid.org/0000-0002-2744-7836)
- Tobias Ziegler (ORCID: https://orcid.org/0000-0002-1602-4512)
Institutions
- Hess (United States) (US)
- Technische Universität Darmstadt (DE)
Publication Details
- Journal
- Proceedings of the ACM on Management of Data
- Published
- 2024-12-18
- DOI
- https://doi.org/10.1145/3698812
- Citations
- 6
- Primary Topic
- Advanced Data Storage Technologies
- Type
- article
- Field-Weighted Citation Impact
- 2.09