H-Rocks: CPU-GPU accelerated Heterogeneous RocksDB on Persistent Memory
Persistent key-value stores (pKVS) such as RocksDB are critical to many internet-scale services. Recent works leveraged persistent memory (PM) to improve pKVS throughput. However, they are typically limited to CPUs. We develop H-Rocks to judiciously leverage both the CPU and the Graphics Processing Unit (GPU) for accelerating a wide range of RocksDB operations. H-Rocks selectively accelerates performance-critical parts of RocksDB on the GPU. It uses operation sub-batching and key-value versioning to leverage GPU's parallelism while maintaining compatibility with RocksDB. It harnesses GPU's high-bandwidth memory while limiting data movement between the CPU and GPU. In YCSB workloads, H-Rocks outperforms CPU-based pKVSs like Viper, Plush, and pmem-RocksDB by 3-18×.
Authors
- Shweta Pandey (ORCID: https://orcid.org/0000-0003-1358-6787)
- Arkaprava Basu (ORCID: https://orcid.org/0000-0002-3466-4556)
Institutions
- Indian Institute of Science Bangalore (IN)
Publication Details
- Journal
- Proceedings of the ACM on Management of Data
- Published
- 2025-02-10
- DOI
- https://doi.org/10.1145/3709694
- Citations
- 2
- Primary Topic
- Distributed and Parallel Computing Systems
- Type
- article
- Field-Weighted Citation Impact
- 2.97