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×.

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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
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article

H-Rocks: CPU-GPU accelerated Heterogeneous RocksDB on Persistent Memory

Shweta Pandey, Arkaprava Basu
2 citations
Proceedings of the ACM on Management of Data
Distributed and Parallel Computing Systems
2.97
article

H-Rocks: CPU-GPU accelerated Heterogeneous RocksDB on Persistent Memory

Shweta Pandey, Arkaprava Basu
article en
2 citations

Abstract

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×.

Proceedings of the ACM on Management of DataVol. 3(1)
Indian Institute of Science Bangalore (IN)
Openalex Percentile: Top 10%
Distributed and Parallel Computing Systems
2.97
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H-Rocks: CPU-GPU accelerated Heterogeneous RocksDB on Persistent Memory — Shweta Pandey, Arkaprava Basu · Proceedings of the ACM on Management of Data (2025) | TGRS Research Map | TGRS