SIGMOD2025
H-Rocks: CPU-GPU accelerated Heterogeneous RocksDB on Persistent Memory
Shweta Pandey, Arkaprava Basu
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×.