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gParaKV: A GPGPU-Accelerated Key-Value Separation-Based KV Store with Optimized Compaction and Garbage Collection

SessionData Analytics, Visualization, and Storage

DescriptionLSM tree-based key-value stores are widely deployed in modern cloud storage systems thanks to high data storage efficiency and retrieval capabilities. The compaction in the LSM tree, however, results in severe performance bottlenecks, especially in large-sized value cases. While key-value separation methods mitigate the performance bottlenecks caused by compaction, the existing methods do not fully address merge-sorting during compaction and expensive garbage collection (GC). We propose gParaKV, a GPGPU-empowered KV store with a KV separation mechanism, leveraging the GPGPU parallel technology to accelerate merge-sorting in compaction and GC. gParaKV embraces a GPGPU bitmap structure, parallel data marking, and a parallel GC mechanism. These critical components curtail the overhead of merge-sorting and GC by virtue of parallel computing. We compare it with state-of-the-art KV stores under various workloads. The experimental results show that gParaKV can improve the write performance and GC efficiency compared to existing key-value separation-based KV stores.

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