LUDA：通过GPU增强LSM键值存储压缩
LUDA: Boost LSM Key Value Store Compactions with GPUs
摘要：
基于日志结构化合并（Log-Structured-Merge，LSM）树的键值存储正面临充分利用底层存储设备的显著性能改进的关键挑战，这使得LSM键值存储的压缩操作变得受CPU限制，并且缓慢的压缩显著降低键值存储性能。为了解决这个问题，我们提出了LUDA，一个带有CUDA的LSM键值存储，它使用GPU来加速LSM键值存储的压缩操作。如何有效地并行压缩程序，以及适应GPU架构的最佳性能合同的挑战LUDA。具体来说，LUDA克服了这些挑战，利用压缩过程之间的数据独立性，并使用合作排序机制和明智的数据移动。在不同CPU开销水平下运行在商用GPU上，评估结果显示LUDA提供了高达2倍的吞吐量和2倍的数据处理速度，并实现了比LevelDB和RocksDB更稳定的第99百分位延迟。
英文摘要：
Log-Structured-Merge (LSM) tree-based key value stores are facing critical challenges of fully leveraging the dramatic performance improvements of the underlying storage devices, which makes the compaction operations of LSM key value stores become CPU-bound, and slow compactions significantly degrade key value store performance. To address this issue, we propose LUDA, an LSM key value store with CUDA, which uses a GPU to accelerate compaction operations of LSM key value stores. How to efficiently parallelize compaction procedures as well as accommodate the optimal performance contract of the GPU architecture challenge LUDA. Specifically, LUDA overcomes these challenges by exploiting the data independence between compaction procedures and using cooperative sort mechanism and judicious data movements. Running on a commodity GPU under different levels of CPU overhead, evaluation results show that LUDA provides up to 2x higher throughput and 2x data processing speed, and achieves more stable 99th percentile latencies than LevelDB and RocksDB.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
