TieredHM: Hotspot-Optimized Hash Indexing for Memory Semantic SSD Based Hybrid Memory

Memory semantic Solid State Drives (MS-SSDs) provide a promising opportunity to enable the hybrid memory architecture (HMA). The memory semantic interface enables the CPUs to directly access structured data in SSDs and eliminate bulk data copy/swap between the memory and storage devices. However, existing hash indexings issue many random writes, resulting in two problems when directly deployed on MS-SSD-based HMA: 1) Highly random traffic persisted to the underlying NAND flash of MS-SSDs incurs significant garbage collection (GC) overhead. 2) Placing frequently updated memory pages of hash indexings in persistent memories (PMs) is anticipated to reduce write latency, failing to work effectively due to the lack of skewness. To address the above problems, we propose a novel MS-SSD-friendly hash indexing scheme called TieredHM. It employs a multi-layer structure and opportunistic data movement (ODM) to construct skewed writes. Hence, the MS-SSD can transform the writes into multi-streamed writes, separating data with different update frequencies to reduce GC overhead. Besides, since the top layer is updated much more frequently (more skewed) than other layers, placing the top layer of TieredHM into persistent memory can significantly reduce write latency. TieredHM further leverages a prefetch mechanism based on the internal parallelism of NAND flash to reduce search overhead incurred by ODM. Experimental results show that TieredHM reduces the average write latency and GC overhead by up to 8.3X and 20.0X compared to state-of-the-art hash indexings without sacrificing read performance.