Range-top-k queries retrieve the top-k elements within an arbitrary subrange of a large array and are a key primitive in real-time analytics. Unlike one-shot top-k selection, practical deployments issue large volumes of queries over varying and often overlapping ranges, frequently interleaved with streaming updates. In this setting, applying conventional GPU top-k kernels per query is inefficient: each query triggers range rescans or O(n)-scale passes that overwhelm HBM bandwidth, thrash on-chip caches, and provide little reuse across overlapping windows.

Memory disaggregation has become an inexorable trend in data centers and the cloud. By physically separating compute and memory resources into independent pools and connecting them with high-speed networks, memory disaggregation enables high resource utilization and elastic resource scaling. However, traditional tree-based indexes become inefficient on disaggregated memory: (1) large tree nodes can easily saturate network bandwidth due to I/O amplification, yet small tree nodes increase network round trips; (2) expensive concurrency control schemes restrict the scalability.

LSM-based storage systems are widely used for superior write performance on block devices. However, they currently fail to efficiently support secondary indexing, since a secondary index query operation usually needs to retrieve multiple small values, which scatter in multiple LSM components. In this work, we revisit secondary indexing in LSM-based storage systems with byte-addressable persistent memory (PM). Existing PM-based indexes are not directly competent for efficient secondary indexing. We propose Perseid , an efficient PM-based secondary indexing mechanism for LSM-based storage systems, which takes into account both characteristics of PM and secondary indexing. Perseid consists of (1) a specifically designed secondary index structure that achieves high-performance insertion and query, (2) a lightweight hybrid PM-DRAM and hash-based validation approach to filter out obsolete values with subtle overhead, and (3) two adapted optimizations on primary table searching issued from secondary indexes to accelerate non-index-only queries. Our evaluation shows that Perseid outperforms existing PM-based indexes by 3–7× and achieves about two orders of magnitude performance of state-of-the-art LSM-based secondary indexing techniques even if on PM instead of disks.