CHash: A High Cost-Performance Hash Design for CXL-based Disaggregated Memory System

The exponential growth in demand for high-performance computing systems has created an urgent requirement for innovative memory technologies that can provide higher bandwidth and enhanced capacity scalability. In particular, Compute Express Link (CXL) has emerged as a promising solution for memory expansion and system acceleration. Hash-based index structures are widely recognized as fundamental components of in-memory database systems, and they are commonly used for indexing in-memory key-value stores due to their capability for rapid lookup performance. How to design and maintain a hash index structure in a CXLbased disaggregated memory system, with comprehensive consolidation, is a challenging topic of in-depth research. In this paper, we conduct extensive experiments on two real CXL memory devices based on the 4 th -generation Intel® Xeon® Scalable Processor with CXL 1.0 support. Specifically, we run various microbenchmarks not only to evaluate the performance characteristics of CXL memory devices, but also to measure the performance impact of different memory allocation methods. We observe significant performance disparities and allocation inefficiencies, motivating us to innovate the hash design in the CXL-based disaggregated memory system. Lastly, we propose CHash, a highly efficient hashing scheme that adopts a DRAM-CXL memory hybrid storage mechanism. CHash maintains components across different media by carefully designing the hash structure to fully exploit the features of CXL memory devices. Furthermore, CHash implements several acceleration techniques to speed up the key-value (KV) record searches. Some optimizations, including stage buckets, two-level filtering, hashing multiplexing and so on, are also further adopted to improve the overall performance. Extensive experimental results validate the efficiency of CHash in different CXL memory devices, demonstrating that it achieves a 2.2× to 9.4× speedup for insertions, and a 1.2× to 4.5× speedup for lookups, all the while maintaining a high load factor (~90%), compared to state-of-the-art hashing schemes.