2025/12/02 by Zheng, Haoyu, Jie Ren, Gao, Shouwei +3
Computer Science · #Advanced Data Storage Technologies #Cloud Computing and Resource Management #Distributed #FOS: Computer and information sciences #Parallel #Parallel Computing and Optimization Techniques #and Cluster Computing (cs.DC)
paper · pdf · doi:10.48550/arxiv.2512.02300
openalex publication_date 2025/12/02 · openalex created_date 2025/12/04 · openalex updated_date 2026/07/28
Memory disaggregation is promising to scale memory capacity and improves utilization in HPC systems. However, the performance overhead of accessing remote memory poses a significant challenge, particularly for compute-intensive HPC applications where execution times are highly sensitive to data locality. In this work, we present DOLMA, a Data Object Level M emory dis Aggregation framework designed for HPC applications. DOLMA intelligently identifies and offloads data objects to remote memory, while providing quantitative analysis to decide a suitable local memory size. Furthermore, DOLMA leverages the predictable memory access patterns typical in HPC applications and enables remote memory prefetch via a dual-buffer design. By carefully balancing local and remote memory usage and maintaining multi-thread concurrency, DOLMA provides a flexible and efficient solution for leveraging disaggregated memory in HPC domains while minimally compromising application performance. Evaluating with eight HPC workloads and computational kernels, DOLMA limits performance degradation to less than 16% while reducing local memory usage by up to 63%, on average.