PANA: A Fine-Grained Runtime-Adaptive Load Balancing for Parallel SpMV on Multicore CPUs
Abstract
SpMV has been widely utilized and is regarded as a significant kernel in various scientific and engineering computing applications, where its parallel performance is heavily influenced by matrix sparsity and hardware architecture. Despite extensive prior research, static partitioning strategies that narrowly target computation or memory access remain a key performance bottleneck, severely stifling performance advancement of SpMV on modern multicore CPUs.
Fields of Study & Subjects
Parallel Computing and Optimization Techniques
Interconnection Networks and Systems
Graph Theory and Algorithms
Publication details
- Journal / source
- Proceedings of the 31st ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming
- Publisher
- ACM
- Pages
- 95–108
- Publication date
- 28 January 2026