Graph partitioning applied to DAG scheduling to reduce NUMA effects
Document typeConference lecture
PublisherAssociation for Computing Machinery (ACM)
Rights accessOpen Access
European Commisision's projectEC-FP7-321253
The complexity of shared memory systems is becoming more relevant as the number of memory domains increases, with different access latencies and bandwidth rates depending on the proximity between the cores and the devices containing the data. In this context, techniques to manage and mitigate non-uniform memory access (NUMA) effects consist in migrating threads, memory pages or both and are typically applied by the system software. We propose techniques at the runtime system level to reduce NUMA effects on parallel applications. We leverage runtime system metadata in terms of a task dependency graph. Our approach, based on graph partitioning methods, is able to provide parallel performance improvements of 1.12X on average with respect to the state-of-the-art.
CitationSánchez Barrera, I., Casas, M., Moreto, M., Ayguade, E., Labarta, J., Valero, M. Graph partitioning applied to DAG scheduling to reduce NUMA effects. A: ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming. "PPoPP '18 Proceedings of the 23rd ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming: February 24-28, 2018: Vienna, Austria". New York: Association for Computing Machinery (ACM), 2018, p. 419-420.