Graph partitioning applied to DAG scheduling to reduce NUMA effects

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.