While multicore architectures are used in the whole product range from server systems to handheld computers, the deployed software still undergoes the slow transition from sequential to parallel. This transition, however, is gaining more and more momentum due to the increased availability of more sophisticated parallel programming environments. Combined with the ever increasing complexity of multicore architectures, this results in a scheduling problem that is different from what it has been, because concurrently executing parallel programs and features such as non-uniform memory access, shared caches, or simultaneous multithreading have to be considered. In this paper, we compare different ways of scheduling multiple parallel applications on multicore architectures. Due to emerging parallel programming environments, we primarily consider applications where the parallelism degree can be changed on the fly. We propose TACO, a topology-aware scheduling scheme that combines equipartitioning and coscheduling, which does not suffer from the drawbacks of the individual concepts. Additionally, TACO is conceptually compatible with contention-aware scheduling strategies. We find that topology-awareness increases performance for all evaluated workloads. The combination with coscheduling is more sensitive towards the executed workloads and NUMA effects. However, the gained versatility allows new use cases to be explored, which were not possible before.
Performance Comparison of Parallel Programming Environments for Implementing AIAC Algorithms