Towards Efficient OpenMP Strategies for Non-Uniform Architectures

Parallel processing is considered as todays and future trend for improving performance of computers. Computing devices ranging from small embedded systems to big clusters of computers rely on parallelizing applications to reduce execution time. Many of current computing systems rely on Non-Uniform Memory Access (NUMA) based processors architectures. In these architectures, analyzing and considering the non-uniformity is of high importance for improving scalability of systems. In this paper, we analyze and develop a NUMA based approach for the OpenMP parallel programming model. Our technique applies a smart threads allocation method and an advanced tasks scheduling strategy for reducing remote memory accesses and consequently their extra time consumption. We implemented our approach within the NANOS runtime system. A set of tests was conducted using the BOTS benchmarks and results showed the capacity of our technique in improving the performance of OpenMP applications especially those dealing with a large amount of data.