Abstract-Currently, parallel platforms based on large scale hierarchical shared memory multiprocessors with NonUniform Memory Access (NUMA) are becoming a trend in scientific High Performance Computing (HPC). Due to their memory access constraints, these platforms require a very careful data distribution. Many solutions were proposed to resolve this issue. However, most of these solutions did not include optimizations for numerical scientific data (array data structures) and portability issues. Besides, these solutions provide a restrict set of memory policies to deal with data placement. In this paper, we describe an user-level interface named Memory Affinity interface (MAi) 1 , which allows memory affinity control on Linux based cache-coherent NUMA (ccNUMA) platforms. Its main goals are, fine data control, flexibility and portability. The performance of MAi is evaluated on three ccNUMA platforms using numerical scientific HPC applications, the NAS Parallel Benchmarks and a Geophysics application. The results show important gains (up to 31%) when compared to Linux default solution.

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Abstract-Multi-core compute nodes with non-uniform memory access (NUMA) are now a common architecture in the assembly of large-scale parallel machines. On these machines, in addition to the network communication costs, the memory access costs within a compute node are also asymmetric. Ignoring this can lead to an increase in the data movement costs. Therefore, to fully exploit the potential of these nodes and reduce data access costs, it becomes crucial to have a complete view of the machine topology (i.e. the compute node topology and the interconnection network among the nodes). Furthermore, the parallel application behavior has an important role in determining how to utilize the machine efficiently. In this paper, we propose a hierarchical load balancing approach to improve the performance of applications on parallel multi-core systems. We introduce NUCOLB, a topology-aware load balancer that focuses on redistributing work while reducing communication costs among and within compute nodes. NUCOLB takes the asymmetric memory access costs present on NUMA multi-core compute nodes, the interconnection network overheads, and the application communication patterns into account in its balancing decisions. We have implemented NUCOLB using the CHARM++ parallel runtime system and evaluated its performance. Results show that our load balancer improves performance up to 20% when compared to state-of-theart load balancers on three different NUMA parallel machines.