Improving memory affinity of geophysics applications on NUMA platforms using minas
VECPAR'10 Proceedings of the 9th international conference on High performance computing for computational science
A dynamic optimization framework for OpenMP
IWOMP'11 Proceedings of the 7th international conference on OpenMP in the Petascale era
Nonuniform memory affinity strategy in multithreaded sparse matrix computations
Proceedings of the 2012 Symposium on High Performance Computing
Critical path-based thread placement for NUMA systems
ACM SIGMETRICS Performance Evaluation Review
Actor scheduling for multicore hierarchical memory platforms
Proceedings of the twelfth ACM SIGPLAN workshop on Erlang
Improving the performance of actor model runtime environments on multicore and manycore platforms
Proceedings of the 2013 workshop on Programming based on actors, agents, and decentralized control
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Currently, parallel platforms based on large scale hierarchical shared memory multiprocessors with Non-Uniform 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), 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.