Abstract
On numerical scientific High Performance Computing (HPC), Non-Uniform Memory Access (NUMA) platforms are now commonplace. On such platforms, the memory affinity management remains an important concern in order to overcome the memory wall problem. Prior solutions have presented some drawbacks such as machine dependency and a limited set of memory policies. This paper introduces Minas, a framework which provides either explicit or automatic memory affinity management with architecture abstraction for ccNUMAs. We evaluate our solution on two ccNUMA platforms using two geophysics parallel applications. The results show some performance improvements in comparison with other solutions available for Linux.
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Pousa Ribeiro, C., Castro, M., Méhaut, JF., Carissimi, A. (2011). Improving Memory Affinity of Geophysics Applications on NUMA Platforms Using Minas. In: Palma, J.M.L.M., Daydé, M., Marques, O., Lopes, J.C. (eds) High Performance Computing for Computational Science – VECPAR 2010. VECPAR 2010. Lecture Notes in Computer Science, vol 6449. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19328-6_27
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DOI: https://doi.org/10.1007/978-3-642-19328-6_27
Publisher Name: Springer, Berlin, Heidelberg
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