Performance Versus Cost of a Parallel Conjugate Gradient Method in Cloud and Commodity Clusters

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Cloud computing is an emerging technology to run HPC applications using computing resources on a pay per use basis. The CG method is a linear solver which is used in many engineering and scientific applications, and is computationally demanding. The authors implement different approaches of a parallel CG method and compare their performance on different types of platforms: an HPC-optimized cluster, a built heterogeneous cluster, and Amazon cloud. They evaluate the performance of two approaches: broadcast and a ring-based communication computation overlap, and compare to that of National Aeronautics and Space Administration Advanced Supercomputing CG parallel benchmark.