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Adapting Irregular Computations to Large CPU-GPU Clusters in the MADNESS Framework

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4 Author(s)
Slavici, V. ; Northeastern Univ., Boston, MA, USA ; Varier, R. ; Cooperman, G. ; Harrison, R.J.

Graphics Processing Units (GPUs) are becoming the workhorse of scalable computations. MADNESS is a scientific framework used especially for computational chemistry. Most MADNESS applications use operators that involve many small tensor computations, resulting in a less regular organization of computations on GPUs. A single GPU kernel may have to multiply by hundreds of small square matrices (with fixed dimension ranging from 10 to 28). We demonstrate a scalable CPU-GPU implementation of the MADNESS framework over a 500-node partition on the Titan supercomputer. For this hybrid CPU-GPU implementation, we observe up to a 2.3-times speedup compared to an equivalent CPU-only implementation with 16 cores per node. For smaller matrices, we demonstrate a speedup of 2.2-times by using a custom CUDA kernel rather than a cuBLAS-based kernel.

Published in:

Cluster Computing (CLUSTER), 2012 IEEE International Conference on

Date of Conference:

24-28 Sept. 2012