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Accelerating LINPACK with MPI-OpenCL on Clusters of Multi-GPU Nodes.

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Title: Accelerating LINPACK with MPI-OpenCL on Clusters of Multi-GPU Nodes.
Authors: Jo, Gangwon1, Nah, Jeongho1, Lee, Jun1, Kim, Jungwon2, Lee, Jaejin1
Source: IEEE Transactions on Parallel & Distributed Systems. Jul2015, Vol. 26 Issue 7, p1814-1825. 12p.
Subjects: LINPACK (Computer system), Graphics processing units, Heterogeneous computing, Communication models, Benchmark testing (Engineering)
Abstract: OpenCL is an open standard to write parallel applications for heterogeneous computing systems. Since its usage is restricted to a single operating system instance, programmers need to use a mix of OpenCL and MPI to program a heterogeneous cluster. In this paper, we introduce an MPI-OpenCL implementation of the LINPACK benchmark for a cluster with multi-GPU nodes. The LINPACK benchmark is one of the most widely used benchmark applications for evaluating high performance computing systems. Our implementation is based on High Performance LINPACK (HPL) and uses the blocked LU decomposition algorithm. We address that optimizations aimed at reducing the overhead of CPUs are necessary to overcome the performance gap between the CPUs and the multiple GPUs. Our LINPACK implementation achieves 93.69 Tflops (46 percent of the theoretical peak) on the target cluster with 49 nodes, each node containing two eight-core CPUs and four GPUs. [ABSTRACT FROM AUTHOR]
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Database: Engineering Source
Description
Abstract:OpenCL is an open standard to write parallel applications for heterogeneous computing systems. Since its usage is restricted to a single operating system instance, programmers need to use a mix of OpenCL and MPI to program a heterogeneous cluster. In this paper, we introduce an MPI-OpenCL implementation of the LINPACK benchmark for a cluster with multi-GPU nodes. The LINPACK benchmark is one of the most widely used benchmark applications for evaluating high performance computing systems. Our implementation is based on High Performance LINPACK (HPL) and uses the blocked LU decomposition algorithm. We address that optimizations aimed at reducing the overhead of CPUs are necessary to overcome the performance gap between the CPUs and the multiple GPUs. Our LINPACK implementation achieves 93.69 Tflops (46 percent of the theoretical peak) on the target cluster with 49 nodes, each node containing two eight-core CPUs and four GPUs. [ABSTRACT FROM AUTHOR]
ISSN:10459219
DOI:10.1109/TPDS.2014.2321742