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2013 IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum (2012)
Shanghai, China China
May 21, 2012 to May 25, 2012
ISBN: 978-1-4673-0974-5
pp: 2404-2413
Dynamic scheduling and varying decomposition granularity are well-known techniques for achieving high performance in parallel computing. Heterogeneous clusters with highly data-parallel processors, such as GPUs, present unique problems for the application of these techniques. These systems reveal a dichotomy between grain sizes: decompositions ideal for the CPUs may yield insufficient data-parallelism for accelerators, and decompositions targeted at the GPU may decrease performance on the CPU. This problem is typically ameliorated by statically scheduling a fixed amount of work for agglomeration. However, determining the ideal amount of work to compose requires experimentation because it varies between architectures and problem configurations. This paper describes a novel methodology for dynamically agglomerating work units at runtime and scheduling them on accelerators. This approach is demonstrated in the context of two applications: an n-body particle simulation, which offloads particle interaction work, and a parallel dense LU solver, which relocates DGEMM kernels to the GPU. In both cases dynamic agglomeration yields comparable or better results over statically scheduling the work across a variety of system configurations.
Graphics processing unit, Kernel, Arrays, Dynamic scheduling, Grain size, Runtime, adaptive runtime, dynamic scheduling, accelerator, GPGPU, CUDA, agglomeration
Jonathan Lifflander, G. Carl Evans, Anshu Arya, Laxmikant V. Kale, "Dynamic Scheduling for Work Agglomeration on Heterogeneous Clusters", 2013 IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum, vol. 00, no. , pp. 2404-2413, 2012, doi:10.1109/IPDPSW.2012.297
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