9552

GPU Acceleration of Particle Advection Workloads in a Parallel, Distributed Memory Setting

David Camp, Hari Krishnan, David Pugmire, Christoph Garth, Ian Johnson, E. Wes Bethel, Kenneth I. Joy, Hank Childs
Lawrence Berkeley National Laboratory, CA, USA
Eurographics Symposium on Parallel Graphics and Visualization (EGPGV), 2013

@inproceedings{camp2013gpu,

   title={GPU Acceleration of Particle Advection Workloads in a Parallel, Distributed Memory Setting},

   author={Camp, David and Krishnan, Hari and Pugmire, David and Garth, Christoph and Johnson, Ian and Bethel, E Wes and Joy, Kenneth I and Childs, Hank and Ye, Yucong and Miller, Robert and others},

   booktitle={Eurographics Symposium on Parallel Graphics and Visualization},

   pages={1–8},

   year={2013},

   organization={The Eurographics Association}

}

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Although there has been significant research in GPU acceleration, both of parallel simulation codes (i.e., GPGPU) and of single GPU visualization and analysis algorithms, there has been relatively little research devoted to visualization and analysis algorithms on GPU clusters. This oversight is significant: parallel visualization and analysis algorithms have markedly different characteristics – computational load, memory access pattern, communication, idle time, etc. – than the other two categories. In this paper, we explore the benefits of GPU acceleration for particle advection in a parallel, distributed-memory setting. As performance properties can differ dramatically between particle advection use cases, our study operates over a variety of workloads, designed to reveal insights about underlying trends. This work has a three-fold aim: (1) to map a challenging visualization and analysis algorithm – particle advection – to a complex system (a cluster of GPUs), (2) to inform its performance characteristics, and (3) to evaluate the advantages and disadvantages of using the GPU. In our performance study, we identify which factors are and are not relevant for obtaining a speedup when using GPUs. In short, this study informs the following question: if faced with a parallel particle advection problem, should you implement the solution with CPUs, with GPUs, or does it not matter?
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