Flexible Hardware Mapping for Finite Element Simulations on Hybrid CPU / GPU Clusters

Aaron Becker, Isaac Dooley, Laxmikant V. Kale
University of Illinois, Urbana, IL 61801
Symposium on Application Accelerators in High Performance Computing, 2009 (SAAHPC’09)


   title={Flexible Hardware Mapping for Finite Element Simulations on Hybrid CPU/GPU Clusters},

   author={Becker, A. and Dooley, I. and Kale, L.},

   booktitle={SAAHPC: Symposium on Application Accelerators in HPC},



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The ever increasing peak floating-point performance and memory bandwidth of GPUs is making them increasingly ubiquitous in the high performance computing community. With increasing adoption of GPUs in cluster environments, applications that cannot take advantage of this hardware will be at a distinct disadvantage. For the class of applications that can achieve massive speedups of 100x or more on the GPU, the way forward is clear: maximum performance will depend on utilizing all available GPUs as efficiently as possible, with the CPU most likely relegated to managing the data flowing into and out of the GPU. However, for applications that can benefit from GPU execution, but may experience speedups that are only in the range of 5-10x, the appropriate relationship between the CPU and GPU is more difficult to determine, and may depend upon the specifics of the algorithm and hardware in question.
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