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2010•Journal of the Institute of Electronics Engineers of KoreaRequires access

Parallel Computation of FDTD algorithm using CUDA

Ho-Young Lee, Jong-Hyun Park, Jun-Seong Kim

Open publisher page 1 citations

Abstract

Modern GPUs(Graphic Processing Units) provide computing capability higher than that of the general CPUs(Central Processor Units). With supports of programmability of graphics pipeline GP-GPU(General Purpose computation on GPU) has gained much attention expanding its application area. This paper compares sequential and massively parallel implementations of FDTD(Finite Difference Time Domain) algorithm using CUDA(Compute Unified Device Architecture). Experimental results show upto 45X speedup over conventional CPU execution.

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What this paper is about

Modern GPUs(Graphic Processing Units) provide computing capability higher than that of the general CPUs(Central Processor Units). With supports of programmability of graphics pipeline GP-GPU(General Purpose computation on GPU) has gained much attention expanding its application area. This paper compares sequential and massively parallel implementations of FDTD(Finite Difference Time Domain) algorithm using CUDA(Compute Unified Device Architecture). Experimental results show upto 45X speedup over conventional CPU execution.

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Available abstract

Modern GPUs(Graphic Processing Units) provide computing capability higher than that of the general CPUs(Central Processor Units). With supports of programmability of graphics pipeline GP-GPU(General Purpose computation on GPU) has gained much attention expanding its application area. This paper compares sequential and massively parallel implementations of FDTD(Finite Difference Time Domain) algorithm using CUDA(Compute Unified Device Architecture). Experimental results show upto 45X speedup over conventional CPU execution.

Key concepts: CUDA, Computer science, Parallel computing, Speedup, Computation, Pipeline (software), Massively parallel, Graphics

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