Detection and GPU Accelerationof 3D FDTD Algorithms Based on Memory Access Patterns

被引：0

作者：

Shao, Ran ^{[1
]}

Linton, David ^{[1
]}

Spence, Ivor ^{[1
]}

Milligan, Peter ^{[1
]}

Zheng, Ning ^{[1
]}

机构：

[1] Queens Univ Belfast, Sch Elect Elect Engn & Comp Sci, Belfast, Antrim, North Ireland

来源：

PROCEEDINGS 2013 INTERNATIONAL CONFERENCE ON MECHATRONIC SCIENCES, ELECTRIC ENGINEERING AND COMPUTER (MEC) | 2013年

关键词：

FDTD; Memory access pattern; LLVM; CUDA; 5.0; RADIATION;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

A semi-automatic tool is reported that first analyzes the sequential FDTD program to obtain memory access patterns and related features, and then optimizes the FDTD program with combined use of several types of CUDA memory on both Fermi and Kepler architecture GPUs. The experiments show a 13% and 18% speedup using Fermi and Kepler GPUs respectively compared to the GPU version program without optimization. Up to 142 times speedup is achieved compared to the sequential FDTD C program at a FDTD 3D mesh size of 250*250*250 (15.625 million mesh cells) with 10 layers CPML boundary conditions in 4096 time steps.

引用

页码：2520 / 2526

页数：7

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