Treffer: Finite Difference Time Domain (FDTD) Simulations Using Graphics Processors

Title:
Finite Difference Time Domain (FDTD) Simulations Using Graphics Processors
Contributors:
AIR FORCE RESEARCH LAB BROOKS AFB TX HUMAN EFFECTIVENESS DIRECTORATE
Source:
DTIC
Publication Year:
2007
Collection:
Defense Technical Information Center: DTIC Technical Reports database
Document Type:
Fachzeitschrift text
File Description:
text/html
Language:
English
Rights:
Approved for public release; distribution is unlimited.
Accession Number:
edsbas.9291B172
Database:
BASE

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This paper presents a graphics processor based implementation of the Finite Difference Time Domain (FDTD), which uses a central finite differencing scheme for solving Maxwell's equations for electromagnetics. FDTD simulations can be very computationally expensive and require thousands of CPU hours to solve on traditional general purpose processors. Modern Graphics Processing Units (GPUs) found in desktop computers are programmable and are capable of much higher vector floating-point performance than general purpose CPUs. This paper shows how GPUs can be used to greatly speedup FDTD simulations. The main objective is to leverage GPU processing power for FDTD update calculations and complete computationally expensive simulations in reasonable time. This allows researchers to simulate much longer pulse lengths and larger models than was possible in the past. A new FDTD code was developed to leverage graphics processors using Linux, C, OpenGL, Cg, and commodity GeForce 7 series GPUs. The graphics hardware was accessed through standard OpenGL. The FDTD model space was then transferred to the GPU device memory through OpenGL textures and host readable via frame buffer objects exposed by the OpenGL 2.0 application programming interface (API). GPU fragment processors were utilized for the FDTD update computations via Cgfragment programs. For models that were sufficiently large, greater than (140) cu cells, the GPU performed FDTD update calculations at least 12 times faster than the execution of the same simulation on a contemporary multicore CPU from Intel or AMD. The use of GPUs shows great promise for high performance computing applications like FDTD that have high arithmetic intensity and limited or no data dependencies in computation streams. Until recently, to use GPUs as a co-processor, the normal CPU-based code needed to be rewritten extensively using special graphics programming language Cg and OpenGL APIs, which is difficult for non-graphics programmers. ; The original document contains color images. All DTIC ...