DocumentCode :
3287433
Title :
Data handling inefficiencies between CUDA, 3D rendering, and system memory
Author :
Gordon, Brian ; Sohoni, Sohum ; Chandler, Damon
Author_Institution :
Electr. & Comput. Eng. Dept., Oklahoma State Univ., Stillwater, OK, USA
fYear :
2010
fDate :
2-4 Dec. 2010
Firstpage :
1
Lastpage :
10
Abstract :
While GPGPU programming offers faster computation of highly parallelized code, the memory bandwidth between the system and the GPU can create a bottleneck that reduces the potential gains. CUDA is a prominent GPGPU API which can transfer data to and from system code, and which can also access data used by 3D rendering APIs. In an application that relies on both GPU programming APIs to accelerate 3D modeling and an easily parallelized algorithm, the hidden inefficiencies of nVidia´s data handling with CUDA become apparent. First, CUDA uses the CPU´s store units to copy data between the graphics card and system memory instead of using a more efficient method like DMA. Second, data exchanged between the two GPU-based APIs travels through the main processor instead of staying on the GPU. As a result, a non-GPGPU implementation of a program runs faster than the same program using GPGPU.
Keywords :
application program interfaces; computer graphic equipment; coprocessors; data handling; electronic data interchange; parallel architectures; rendering (computer graphics); solid modelling; storage management; 3D rendering; API; CUDA; GPGPU programming; data exchange; data handling; data transfer; system memory; Analytical models; Computational modeling; Graphics processing unit; Load modeling; Pixel; Runtime; Three dimensional displays;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Workload Characterization (IISWC), 2010 IEEE International Symposium on
Conference_Location :
Atlanta, GA
Print_ISBN :
978-1-4244-9297-8
Electronic_ISBN :
978-1-4244-9296-1
Type :
conf
DOI :
10.1109/IISWC.2010.5648828
Filename :
5648828
Link To Document :
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