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GPGPU programming and the pitfalls o...

Gordon, Brian Patrick.

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GPGPU programming and the pitfalls of naive data handling.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	GPGPU programming and the pitfalls of naive data handling./
作者:	Gordon, Brian Patrick.
面頁冊數:	57 p.
附註:	Source: Masters Abstracts International, Volume: 48-06, page: 3797.
Contained By:	Masters Abstracts International48-06.
標題:	Engineering, Computer. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1477485
ISBN:	9781124052014

GPGPU programming and the pitfalls of naive data handling.
Gordon, Brian Patrick.

GPGPU programming and the pitfalls of naive data handling. - 57 p.

Source: Masters Abstracts International, Volume: 48-06, page: 3797.

Thesis (M.S.)--Oklahoma State University, 2010.

Scope and Method of Study. The purpose of this study was to implement a known bottleneck of a model based video encoder in code that runs on a modern graphics card using various methods. The study then looks the degree of speedup as well as the new bottlenecks for each implementation. The study also compares and contrasts the effectiveness of each method at improving the performance of the encoder.

ISBN: 9781124052014Subjects--Topical Terms:

1669061
Engineering, Computer.

GPGPU programming and the pitfalls of naive data handling.
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500 $a Source: Masters Abstracts International, Volume: 48-06, page: 3797.
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502 $a Thesis (M.S.)--Oklahoma State University, 2010.
520 $a Scope and Method of Study. The purpose of this study was to implement a known bottleneck of a model based video encoder in code that runs on a modern graphics card using various methods. The study then looks the degree of speedup as well as the new bottlenecks for each implementation. The study also compares and contrasts the effectiveness of each method at improving the performance of the encoder.
520 $a Findings and Conclusions. While moving the highly parallel error calculation to the graphics card reduced the time required to perform the computation, the program still ran slower. Through analysis of the processors performance counters, the image data appears to move through system memory even though its origin and destination are in graphics memory. This inefficient handling of the data that should remain in graphics memory is removing any potential speedup from using the graphics card as a highly parallel coprocessor to accelerate the slowest part of the original program.
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