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Directive-based general-purpose GPU ...
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Han, Tian Yi David.
Directive-based general-purpose GPU programming.
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Directive-based general-purpose GPU programming.
作者:
Han, Tian Yi David.
面頁冊數:
112 p.
附註:
Source: Masters Abstracts International, Volume: 48-04, page: 2384.
Contained By:
Masters Abstracts International48-04.
標題:
Engineering, Computer.
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=MR59180
ISBN:
9780494591802
Directive-based general-purpose GPU programming.
Han, Tian Yi David.
Directive-based general-purpose GPU programming.
- 112 p.
Source: Masters Abstracts International, Volume: 48-04, page: 2384.
Thesis (M.Sc.)--University of Toronto (Canada), 2009.
Graphics Processing Units (GPUs) have become a competitive accelerator for non-graphics applications, mainly driven by the improvements in GPU programmability. Although the Compute Unified Device Architecture (CUDA) is a simple C-like interface for programming NVIDIA GPUs, porting applications to CUDA remains a challenge to average programmers. In particular, CUDA places on the programmer the burden of packaging GPU code in separate functions, of explicitly managing data transfer between the host and GPU memories, and of manually optimizing the utilization of the GPU memory. We have designed hiCUDA, a high-level directive-based language for CUDA programming. It allows programmers to perform these tedious tasks in a simpler manner, and directly to the sequential code. We have also prototyped a compiler that translates a hiCUDA program to a CUDA program and can handle real-world applications. Experiments using seven standard CUDA benchmarks show that the simplicity hiCUDA provides comes at no expense to performance.
ISBN: 9780494591802Subjects--Topical Terms:
384375
Engineering, Computer.
Directive-based general-purpose GPU programming.
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Graphics Processing Units (GPUs) have become a competitive accelerator for non-graphics applications, mainly driven by the improvements in GPU programmability. Although the Compute Unified Device Architecture (CUDA) is a simple C-like interface for programming NVIDIA GPUs, porting applications to CUDA remains a challenge to average programmers. In particular, CUDA places on the programmer the burden of packaging GPU code in separate functions, of explicitly managing data transfer between the host and GPU memories, and of manually optimizing the utilization of the GPU memory. We have designed hiCUDA, a high-level directive-based language for CUDA programming. It allows programmers to perform these tedious tasks in a simpler manner, and directly to the sequential code. We have also prototyped a compiler that translates a hiCUDA program to a CUDA program and can handle real-world applications. Experiments using seven standard CUDA benchmarks show that the simplicity hiCUDA provides comes at no expense to performance.
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