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Dynamic translation of runtime envir...

Dominguez, Rodrigo.

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Dynamic translation of runtime environments for heterogeneous computing.

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Dynamic translation of runtime environments for heterogeneous computing./
作者:	Dominguez, Rodrigo.
面頁冊數:	88 p.
附註:	Source: Dissertation Abstracts International, Volume: 74-09(E), Section: B.
Contained By:	Dissertation Abstracts International74-09B(E).
標題:	Engineering, Computer. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3563295
ISBN:	9781303115158

Dynamic translation of runtime environments for heterogeneous computing.
Dominguez, Rodrigo.

Dynamic translation of runtime environments for heterogeneous computing. - 88 p.

Source: Dissertation Abstracts International, Volume: 74-09(E), Section: B.

Thesis (Ph.D.)--Northeastern University, 2013.

The recent move toward heterogeneous computer architectures calls for a global rethinking of current software and hardware paradigms. Researchers are exploring new parallel programming models, advanced compiler designs, and novel resource management techniques to exploit the features of many-core processor architectures. Graphics Processing Units (GPUs) have become the platform of choice in this area for accelerating a large range of data-parallel and task-parallel applications. The rapid adoption of GPU computing has been greatly aided by the introduction of high-level programming environments such as CUDA C and OpenCL. However, each vendor implements these programming models differently and we must analyze the internals in order to get a better understanding of the performance results.

ISBN: 9781303115158Subjects--Topical Terms:

1669061
Engineering, Computer.

Dynamic translation of runtime environments for heterogeneous computing.
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500 $a Source: Dissertation Abstracts International, Volume: 74-09(E), Section: B.
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520 $a The recent move toward heterogeneous computer architectures calls for a global rethinking of current software and hardware paradigms. Researchers are exploring new parallel programming models, advanced compiler designs, and novel resource management techniques to exploit the features of many-core processor architectures. Graphics Processing Units (GPUs) have become the platform of choice in this area for accelerating a large range of data-parallel and task-parallel applications. The rapid adoption of GPU computing has been greatly aided by the introduction of high-level programming environments such as CUDA C and OpenCL. However, each vendor implements these programming models differently and we must analyze the internals in order to get a better understanding of the performance results.
520 $a This dissertation presents the design of Caracal, a dynamic compiler that can translate between runtime environments used in heterogeneous computing. A major challenge of developing such a translation system is the inherent difference in both the underlying instruction set architecture, as well as the runtime system. One of the more challenging questions across different runtime environments is the handling of program control flow by the compiler and the hardware. Some implementations can support unstructured control flow based on branches and labels, while others are based on structured control flow relying solely on if-then and while constructs.
520 $a Caracal can translate applications based on unstructured control flow so they can run on hardware that requires structured programs. In order to accomplish this, Caracal builds the control tree of the program and creates single-entry, single-exit regions called hammock graphs. We use Caracal to analyze the performance differences between NVIDIA's implementation of CUDA C and AMD's implementation of OpenCL. Our results show that the requirement for structured control flow can impact register pressure, and can degrade performance by as much as 2.5X.
520 $a We also explore the differences between heterogeneous parallel processors architectures that will impact the design of a translator when attempting to optimize code. We evaluate this issue for two specific compiler optimizations that are highly sensitive to the underlying architecture. We use Caracal to evaluate 1) vectorization and 2) loop unrolling. Our translation system needs to capture rich semantics to be able to effectively translate code to different systems that will exploit these two optimization techniques differently. Our experiments show that by tuning these two optimizations in our translation system, we can reduce execution time by up to 90%.
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