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Dynamically Finding Optimal Kernel Launch Parameters for CUDA Programs.
紀錄類型:
書目-語言資料,手稿 : Monograph/item
正題名/作者:
Dynamically Finding Optimal Kernel Launch Parameters for CUDA Programs./
作者:
Jeshani, Taabish.
面頁冊數:
1 online resource (67 pages)
附註:
Source: Masters Abstracts International, Volume: 85-01.
Contained By:
Masters Abstracts International85-01.
標題:
Statistics. -
電子資源:
click for full text (PQDT)
ISBN:
9798379872571
Dynamically Finding Optimal Kernel Launch Parameters for CUDA Programs.
Jeshani, Taabish.
Dynamically Finding Optimal Kernel Launch Parameters for CUDA Programs.
- 1 online resource (67 pages)
Source: Masters Abstracts International, Volume: 85-01.
Thesis (M.Sc.)--The University of Western Ontario (Canada), 2023.
Includes bibliographical references
In this thesis, we present KLARAPTOR (Kernel LAunch parameters RAtional Program estimaTOR), a freely available tool to dynamically determine the values of kernel launch parameters of a CUDA kernel. We describe a technique for building a helper program, at the compile-time of a CUDA program, that is used at run-time to determine near-optimal kernel launch parameters for the kernels of that CUDA program. This technique leverages the MWP-CWP performance prediction model, runtime data parameters, and runtime hardware parameters to dynamically determine the launch parameters for each kernel invocation. This technique is implemented within the KLARAPTOR tool, utilizing the LLVM Pass Framework and NVIDIA Nsight Compute CLI profiler. We demonstrate the effectiveness of our approach through experimentation on the PolyBench benchmark suite of CUDA kernels.
Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024
Mode of access: World Wide Web
ISBN: 9798379872571Subjects--Topical Terms:
556824
Statistics.
Index Terms--Genre/Form:
554714
Electronic books.
Dynamically Finding Optimal Kernel Launch Parameters for CUDA Programs.
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In this thesis, we present KLARAPTOR (Kernel LAunch parameters RAtional Program estimaTOR), a freely available tool to dynamically determine the values of kernel launch parameters of a CUDA kernel. We describe a technique for building a helper program, at the compile-time of a CUDA program, that is used at run-time to determine near-optimal kernel launch parameters for the kernels of that CUDA program. This technique leverages the MWP-CWP performance prediction model, runtime data parameters, and runtime hardware parameters to dynamically determine the launch parameters for each kernel invocation. This technique is implemented within the KLARAPTOR tool, utilizing the LLVM Pass Framework and NVIDIA Nsight Compute CLI profiler. We demonstrate the effectiveness of our approach through experimentation on the PolyBench benchmark suite of CUDA kernels.
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