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Dynamically Finding Optimal Kernel Launch Parameters for CUDA Programs.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Dynamically Finding Optimal Kernel Launch Parameters for CUDA Programs./
Author:	Jeshani, Taabish.
Description:	1 online resource (67 pages)
Notes:	Source: Masters Abstracts International, Volume: 85-01.
Contained By:	Masters Abstracts International85-01.
Subject:	Problem solving. -
Online resource:	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:

527887
Problem solving.
Index Terms--Genre/Form:

554714
Electronic books.

Dynamically Finding Optimal Kernel Launch Parameters for CUDA Programs.
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504 $a Includes bibliographical references
520 $a 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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