Search Results for author: Jeeva Paudel

Found 1 papers, 1 papers with code

KLARAPTOR: A Tool for Dynamically Finding Optimal Kernel Launch Parameters Targeting CUDA Programs

1 code implementation5 Nov 2019 Alexander Brandt, Davood Mohajerani, Marc Moreno Maza, Jeeva Paudel, Linxiao Wang

In this paper we present KLARAPTOR (Kernel LAunch parameters RAtional Program estimaTOR), a new tool built on top of the LLVM Pass Framework and NVIDIA CUPTI API to dynamically determine the optimal values of kernel launch parameters of a CUDA program P. To be precise, we describe a novel technique to statically build (at the compile time of P) a so-called rational program R. Using a performance prediction model, and knowing particular data and hardware parameters of P at runtime, the program R can automatically and dynamically determine the values of launch parameters of P that will yield optimal performance.

Distributed, Parallel, and Cluster Computing Performance

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