Characterizing compute kernel execution behavior on GPUs for efficient task scheduling is a non trivial task. We address this with a simple model enabling portable and fast predictions among different GPUs using only hardware-independent features extracted . This model is built based on random forests using 189 individual compute kernels from benchmarks such as Parboil, Rodinia, Polybench-GPU and SHOC. Evaluation of the model performance using cross-validation yields a median Mean Average Percentage Error (MAPE) of 13.45\% , 44.56\% and%DIFDELCMD < [%%% 1.81\%, 2.91\% , for time respectively power prediction on five different GPUs, while latency for a single prediction varies between 0.1 and 0.2 seconds .
Characterizing compute kernel execution behavior on GPUs for efficient task scheduling is a non-trivial task. We address this with a simple model enabling portable and fast predictions among different GPUs using only hardware-independent features . This model is built based on random forests using 189 individual compute kernels from benchmarks such as Parboil, Rodinia, Polybench-GPU and SHOC. Evaluation of the model performance using cross-validation yields a median Mean Average Percentage Error (MAPE) of %DIFDELCMD < [%%% 8.86-52.00\% and 1.84-2.94\%, for time respectively power prediction across five different GPUs, while latency for a single prediction varies between 15 and 108 milliseconds .
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non trivial
non-trivial
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extracted
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13.45\% , 44.56\%
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and
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1.81\%, 2.91\%
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,
8.86-52.00\% and 1.84-2.94\%,
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on
across
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0.1 and 0.2 seconds
15 and 108 milliseconds