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PRODID:-//Microsoft Corporation//Outlook MIMEDIR//EN
VERSION:2.0
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DTSTART:20151117T231500Z
DTEND:20151118T010000Z
LOCATION:Level 4 - Lobby
DESCRIPTION;ENCODING=QUOTED-PRINTABLE:ABSTRACT: CUDA programmers need to decide the block size to use for a kernel launch that yields the lowest execution time. However, existing models to predict the best block size are not always accurate and involve a lot of manual effort from programmers. We identify a list of static metrics that can be used to characterize a kernel and build a Machine Learning model to predict block size that can be used in a kernel launch to minimize execution time. We use a set of kernels to train our model based on these identified static metrics and compare its predictions with the well-known NVIDIA tool called Occupancy Calculator on test kernels. Our model is able to predict block size that gives average error of 4.4% in comparison to Occupancy Calculator that gives error of 6.6%. Our model requires no trial runs of the kernel and lesser effort compared to Occupancy Calculator.
SUMMARY:STATuner: Efficient Tuning of CUDA Kernels Parameters
PRIORITY:3
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