SCHEDULE: NOV 15-20, 2015

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STATuner: Efficient Tuning of CUDA Kernels Parameters

SESSION: Regular & ACM Student Research Competition Poster Reception

EVENT TYPE: Posters, Receptions, ACM Student Research Competition

EVENT TAG(S): HPC Beginner Friendly, Regular Poster

TIME: 5:15PM - 7:00PM

SESSION CHAIR(S): Michela Becchi, Manish Parashar, Dorian C. Arnold

AUTHOR(S):Ravi Gupta, Ignacio Laguna, Dong H. Ahn, Todd Gamblin, Saurabh Bagchi, Felix Xiaozhu Lin

ROOM:Level 4 - Lobby

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.

Chair/Author Details:

Michela Becchi, Manish Parashar, Dorian C. Arnold (Chair) - University of Missouri|Rutgers University|University of New Mexico|

Ravi Gupta - Purdue University

Ignacio Laguna - Lawrence Livermore National Laboratory

Dong H. Ahn - Lawrence Livermore National Laboratory

Todd Gamblin - Lawrence Livermore National Laboratory

Saurabh Bagchi - Purdue University

Felix Xiaozhu Lin - Purdue University

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