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GTC ON-DEMAND

Performance Optimization
Presentation
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ACCELERATING CUBLAS/CUDNN USING INPUT-AWARE AUTO-TUNING: THE ISAAC LIBRARY
Abstract:

This session describes the design and implementation of ISAAC, an open-source framework for GEMM and CONV that provides improved performance over cuBLAS and cuDNN. Attendees will learn about input-aware auto-tuning, a technique that relies on machine learning models to automatically derive input- and hardware- portable PTX kernels. Benchmarks will be provided for GEMM and CONV in the context of LINPACK, DeepBench, ICA and SVD, showing up to 3x performance gains over vendor libraries on a GTX980 and a Tesla P100.

 
Topics:
Performance Optimization, Artificial Intelligence and Deep Learning, Tools & Libraries
Type:
Talk
Event:
GTC Silicon Valley
Year:
2017
Session ID:
S7150
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