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2017 GTC San Jose

S7150 - ACCELERATING CUBLAS/CUDNN USING INPUT-AWARE AUTO-TUNING: THE ISAAC LIBRARY

Session Speakers
Session Description

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.


Additional Session Information
Intermediate
Talk
Deep Learning and AI Performance Optimization Tools and Libraries
Defense Energy / Oil & Gas Government / National Labs Higher Education / Research Manufacturing
25 minutes
Session Schedule