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Performance Optimization
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ACCELERATING CUBLAS/CUDNN USING INPUT-AWARE AUTO-TUNING: THE ISAAC LIBRARY
Philippe Tillet (Harvard University)
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 ma ...Read More

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.

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Keywords:
Performance Optimization, Deep Learning and AI, Tools and Libraries, GTC Silicon Valley 2017 - ID S7150
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