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2019 GTC San Jose

S9553 - Demystifying Deep Learning Infrastructure Choices Using MLPerf Benchmark Suite

Session Speakers
Session Description

We'll describe a new benchmark suite proposed by the Deep Learning community for machine learning workloads. We'll present a quantitative analysis of an early version (0.5) of benchmark known as MLPerf and explain performance impact of NVIDIA GPU architecture across a range of DL applications. This work includes evaluating MLPerf performance on Turing, Volta, and Pascal to demonstrate the performance impact of NVIDIA GPU architecture across a range of DL applications. We'll evaluate the impact of system-level technologies — Nvlink vs. PCIe topology — using server- and workstation-class platforms to show how system architecture impacts DL training workloads. We also plan to discuss our work to characterize MLPerf benchmark performance using profiling tools (GPU, CPU, memory & I/O), our hyperparameter tuning study (batch size, learning rate, SGD optimizer) on MLPerf performance, and map real world application use cases to MLPerf suite and how to quantify results for specific DL practitioner use cases.


Additional Information
Data Center/Cloud Infrastructure
Data Center/Cloud Infrastructure, Performance Optimization
General
All technical
Talk
50 minutes
Session Schedule