Joon Yee Chuah
ABSTRACT
Machine Learning (ML) is becoming critical for many industrial and scientific endeavors, and has a growing presence in High Performance Computing (HPC) environments. Neural network training requires long execution times for large data sets, and libraries like TensorFlow implement GPU acceleration to reduce the total runtime for each calculation. This tutorial demonstrates how to 1) use Chameleon Cloud to perform comparative studies of ML training performance across different hardware configurations; and 2) run and monitor power utilization of TensorFlow on NVIDIA GPUs.
Index Terms
- Machine Learning GPU Power Measurement on Chameleon Cloud
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