On Benchmarking Multiple GPU Computing Resources for Faster Training of Deep Neural Networks
Abstract
In this technical note, we explore a method to benchmark the performance of a Lambda Quad Deep Learning Workstation on training seven leading deep neural network (DNN) models using multiple GTX-1080 Ti graphics processing units (GPUs). We compute the average number of images processed per second for each DNN and quantify the consistent improvements in speed/performance when additional GPUs are assigned to the task. Our results show that the multi-GPU performance statistics calculated at the US Army Combat Capabilities Development Command Army Research Laboratory (CCDC ARL) are in close agreement to those calculated at Lambda Labs. The benchmarking of an accessible distributed computing resource provides an important step toward mitigating the excessive computational costs often associated with modern machine-learning applications, to include the training of DNNs on large data sets.
Document Details
- Document Type
- Technical Report
- Publication Date
- Feb 06, 2020
- Accession Number
- AD1091936
Entities
People
- Arnold D. Tunick
Organizations
- United States Army Research Laboratory