Research Website
cam2.ecn.purdue.edu
Keywords
GPU, image processing, parallel computing
Presentation Type
Event
Research Abstract
Over the past several years, graphics processing units (GPU) have increasingly been viewed as the future of image processing engines. Currently, the Continuous Analysis of Many CAMeras (CAM2) project performs its processing on CPUs, which will potentially be more costly as the system scales to service more users. This study seeks to analyze the performance gains of GPU processing and evaluate the advantage of supporting GPU-accelerated analysis for CAM2 users. The platform for comparing the CPU and GPU performance has been the NVIDIA Jetson TK1. The target hardware implementation is an Amazon cloud instance, where final cost analysis will be performed. It is expected that the GPU will out perform its CPU counterpart in some image processing applications. The degree to which it outperforms the CPU is subject to a number of factors. So far, tests have shown the expected speedup (and lackthereof) in basic mathematical operations performed on the GPU, indicative of the expected success of the integration into the CAM2 system.
Session Track
Computer and Web Based Applications
Recommended Citation
Jonathan Cottom, Yung-Hsiang Lu, and Young-Sol Koh,
"GPU/CPU Performance of Image Processing Tasks for use in the CAM 2 System"
(August 6, 2015).
The Summer Undergraduate Research Fellowship (SURF) Symposium.
Paper 3.
https://docs.lib.purdue.edu/surf/2015/presentations/3
Included in
GPU/CPU Performance of Image Processing Tasks for use in the CAM 2 System
Over the past several years, graphics processing units (GPU) have increasingly been viewed as the future of image processing engines. Currently, the Continuous Analysis of Many CAMeras (CAM2) project performs its processing on CPUs, which will potentially be more costly as the system scales to service more users. This study seeks to analyze the performance gains of GPU processing and evaluate the advantage of supporting GPU-accelerated analysis for CAM2 users. The platform for comparing the CPU and GPU performance has been the NVIDIA Jetson TK1. The target hardware implementation is an Amazon cloud instance, where final cost analysis will be performed. It is expected that the GPU will out perform its CPU counterpart in some image processing applications. The degree to which it outperforms the CPU is subject to a number of factors. So far, tests have shown the expected speedup (and lackthereof) in basic mathematical operations performed on the GPU, indicative of the expected success of the integration into the CAM2 system.
https://docs.lib.purdue.edu/surf/2015/presentations/3