High performance computing is increasingly common in technological industries and there are many different solutions available on the market. Determining which computing solution is most cost-effective can be difficult. This study outlines the performance between a single-user, traditional high-performance workstation and a multi-user, virtualized workstation. Along with this direct performance comparison, the impacts of virtualization on rendering performance, GPUs, and the technological industry is evaluated in this study. Through the repeated rendering of two different Computer-Aided Design (CAD) models under varying test scenarios, a pool of data including render times and image quality is collected and analyzed. Two phenomena are observed and explained. One is a diminishing return in GPU power output that is observed after allocating four or more GPUs to a single rendering task. The second is a noticeable point of image-noise convergence during a render that could potentially be calculated and exploited to make rendering more time-efficient. These discoveries may impact the effectiveness of virtual GPU scalability and make time-consuming rendering more efficient for industry users. The NVIDIA GRID Visual Computing Appliance (VCA) is found to be cost effective for research laboratories that have several users with diverse needs.
| Published in | Internet of Things and Cloud Computing (Volume 6, Issue 2) |
| DOI | 10.11648/j.iotcc.20180602.11 |
| Page(s) | 36-48 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2018. Published by Science Publishing Group |
Rendering, Virtualization, Nvidia Grid Visual Computing Appliance, Graphics Processing Unit Computing, Iray, V-Ray
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APA Style
Kenneth Ritter III, Aaron Morgan, Charles Taylor, Terrence Chambers. (2018). Multilevel Performance Evaluation of Nvidia Grid VCA Using Iray and V-Ray Rendering Engines in 3DS Max Design. Internet of Things and Cloud Computing, 6(2), 36-48. https://doi.org/10.11648/j.iotcc.20180602.11
ACS Style
Kenneth Ritter III; Aaron Morgan; Charles Taylor; Terrence Chambers. Multilevel Performance Evaluation of Nvidia Grid VCA Using Iray and V-Ray Rendering Engines in 3DS Max Design. Internet Things Cloud Comput. 2018, 6(2), 36-48. doi: 10.11648/j.iotcc.20180602.11
AMA Style
Kenneth Ritter III, Aaron Morgan, Charles Taylor, Terrence Chambers. Multilevel Performance Evaluation of Nvidia Grid VCA Using Iray and V-Ray Rendering Engines in 3DS Max Design. Internet Things Cloud Comput. 2018;6(2):36-48. doi: 10.11648/j.iotcc.20180602.11
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Download@article{10.11648/j.iotcc.20180602.11,
author = {Kenneth Ritter III and Aaron Morgan and Charles Taylor and Terrence Chambers},
title = {Multilevel Performance Evaluation of Nvidia Grid VCA Using Iray and V-Ray Rendering Engines in 3DS Max Design},
journal = {Internet of Things and Cloud Computing},
volume = {6},
number = {2},
pages = {36-48},
doi = {10.11648/j.iotcc.20180602.11},
url = {https://doi.org/10.11648/j.iotcc.20180602.11},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.iotcc.20180602.11},
abstract = {High performance computing is increasingly common in technological industries and there are many different solutions available on the market. Determining which computing solution is most cost-effective can be difficult. This study outlines the performance between a single-user, traditional high-performance workstation and a multi-user, virtualized workstation. Along with this direct performance comparison, the impacts of virtualization on rendering performance, GPUs, and the technological industry is evaluated in this study. Through the repeated rendering of two different Computer-Aided Design (CAD) models under varying test scenarios, a pool of data including render times and image quality is collected and analyzed. Two phenomena are observed and explained. One is a diminishing return in GPU power output that is observed after allocating four or more GPUs to a single rendering task. The second is a noticeable point of image-noise convergence during a render that could potentially be calculated and exploited to make rendering more time-efficient. These discoveries may impact the effectiveness of virtual GPU scalability and make time-consuming rendering more efficient for industry users. The NVIDIA GRID Visual Computing Appliance (VCA) is found to be cost effective for research laboratories that have several users with diverse needs.},
year = {2018}
}
TY - JOUR T1 - Multilevel Performance Evaluation of Nvidia Grid VCA Using Iray and V-Ray Rendering Engines in 3DS Max Design AU - Kenneth Ritter III AU - Aaron Morgan AU - Charles Taylor AU - Terrence Chambers Y1 - 2018/05/05 PY - 2018 N1 - https://doi.org/10.11648/j.iotcc.20180602.11 DO - 10.11648/j.iotcc.20180602.11 T2 - Internet of Things and Cloud Computing JF - Internet of Things and Cloud Computing JO - Internet of Things and Cloud Computing SP - 36 EP - 48 PB - Science Publishing Group SN - 2376-7731 UR - https://doi.org/10.11648/j.iotcc.20180602.11 AB - High performance computing is increasingly common in technological industries and there are many different solutions available on the market. Determining which computing solution is most cost-effective can be difficult. This study outlines the performance between a single-user, traditional high-performance workstation and a multi-user, virtualized workstation. Along with this direct performance comparison, the impacts of virtualization on rendering performance, GPUs, and the technological industry is evaluated in this study. Through the repeated rendering of two different Computer-Aided Design (CAD) models under varying test scenarios, a pool of data including render times and image quality is collected and analyzed. Two phenomena are observed and explained. One is a diminishing return in GPU power output that is observed after allocating four or more GPUs to a single rendering task. The second is a noticeable point of image-noise convergence during a render that could potentially be calculated and exploited to make rendering more time-efficient. These discoveries may impact the effectiveness of virtual GPU scalability and make time-consuming rendering more efficient for industry users. The NVIDIA GRID Visual Computing Appliance (VCA) is found to be cost effective for research laboratories that have several users with diverse needs. VL - 6 IS - 2 ER -
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