PAVE: An in situ framework for scientific visualization and machine learning coupling

Samuel Leventhal, Mark Kim, David Pugmire

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations

Abstract

Machine learning (ML) has emerged as a tool for understanding data at scale. However, this new methodology comes at a cost because ML requires the use of even more HPC resources to generate ML algorithms. In addition to the compute resources required to develop ML algorithms, ML does not sidestep one of the biggest challenges on leading-edge HPC systems: the increasing gap between compute performance and I/O bandwidth. This has led to a strong push towards in situ, processing the data as it is generated, strategies to mitigate the I/O bottleneck. Unfortunately, there are no in situ frameworks dedicated to coupling scientific visualization and ML at scale to develop ML algorithms for scientific visualization. To address the ML and in situ visualization gap, we introduce PAVE. PAVE is an in situ framework which addresses the data management needs between visualisation and machine learning tasks. We demonstrate our framework with a case study that accelerates physically-based light rendering, path-tracing, through the use of a conditional Generative Adversarial neural Network (cGAN). PAVE couples the training over path-traced images resulting in a generative model able to produce scene renderings with accurate light transport and global illumination of a quality comparable to offline approaches in a more efficient manner.

Original languageEnglish
Title of host publicationProceedings of DRBSD-5 2019
Subtitle of host publication5th International Workshop on Data Analysis and Reduction for Big Scientific Data - Held in conjunction with SC 2019: The International Conference for High Performance Computing, Networking, Storage and Analysis
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages8-15
Number of pages8
ISBN (Electronic)9781728160177
DOIs
StatePublished - Nov 2019
Event5th IEEE/ACM International Workshop on Data Analysis and Reduction for Big Scientific Data, DRBSD-5 2019 - Denver, United States
Duration: Nov 17 2019 → …

Publication series

NameProceedings of DRBSD-5 2019: 5th International Workshop on Data Analysis and Reduction for Big Scientific Data - Held in conjunction with SC 2019: The International Conference for High Performance Computing, Networking, Storage and Analysis

Conference

Conference5th IEEE/ACM International Workshop on Data Analysis and Reduction for Big Scientific Data, DRBSD-5 2019
Country/TerritoryUnited States
CityDenver
Period11/17/19 → …

Funding

This research was supported in part by an appointment to the Oak Ridge National Laboratory ASTRO Program, sponsored by the U.S. Department of Energy and administered by the Oak Ridge Institute for Science and Education and is supported by the Exascale Computing Project (17-SC-20-SC), a collaborative effort of the U.S. Department of Energy Office of Science and the National Nuclear Security Administration.

Keywords

  • Generative adversarial network
  • In situ
  • Neural networks
  • Path-tracing
  • PyTorch
  • VTKm

Fingerprint

Dive into the research topics of 'PAVE: An in situ framework for scientific visualization and machine learning coupling'. Together they form a unique fingerprint.

Cite this