research-article

Vapor 3: A modern 3D visualization suite for Geophysical Sciences

Author:

Scott PearseAuthors Info & Claims

PEARC '19: Practice and Experience in Advanced Research Computing 2019: Rise of the Machines (learning)

Article No.: 131, Pages 1 - 3

https://doi.org/10.1145/3332186.3341299

Published: 28 July 2019 Publication History

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Abstract

Vapor v3.1 is an open-source, cross platform visualization and analysis tool for finite element simulations in the physical sciences. Vapor offers users a set of tools called Renderers that let them depict their simulation data in different ways.

With this most recent release, Vapor has undergone a significant refactorization of its code base which has spanned a 15 year history. Much of Vapor's old code has been discarded and rewritten to improve its ease of use, maintainability, and rendering quality. These improvements aim to help scientists easily demonstrate their simulations in a way that results in superior communication of their research.

The video pertaining to this submission will demonstrate how Vapor is used, new features in the current version, and how things have changed since the previous major release, Vapor 2.6.

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Published In

PEARC '19: Practice and Experience in Advanced Research Computing 2019: Rise of the Machines (learning)

July 2019

775 pages

ISBN:9781450372275

DOI:10.1145/3332186

General Chair:
Tom Furlani
Roswell Park Comprehensive Cancer Center

© 2019 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of the United States government. As such, the United States Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 28 July 2019

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PEARC '19

PEARC '19: Practice and Experience in Advanced Research Computing

July 28 - August 1, 2019

IL, Chicago, USA

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Overall Acceptance Rate 133 of 202 submissions, 66%

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