IEEE Computer Graphics and Applications
IEEE CG&A Special Issue on High Performance Visualization and Analysis
Final submissions due: 1 September 2015
Publication date: May/June 2016
In the 27 years since the groundbreaking report by McCormick, DeFanti, and Brown that coined the phrase “visualization in scientific computing,” we have witnessed a dramatic growth in our ability to collect and generate data. Concurrently, computing technology has rapidly evolved from single-processor systems to large scale, multi-petaflop systems comprised of 10Ks to 100Ks processors, with processors having upwards of 100s of cores per chip. The confluence of larger HPC systems, data sets of unprecedented size and complexity, and complex lines of inquiry, gives rise to diverse and difficult research challenges and opportunities for visualization and analysis that were only dimly visible at the dawn of the field of scientific visualization.
We define high performance visualization and analysis as those methods that are, by their design, capable of taking advantage of modern computational platforms, either in whole or in part. “In whole” refers to techniques that are capable of effectively using all computational resources on today’s largest computational platforms. “In part” refers to techniques that are specifically designed and implemented to take advantage of new processor or system architectures in one way or another.
The upcoming Special Issue of IEEE Computer Graphics and Applications will focus on High Performance Visualization and Analysis (HPVA). For this special issue, we solicit papers presenting original research that span a diversity of visualization and analysis topics including:
- New algorithms and methods for knowledge discovery suitable for use on modern computational platforms, methods that leverage the extreme-scale concurrency of these platforms to solve a problem of extreme scale or complexity.
- Examples of new methods for visualization and analysis that are designed to take advantage of new architectural features, such as deepening memory hierarchies, extreme-scale concurrency, etc.; methods that overcome the challenges inherent to modern HPC platforms where, for example, it is increasingly expensive to move data through the memory hierarchy and increasingly intractable to save full-resolution data to persistent storage for subsequent analysis.
- Case studies/applications of HPVA methods to solve knowledge discovery problems in physical or social science, engineering, medicine, etc. where there is a thematic element of size and/or complexity that is made tractable through the use of new scalable methods making use of modern HPC-class platforms.
Please direct any correspondence before submission to the guest editors:
- E. Wes Bethel, firstname.lastname@example.org, Lawrence Berkeley National Laboratory
- Kelly Gaither, email@example.com, University of Texas Austin
Articles should be no more than eight magazine pages, where a page is 800 words and a quarter-page image counts as 200 words. Please cite only the 12 most relevant references, and consider providing technical background in sidebars for nonexpert readers. Color images are preferable and should be limited to 10. Visit the CG&A style and length guidelines at www.computer.org/web/peer-reviewmagazines. We also strongly encourage you to submit multimedia (videos, podcasts, and so on) to enhance your article. Visit the CG&A supplemental guidelines at www.computer.org/web/peer-reviewmagazines.
Please submit your paper using the online manuscript submission service at https://mc.manuscriptcentral.com/cs-ieee. When uploading your paper, select the appropriate special-issue title under the category "Manuscript Type." Also, include complete contact information for all authors. If you have any questions about submitting your article, contact the peer review coordinator at firstname.lastname@example.org.