Scientific discovery at the extreme scale is a unique technical challenge, requiring the reduction of massive amounts of data into compact analysis products that capture key scientific insights. This analysis process needs to occur under extreme scale computational constraints including minimizing 1) data movement, 2) energy usage and 3) storage usage. Put simply, extreme scale computing platforms are to achieve a three orders of magnitude increase in computational performance while consuming only two times the electrical power of current platforms. Data movement costs will dominate energy usage at this scale, so the HPC community expects extreme scale analysis algorithms will be utilized to reduce simulation results insitu – that is, during the simulation run. This reduction will occur, broadly speaking, via some type of adaptive sampling, such as signal, statistical or feature-based sampling.
We are investigating how changes in our sampling algorithms – necessary because of exascale power constraints – impact the cognitive value of the resulting data. Our goal is perceptual and cognitive optimization of tools that enhance analytic workflows while minimizing power consumption. We will pursue a three phase approach to the research, isolating specific portions of the potentially vast experimental space, in order to be able to draw conclusions across scales – from supercomputers to compute nodes, down to the subsystems of the compute node. We have assembled a team of experts from the diverse areas of Algorithms, Power, and Cognition/Perception to address the issues inherent in the optimization of the human/compute system at exascale.
The ECX project has resulted in several online communities that include a variety of open source software, websites, interactive tools, and information that you can take advantage of. We invite you to use these tools, and add to them as well.
An open source toolkit designed to help you conduct image-based perceptual studies on your own visualizations. See the ETK Wiki for details.
The ECX Experimental Test Harness (ETH) Experimental Test Harness GitHub
A lightweight toolkit for exploring data analysis and visualization workflows across a large parallel design space.
This website collects a range of research on colormaps, color schemes, and interactive creation of tuned colormaps.
colormeasures.org ColorMeasures Tool
An online tool to enable you to mathematically analyze the properties of a colormap in multiple color spaces. The mathematical measures calculated by the ColorMeasures tool are described in a paper presented at IEEE VIS 2017: The Good, the Bad, and the Ugly: A Theoretical Framework for the Assessment of Continuous Colormaps. These measures can help you to understand both local and global perceptual properties of the colormap tested. These properties include: uniformity, discriminative power, and order.
The ECX project has a GitHub repository. All of the open-source code released under the ECX grant can be found at https://github.com/ascr-ecx Contact [email protected] with questions about this work, and any of the related websites.