In evolutionary multiobjective optimization, it is very important to be able to visualize approximations of the Pareto front (called approximation sets) that are found by multiobjective evolutionary algorithms. While scatter plots can be used for visualizing 2-D and 3-D approximation sets, more advanced approaches are needed to handle four or more objectives. This paper presents a comprehensive review of the existing visualization methods used in evolutionary multiobjective optimization, showing their outcomes on two novel 4-D benchmark approximation sets. In addition, a visualization method that uses prosection (projection of a section) to visualize 4-D approximation sets is proposed. The method reproduces the shape, range, and distribution of vectors in the observed approximation sets well and can handle multiple large approximation sets while being robust and computationally inexpensive. Even more importantly, for some vectors, the visualization with prosections preserves the Pareto dominance relation and relative closeness to reference points. The method is analyzed theoretically and demonstrated on several approximation sets.

This talk will describe our research focus. It will first define “HPC in the Small” and “Abstract Execution Models” (AEMs). It will then describe our execution model, a variant of task graphs, and how we have used these task graphs to accelerate a variety of applications by 2—4 orders of magnitude along with an overview of these applications.

Bio:

Walid Keyrouz joined NIST as a research scientist in 2011. His research interests are in High Performance Computing and Engineering Design. He has an undergraduate degree in Engineering from the American University of Beirut and MS and PhD from Carnegie Mellon University. His first exposure to parallel computing was during his Master’s degree in the early 1980s and has used GPUs as compute engines since 2006.