Towards Exascale: High Performance Visualization and Analytics – Project Status Report
The LBNL Visualization research program focuses on a key set of problems facing our community as we move towards the exascale regime of computing. First, we aim to better understand how to effectively take advantage of an evolving architectural landscape where there are an increasing number of cores per processor by examining algorithmic reformulation and alternative methods of programming these increasingly complex platforms. Second, as the gap widens between computational and I/O capacity, it is increasingly imperative to perform more visual data exploration and analysis operations concurrent with the simulation to avoid increasingly expensive I/O. Our approach is to examine several interrelated issues aimed at minimizing the memory footprint, data movement, and design/implementation issues related to deploying tools that can have broad applicability across many different simulation codes and science domains. Third, increasing computational power produces larger and more complex scientific data, and our research program focuses on new technologies aimed at enabling scientific data understanding with ever-larger data. Here, we explore how multi-resolution data representation techniques can reduce data movement, and study novel approaches that focus visualization and analysis processing on subsets of data that are “scientifically interesting,” thereby also minimizing data movement while accelerating scientific knowledge discovery.