On-Demand Visualization on Scalable Shared Infrastructure

Abstract

The emergence of high-resolution simulation, where simulation outputs have grown to terascale levels and beyond, raises major new challenges for the visualization community, which is serving computational scientists who want adequate visualization services provided to them on-demand. Many existing algorithms for parallel visualization were not designed to operate optimally on time-shared parallel systems or on heterogeneous systems. They are usually optimized for systems that are homogeneous and have been reserved for exclusive use. This chapter explores the possibility of developing parallel visualization algorithms that can use distributed, heterogeneous processors to visualize cutting edge simulation datasets. The authors study how to effectively support multiple concurrent users operating on the same large dataset, with each focusing on a dynamically varying subset of the data. From a system design point of view, they observe that a distributed cache offers various advantages, including improved scalability. They develop basic scheduling mechanisms that were able to achieve fault-tolerance and load-balancing, optimal use of resources, and flow-control using system-level back-off, while still enforcing deadline driven (i.e. time-critical) visualization.