Visualization for exascale: Portable performance is critical

Abstract

Researchers face a daunting task to provide scientific visualization capabilities for exascale computing. Of the many fundamental changes we are seeing in HPC systems, one of the most profound is a reliance on new processor types optimized for execution bandwidth over latency hiding. Multiple vendors create such accelerator processors, each with significantly different features and performance characteristics. To address these visualization needs across multiple platforms, we are embracing the use of data parallel primitives that encapsulate highly efficient parallel algorithms that can be used as building blocks for conglomerate visualization algorithms. We can achieve performance portability by optimizing this small set of data parallel primitives whose tuning conveys to the conglomerates. In this paper we provide an overview of how to use data parallel primitives to solve some of the most common problems in visualization algorithms. We then describe how we are using these fundamental approaches to build a new toolkit, VTK-m, that provides efficient visualization algorithms on multi- and many-core architectures. We conclude by reviewing a comparison of a visualization algorithm written with data parallel primitives and separate versions hand written for different architectures to show comparable performance with data parallel primitives with far less development work.