A study of scientific visualization on heterogeneous processors using Legion

Abstract

We present a study of scientific visualization on heterogeneous processors using the Legion runtime system. We describe the main functions in our approach to conduct scientific visualization that can consist of multiple operations with different data requirements. Our approach can help users simplify programming on the data partition, data organization and data movement for distributed-memory heterogeneous architectures, thereby facilitating a simultaneous execution of multiple operations on modern and future supercomputers. We demonstrate the scalable performance and the easy usage of our approach by a hybrid data partitioning and distribution scheme for different data types using both CPUs and GPUs on a heterogeneous system.