Efficient Dynamic Derived Field Generation on Many-Core Architectures Using Python

Derived field generation is a critical aspect of many visualization and analysis systems. This capability is frequently implemented by providing users with a language to create new fields and then translating their "programs" into a pipeline of filters that are combined in sequential fashion. Although this design is highly extensible and practical for development, the runtime characteristics of the typical implementation are poor, since it iterates over large arrays many times. As we reconsider visualization and analysis systems for many-core architectures, we must re-think the best way to implement derived fields while being cognizant of data movement. In this paper, we describe a flexible Python-based framework that realizes efficient derived field generation on many-core architectures using OpenCL. Our framework supports the development of different execution strategies for composing operations using a common library of building blocks. We present an evaluation of our framework by testing three execution strategies to explore tradeoffs between runtime performance and memory constraints. We successfully demonstrate our framework in an HPC environment using the vortex detection application on a large-scale simulation.