Abstract

Writing efficient, scalable, and portable HPC synthetic aperture radar (SAR) applications is increasingly challenging due to the growing diversity and heterogeneity in distributed systems. Considerable developer and computational resources are often spent to port applications to new HPC platforms and architectures, which is both time consuming and expensive. Domain-specific languages have been shown to be highly productive for development effort, but additionally achieving both scalable computational efficiency and platform portability remains challenging. The Halide programming language is both productive and efficient for dense data processing, supports common CPU architectures and heterogeneous resources like GPUs, and has previously been extended for distributed processing. We propose to use a distributed Halide implementation for scalable and heterogeneous HPC SAR processing. We …

Date

September 20, 2021

Authors

Connor Imes, Tzu-Mao Li, Mark Glines, Rishi Khan, John Paul Walters

Conference

2021 IEEE High Performance Extreme Computing Conference (HPEC)

Pages

1-9

Publisher

IEEE