Distributed and Heterogeneous SAR Backprojection with Halide
Connor Imes, Tzu‐Mao Li, Mark Glines, Rishi Khan, John Paul Walters · 2021
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 implement a backprojection algorithm for SAR image reconstruction and demonstrate scalability on the OLCF Summit supercomputer up to 1,024 compute nodes (43,008 cores, each with 4 hardware threads) with a large 32,768×32,768 dataset, and up to 8 distributed GPUs with a 8,192×8,192 dataset. Our results show excellent scaling and portability to heterogeneous resources, and motivate additional improvements in Halide to better support distributed high-performance signal processing.