astroCAMP: A Community Benchmark and Co-Design Framework for Sustainable SKA-Scale Radio Imaging

TL;DR

astroCAMP is a comprehensive benchmarking and co-design framework for large-scale radio imaging in SKA, addressing performance, sustainability, and fidelity challenges with standardized metrics and multi-objective optimization.

Contribution

It introduces a unified metric suite, standardized datasets, a multi-objective co-design formulation, and a workflow for Pareto-optimal exploration tailored for SKA-scale imaging.

Findings

WSClean+IDG reveals bottlenecks and limited CPU scaling on AMD EPYC and NVIDIA H100.

astroCAMP demonstrates heterogeneous CPU-FPGA exploration capabilities.

Calls for community-defined fidelity thresholds to improve optimization.

Abstract

The Square Kilometre Array (SKA) will operate one of the world's largest continuous scientific data systems, sustaining petascale imaging under strict power envelopes. Current radio-interferometric pipelines typically achieve only 4-14% of hardware peak utilization due to memory and I/O bottlenecks, incurring high energy, operational, and carbon costs, further compounded by the absence of standardised cross-layer metrics and fidelity tolerances for principled hardware--software co-design. We present astroCAMP, a reproducible benchmarking and co-design framework for SKA-scale imaging, contributing: (1) a unified metric suite spanning performance, utilisation, memory/data-movement, sustainability, economics, and scientific fidelity; (2) standardised SKA-representative datasets and benchmark configurations for reproducible cross-platform evaluation; (3) a multi-objective co-design formulation linking quality constraints to time-, energy-, carbon-, and cost-to-solution; and (4) a design-space exploration workflow to derive Pareto-optimal operating regions. We evaluate WSClean+IDG on an AMD EPYC 9334 CPU and NVIDIA H100 GPU, revealing orchestration and synchronization bottlenecks despite efficient kernels, limited CPU strong scaling, and location-dependent carbon/cost efficiency. We illustrate astroCAMP for heterogeneous CPU--FPGA exploration and call on the SKA community to define quantifiable fidelity thresholds to accelerate principled optimisation for SKA-scale imaging.