| Abstract: |
| Many scientific domains, including 3D tomography and radio astronomy, rely on solving large-scale imaging inverse problems, where a single data sample can reach sizes of 100 million to 1 billion pixels, making both computation and memory critical challenges.
This talk introduces a benchmarking framework, built on Benchopt, designed to evaluate the scalability of modern inverse problem solvers on HPC systems. We focus in particular on recent deep learning-based approaches, such as Plug-and-Play methods. The benchmark enables controlled comparisons of reconstruction quality, runtime, and memory usage as computational resources are scaled across distributed architectures.
Beyond performance evaluation, this work aims to establish a common ground between methodological innovation and large-scale scientific applications, enabling the development of algorithms that are not only accurate, but also scalable and practically deployable in real-world HPC settings. |
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