RESILIO : A Scalable and Composable Architecture for Tomographic Reconstruction Workflows

Amal Gueroudji, Matthieu Dorier, Philip Carns, Parth Patel, Tekin Bicer, Robert Latham, Robert Ross, Kyle Chard, Ian Foster

Abstract: Tomographic reconstruction (TR) aims to reconstruct a 3D object from a finite number of 2D projections. It is an important technique across domains such as medical imaging, materials science, and security, where high-resolution volumetric data is essential for decision-making. With advanced facilities such as the upgraded Advanced Photon Source enabling unprecedented data acquisition rates, TR pipelines struggle to handle large data volumes while maintaining low latency in time-sensitive domains, fault tolerance, and scalability. Traditional, tightly coupled, batch-oriented workflows are increasingly inadequate in such high-performance contexts. In response, we propose ResiliO , a composable, high-performance TR framework built atop the Mochi ecosystem that uses Mofka to improve resilience through persistent streaming and fully leverage high-performance computing platforms. Our design enables scalable and elastic execution across heterogeneous environments and programming models and languages. We contribute a reimagined TR pipeline architecture, its implementation using Mochi services, and an empirical evaluation showing up to 3490 × reduction in the per-event overhead compared to the original ZeroMQ implementation, and up to 3268 × improvement in throughput with performance-tuned configurations using Mofka.

External IDs:doi:10.1145/3731599.3767575