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RESILIO : A Scalable and Composable Architecture for Tomographic Reconstruction Workflows
DescriptionTomographic reconstruction (TR) aims to reconstruct a 3D object from 2D projections. It is an important technique across domains such as medical imaging and materials science, where high-resolution volumetric data is essential for decision-making. With advanced facilities such as the upgraded APS enabling unprecedented data acquisition rates, TR pipelines struggle to handle large data volumes while maintaining low latency, 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 persistent streaming and fully leverages HPC platforms. Our design enables scalable and elastic execution across heterogeneous environments. We contribute a reimagined TR architecture, its implementation using Mochi, and an empirical evaluation showing up to 3490× reduction in the per-event overhead compared to the original implementation, and up to 3268× improvement in throughput with performance-tuned configurations using Mofka.
Author/Presenters
Event Type
Workshop
TimeMonday, 17 November 20259:06am - 9:24am CST
Location264
Livestreamed
Recorded
TP
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