Enterprise AI Analysis
RESILIO: A Scalable and Composable Architecture for Tomographic Reconstruction Workflows
This paper introduces RESILIO, a composable and high-performance tomographic reconstruction framework built on the Mochi ecosystem. It leverages Mofka for persistent streaming to achieve resilience, scalability, and elastic execution across heterogeneous environments. The framework significantly reduces overhead and improves throughput compared to traditional ZeroMQ implementations, addressing critical challenges in data-intensive scientific imaging at facilities like the Advanced Photon Source.
Executive Impact
Key performance indicators from the RESILIO framework's empirical evaluation.
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Reduction in per-event overhead vs. ZeroMQ.
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Improvement in throughput with Mofka performance-tuned configurations.
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Aggregated throughput growth for batch size 1 with 2 to 32 partitions.
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
3490x
Reduction in per-event overhead (Mofka vs ZeroMQ)
RESILIO's Mofka-based architecture significantly reduces overhead, achieving up to 3490x lower per-event overhead compared to the original ZeroMQ implementation due to high-performance networking and communication-computation overlap.
Enterprise Process Flow
DAQ (Data Acquisition)
→
Mofka Servers (Persistent Streaming)
→
DIST (Data Distribution)
→
SIRT (Iterative Reconstruction)
→
DEN (Denoising)
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Reconstructed Images
The new RESILIO architecture leverages Mofka as a persistent streaming backbone, decoupling components and enabling independent, asynchronous operation, fault tolerance, and scalable data flow. This visualizes the reimagined tomographic reconstruction pipeline.
3268x
Throughput improvement with Mofka (DIST producer)
Performance-tuned Mofka configurations yield up to 3268x throughput improvement for the DIST producer, highlighting the importance of batch size and partition distribution.
| Feature | Mofka (RESILIO) | ZeroMQ (Original) |
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| Fault Tolerance |
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| Scalability |
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| Decoupling |
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| Asynchronous Execution |
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A detailed comparison highlights Mofka's advantages in overhead, resilience, scalability, decoupling, and asynchronous execution for HPC workflows over traditional ZeroMQ.
Mofka's Role in Next-Gen Scientific Workflows
Mofka’s persistent streaming architecture introduces fault tolerance by default, storing each event on the server side and enabling independent failure recovery. This is crucial in beamline settings where experiment time is scarce and data loss is costly. The ability for producers and consumers to operate at different rates, scales, and times without direct awareness simplifies integration across heterogeneous components (different languages, runtimes, hardware), enabling seamless incorporation of AI-based denoising modules and other diverse tools.
Explore how Mofka’s persistent streaming capabilities contribute to resilience and enable the integration of diverse computational components in modern scientific workflows, addressing challenges like data loss and heterogeneous environments.
Calculate Your Potential AI ROI
Estimate the efficiency gains and cost savings your enterprise could achieve by integrating advanced AI solutions like RESILIO.
Estimated Annual Savings
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Annual Hours Reclaimed
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Implementation Roadmap
A phased approach to integrate RESILIO and similar AI-driven solutions into your enterprise.
Phase 1: Prototype Validation & Expansion
Transition RESILIO from prototype to production-ready by integrating a direct interface to experimental instruments at beamlines. Validate under real-time data acquisition conditions.
Phase 2: Hierarchical Streaming & Cloud Integration
Integrate hierarchical streaming architecture (Diaspora) for multi-level event dissemination across compute tiers, including edge devices, distributed facilities, and cloud platforms (Octopus).
Phase 3: Intelligent & Adaptive Components
Expand RESILIO with LLMs and AI techniques for semantic metadata enrichment, anomaly detection, intelligent data triage, and experiment steering based on real-time feedback.
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