SuperSONIC: Scalable ML Inference

• SuperSONIC is a cloud-native infrastructure designed for high-throughput ML inference in data-intensive scientific workflows.
• It leverages Kubernetes, dedicated GPUs, and industry-standard tools like NVIDIA Triton and Envoy for efficient deployment.
• Its modular design supports dynamic autoscaling, real-time monitoring, and seamless integration into high energy physics and astrophysics experiments.

SuperSONIC refers to a cloud-native server infrastructure specifically designed for scalable, high-throughput ML inferencing in large, data-intensive scientific workflows. Developed as an extension and generalization of the Services for Optimized Network Inference on Coprocessors (SONIC) paradigm, SuperSONIC enables efficient deployment of computationally intensive ML tasks onto Kubernetes clusters with dedicated accelerator resources (notably GPUs), decoupling the client workflow from the inference infrastructure. By integrating industry-standard tools for orchestration, load balancing, monitoring, autoscaling, and security, SuperSONIC serves as a reusable and adaptable middleware layer for accelerator-based ML inference in high energy physics (HEP), multi-messenger astrophysics (MMA), and related scientific and industrial applications (2506.20657).

1. System Architecture and Design Principles

SuperSONIC is built as a modular, microservices-based infrastructure operating on Kubernetes clusters. Its design follows the principle of decoupling: experimental clients, running complex data processing or physics analysis software, outsource ML inference tasks to centrally managed SuperSONIC servers. This separation allows for independent evolution and scaling of experimental workflows and the underlying accelerator infrastructure.

Key architectural components include:

• NVIDIA Triton Inference Server: Serving as the inference backend, Triton loads and executes ML models (e.g., GNNs, CNNs, transformers) from configurable repositories. Model storage is handled via Kubernetes persistent storage, CVMFS, or mounted NFS volumes.
• Envoy Proxy: Functions as a gateway between clients and Triton servers. It provides efficient request routing, load balancing (e.g., round robin algorithm), rate limiting, and token-based authentication, abstracting connection details from clients.
• Monitoring and Tracing: Prometheus and Grafana are deployed for time-series metrics and dashboarding, with OpenTelemetry and Grafana Tempo integrated for distributed tracing. This instrumentation enables real-time visibility into system health, usage, and request flows.
• Deployment and Portability: SuperSONIC is packaged with Helm charts for portability, supporting deployments that range from small “kind” clusters (e.g., GitHub Actions runners with 4 CPUs and 16 GB RAM) to large, GPU-rich computing centers.

This composable design supports rapid adaptation to novel accelerators and experimental requirements, in line with evolving ML/AI hardware and scientific workflows.

2. Deployment Patterns and Scalability

SuperSONIC is engineered for both vertical and horizontal scaling within cloud-native environments:

• Autoscaling: The integration of Kubernetes-based Event Driven Autoscaling (KEDA) allows SuperSONIC to dynamically adjust the number of Triton inference server instances according to real-time workload metrics—most notably, the average inference request queue latency observed across active servers. As the demand increases (e.g., a surge in parallel inference requests from multiple clients), KEDA rapidly spins up additional GPU-enabled Triton containers. Conversely, under low-load conditions, unused resources are deprovisioned.
• Load Balancing: Incoming requests are distributed across Triton instances by Envoy, which uses algorithms such as round robin to prevent overloading any individual server and to maximize GPU utilization.

The platform is validated in production at leading experiments, including CMS and ATLAS at CERN’s LHC, IceCube, and LIGO, as well as at facilities like the National Research Platform (NRP), Purdue University, and the University of Chicago. SuperSONIC’s scaling model has been demonstrated to support up to 100 concurrent GPU-backed Triton servers, sustaining high aggregate inference throughput (2506.20657).

3. Performance Optimization and Monitoring

Performance considerations in SuperSONIC focus on minimizing latency, maximizing utilization, and ensuring throughput predictability:

• Queue Latency Metric: Autoscaling decisions are driven by the observed average inference queue latency $L_\text{avg} = \frac{1}{n}\sum_{i=1}^n L_i$, where $L_i$ is the inference queue latency at the $i$th Triton server. When $L_\text{avg}$ exceeds a set threshold, new servers are spawned; if it drops, servers are decommissioned.
• Observability: Continuous monitoring of GPU engine/memory utilization and per-model inference rates feeds into both dashboarding and scaling logic. This holistic view helps identify bottlenecks and optimize rate limiting/routing policies.
• Standardized Communication: Envoy proxies standardize communications, providing a uniform API to clients regardless of backend configuration or server distribution, thus streamlining integration into diverse workflows and easing migration between infrastructures.

This tightly integrated pipeline enables SuperSONIC to deliver low-latency, high-throughput inference even under fluctuating and unpredictable loads common in modern experimental science.

4. Applications in Scientific Workflows

SuperSONIC is designed to meet the needs of data-intensive experiments that require rapid, real-time, or high-throughput ML inference:

• High Energy Physics: At CMS and ATLAS (CERN), SuperSONIC enables “offloading” of GNN, CNN, and transformer model inference from experimental nodes to centralized GPU clusters, significantly increasing analysis throughput and optimizing the use of specialized resources.
• Astrophysics and MMA: In the IceCube and LIGO projects, SuperSONIC serves inference for deep neural networks used in real-time event detection and data stream processing, where quick turnaround is essential for multi-messenger follow-up.
• Resource Utilization and Standardization: Across these use cases, SuperSONIC provides a unified communication and execution platform, reducing both software and operational overhead, and enabling better cost-efficiency by dynamically matching computational resources to experimental needs.

The system has also proven valuable for accelerator offloading of non-ML tracking algorithms, indicating versatility in serving heterogeneous computational workloads.

5. Cloud-Native Challenges and Solutions

Scientific computing increasingly faces challenges in efficiently orchestrating heterogeneous resources (e.g., mixing CPU, GPU, and possibly other coprocessor architectures) under variable demand:

• Decoupling and Reusability: By abstracting hardware details behind a reusable, configurable framework, SuperSONIC avoids co-location constraints and allows dynamic allocation of expensive resources only when needed.
• Observability and Troubleshooting: The inclusion of real-time monitoring and distributed tracing provides actionable insights into system performance and enables rapid identification of configuration or bottleneck issues.
• Industry Standard Practices: Adopting Kubernetes, Helm, Prometheus, Envoy, and related open tools ensures maintainability and broad compatibility with the evolving cloud-native ecosystem.

This approach addresses core limitations of previous, more static hardware utilization strategies, thus aligning well with the operational realities of contemporary experimental science.

6. Prospects and Future Developments

Planned future directions for SuperSONIC include:

• Multi-vendor Accelerator Support: Extending support to additional accelerator vendors (e.g., AMD or via PyTriton) to further expand hardware flexibility.
• Enhanced Autoscaling and Scheduling: Refinement of scaling and load balancing algorithms for greater granularity and more predictive scaling, possibly incorporating advanced telemetry and ML-based capacity planning.
• Automation and User Experience: Continued improvement of deployment workflows, monitoring dashboards, and troubleshooting aids to support even more diverse and large-scale research collaborations.

The overarching goal is to maintain adaptability as experimental requirements, ML algorithms, and hardware evolve, making SuperSONIC a sustainable foundation for accelerator-based inference across numerous scientific and industrial domains.

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In summary, SuperSONIC defines a cloud-native paradigm for accelerator-driven ML inference infrastructure, providing scalable, efficient, and portable solutions to the challenges posed by high-volume, computationally intensive scientific experimentation (2506.20657). Its modular architecture, autoscaling design, integrated observability, and proven applicability in large collaborative research settings position it as a versatile resource for the modern data-driven scientific enterprise.