SONIC: Optimized Network Inference on Coprocessors

• SONIC is a platform that accelerates network-based machine learning inference by offloading computation to specialized coprocessors like GPUs and FPGAs.
• Deployed in major scientific experiments (CERN, IceCube), SONIC achieves significant throughput increases and resource pooling across heterogeneous hardware.
• Utilizing a cloud-native architecture with Kubernetes and Triton, SONIC provides a portable, scalable, and robust platform for deploying inference services across diverse environments.

Services for Optimized Network Inference on Coprocessors (SONIC) is a family of methodologies and platforms that accelerate neural network and ML inference by strategically offloading computations from general-purpose CPUs to specialized coprocessors such as GPUs, FPGAs, IPUs, and programmable network interfaces. SONIC and its evolutions address the resource, scaling, and integration challenges present in large-scale scientific, industrial, and edge computing environments by providing a hardware-agnostic, scalable, and cloud-native architecture for configurable, high-throughput inference services.

1. Foundational Principles of SONIC

SONIC is based on decoupling the ML inference workload from the main data processing application. In the SONIC architecture, client workflows—typically operating on CPUs—send inference requests to remote or local coprocessor-backed servers spatially pooled and managed independently of the client codebase. This separation enables optimized resource utilization, high concurrency, and flexible deployment.

The client-server model is central: clients package inference requests (data and model metadata), communicate over the network via standardized protocols such as gRPC, and receive inference results transparently. Server-side, SONIC adopts an inference engine (notably NVIDIA Triton Inference Server) capable of managing multiple models, dynamic batching, and load balancing across various coprocessors. This design allows for rapid adaptation to workload changes and efficient scaling in both cloud and on-premises environments (2506.20657, 2402.15366).

2. SONIC Infrastructure and Workflow

The SONIC approach, and its cloud-native realization in SuperSONIC, leverage modern infrastructure orchestration (primarily Kubernetes) to automate deployment, scaling, and monitoring of inference servers. Key infrastructure components include:

• Kubernetes: Automates resource provisioning, failure recovery, and scaling of inference servers and network proxies.
• Helm Charts: Facilitate deployment and versioning of SONIC components.
• NVIDIA Triton Inference Server: Provides pluggable support for diverse model frameworks and hardware backends, supports in-memory model management, batching, and concurrent multi-model serving.
• Envoy Proxy: Handles ingress, load balancing (e.g., round-robin), rate limiting, and authentication (token-based).
• Monitoring and Autoscaling: Prometheus collects metrics; Grafana exposes dashboards; KEDA dynamically scales servers in response to real-time inference demand.

This infrastructure enables transparent horizontal scaling, fault tolerance, and observability, ensuring consistent low-latency inference—even under bursty or unpredictable loads. The microservices design allows the same platform to be deployed from small university clusters to large research clouds spanning hundreds of GPUs (2506.20657, 2402.15366).

3. Applications and Performance Impact

SONIC and SuperSONIC have been deployed in major scientific experiments including CMS and ATLAS at the CERN LHC, IceCube, and LIGO. The system efficiently accelerates various workflows:

• High-Energy Physics: Jet tagging with GNNs (ParticleNet) and other ML services in CMS/ATLAS, supporting both ML and non-ML offload (2506.20657, 2402.15366).
• Astrophysics: CNN-based event filtering in IceCube and ML-based data analysis in LIGO.

In these deployments, SONIC achieves:

• Increased throughput: For example, in the CMS Mini-AOD workflow, throughput improved from 3.5 to 4.0 events/sec (+13%) when 10,000 CPU cores were supported by ~100 GPUs (2402.15366).
• Resource pooling: A single GPU can serve up to 160 multi-threaded CPU jobs on representative models, maximizing accelerator usage.
• Portability: Workflows can be easily retargeted to different coprocessors (GPUs, IPUs, FPGAs) and across distributed infrastructure with minimal code or configuration changes.
• Elastic autoscaling: Dynamic scaling of inference servers according to load ensures high utilization during peak demand and cost savings during idle periods.

This approach also improves manageability and performance in cross-site deployments, as location and coupling between data processing and accelerator hardware are decoupled.

4. Integration, Generalization, and Portability

SONIC is architected for portability, supporting:

• Heterogeneous coprocessors: Plug-in backends for GPUs (NVIDIA T4, V100), IPUs (e.g., Graphcore), FPGAs, and CPUs (2506.20657, 2402.15366).
• Multiple ML frameworks: PyTorch, TensorFlow, ONNX, XGBoost, and custom frameworks are supported via Triton backends.
• Client-side abstraction: Clients remain agnostic to hardware and backend specifics, lowering barrier for adoption and maintenance.
• Deployment flexibility: Supports on-premises, campus, cloud, and mixed environments through cloud-native design, minimizing operational complexity.

Portability is further strengthened by containerization and abstraction within the client software (e.g., via SonicCore, SonicTriton packages in CMS workflows), which separate workflow logic from model and server implementation details.

5. Autoscaling, Monitoring, and Operational Robustness

SuperSONIC provides detailed telemetry and adaptive resource management:

• Prometheus and Grafana supply real-time statistics on inference rates, utilization, and server health.
• KEDA and autoscalers respond quickly to changing demand, scaling the number of active inference servers and GPUs.
• Application-level load balancers (Envoy) provide failover, authentication, and queue management.
• Security: Token-based authentication at proxy level separates workloads and users, supporting multi-tenant use cases.

Performance benchmarks confirm that autoscaling maintains lower latency, reduces SLO violations, and prevents resource idleness compared to static server allocation (2506.20657).

6. Open Challenges and Evolution

While SONIC and SuperSONIC have demonstrated success in large-scale, production scientific settings, ongoing and future directions include:

• Supporting additional coprocessor types: Plans to add AMD/Intel GPUs, IPUs, TPUs, and custom ASICs, as well as related model-serving connectors (e.g., PyTriton).
• Workflow and model diversity: Adapting to new types of inference tasks (e.g., transformer models, graph-based inference), streaming/batch hybrid processing, and ultra-low-latency applications.
• Integration with data management frameworks: Combining with data movement and orchestration systems to deliver end-to-end optimized scientific pipelines (2203.08280).
• Further automation: Predictive scaling, energy-aware scheduling, and federated multi-cluster deployments across scientific or industrial domains.
• Broader applicability: Adoption in industries with elastic, distributed inference demands, such as IoT, finance, and real-time analytics.

The platform is positioned as reusable and configurable, lowering the cost and complexity of deploying accelerator-based inference across diverse scientific and industrial domains (2506.20657).

7. Summary Table: SuperSONIC Core Attributes

Feature	Description	Demonstrated Impact
Decoupled inference serving	Client workflows outsource inference to pooled accelerator servers	Improved resource efficiency
Cloud-native deployment	Kubernetes, microservices, Helm charts, autoscaling, integrated monitoring	Scalability and reliability
Model/backend support	Multi-framework (PyTorch, TensorFlow, ONNX, XGBoost, custom), coprocessor-agnostic	Portability and flexibility
Autoscaling and load balancing	KEDA, Envoy, per-client protocol, dynamic queue management	Low latency, high utilization
Production usage	CMS, ATLAS, IceCube, LIGO, major university clusters, research platforms	Performance and operational gains

Conclusion

SONIC and SuperSONIC define a cloud-native, accelerator-agnostic architecture for machine learning inference, enabling scalable, efficient, and portable deployment of complex models across distributed and heterogeneous computing infrastructures. By abstracting accelerator management from workflows, standardizing network communication, and leveraging modern orchestration, SONIC significantly enhances efficiency and agility in scientific and potentially broader industrial ML applications (2506.20657, 2402.15366).