Enabling DPUs for cloud-native operations with Red Hat OpenShift
In this blog, we are going to learn how to enable DPUs for cloud-native operations with Red Hat OpenShift.
Introduction
Modern data centers are undergoing rapid transformation to deliver better performance, stronger security, and higher efficiency. At the heart of this transformation is the Data Processing Unit (DPU) — a specialized hardware component designed to handle tasks such as networking, security, and storage independently of the host CPU. Offloading these functions improves overall system performance, enhances isolation between workloads, and strengthens security.
Red Hat OpenShift enables DPUs to be smoothly incorporated into existing infrastructure environments. What sets them apart is their cloud-native compatibility: workloads already running on OpenShift or Kubernetes can be extended to DPUs without requiring application changes. Some containers can be moved off the host and executed directly on the DPU.
Standard OpenShift Node vs. DPU-Enabled Node
Deploying and Managing DPUs in OpenShift
The OpenShift DPU Operator brings vendor-agnostic management capabilities for DPUs directly into OpenShift. It simplifies deployment and configuration while reducing maintenance efforts. Thanks to intuitive custom resources (CRs), administrators can manage DPUs without needing in-depth knowledge of specific hardware vendors.
The operator simplifies tasks such as discovering DPUs, scheduling workloads, and managing their lifecycle through vendor-neutral APIs aligned with the standards of the Open Programmable Infrastructure (OPI) Project. It is designed to optimize data transfer between the host and the network, minimizing congestion and improving performance for network-heavy workloads.
By supporting standard APIs, the DPU Operator makes it possible to integrate DPUs from different vendors, letting organizations take advantage of diverse features. Administrators can set rules for workload placement, monitor DPU performance, and use familiar OpenShift tools like Prometheus and Grafana, ensuring seamless integration.
Supported DPUs utilize built-in open-source components compatible with the entire Red Hat stack, eliminating the need for complex custom solutions while providing reliable support and ease of maintenance over time.
The OpenShift DPU Operator is available in the OpenShift catalog, with its source code openly accessible on GitHub.
Benefits of Using DPUs in OpenShift
Incorporating DPUs within an OpenShift setup offers multiple benefits, creating an infrastructure that is more secure, efficient, and adaptable.
One major advantage is accelerated networking. DPUs take on the task of processing network traffic, improving throughput, and lowering latency. This is particularly beneficial for applications that demand fast, real-time performance, such as financial systems, high-speed communications, and data-heavy workloads.
DPUs also enhance security by isolating critical functions such as firewalls and encryption into a separate hardware domain. This minimizes the risk to the host system, and these protections remain operational even if the host is compromised, all without requiring application changes.
Additionally, DPUs free up host CPU resources by taking over infrastructure-level workloads. This allows the CPU to focus on business-critical applications, increasing efficiency and potentially reducing hardware costs by supporting more workloads on the same infrastructure.
For storage-heavy workloads, DPUs improve performance by managing I/O operations, exposing NVMe devices dynamically, and allowing efficient, high-speed allocation of storage resources. This is especially advantageous for use cases such as databases and analytics.
Use Cases for DPUs in OpenShift
Combining the orchestration capabilities of OpenShift with the unique strengths of DPUs enables innovative use cases:
High-Performance Networking: DPUs act as dedicated network controllers, offloading tasks like switching, routing, firewalling, and load balancing. This supports advanced traffic management, fine-grained segmentation, and detailed telemetry without impacting application resources.
AI/ML Workloads: DPUs manage AI inference tasks, reducing latency and offloading work from CPUs and GPUs to optimize overall computation. This allows AI-driven services to scale more efficiently and cost-effectively.
Advanced Security Enforcement: DPUs offer an isolated execution environment, allowing intrusion detection, microsegmentation, and deep packet inspection to run directly in the data path. These security measures remain effective even during host-level compromises.
Enhanced Storage Performance: For use cases such as distributed databases or AI training pipelines, DPUs manage storage protocols and accelerate I/O by operating at wire speed. This enables quick, low-latency access to separated storage resources and promotes more adaptable infrastructure designs.
Conclusion
When combined with Red Hat OpenShift, DPUs transform the capabilities of cloud-native infrastructure by enhancing performance, strengthening security, and streamlining workload deployment. OpenShift treats DPUs as compute resources, enabling organizations to shift workloads onto the DPU while maintaining simple and efficient operations.
As DPUs continue to gain adoption, Red Hat OpenShift is expected to expand support for additional hardware and unlock even more acceleration capabilities in the future.
