Container orchestration in HPC for SDV: Kubernetes Versus Podman

#Disclaimer: My views in article are personal and not promoting any product over other.

In the rapidly evolving landscape of high-performance computing (HPC) and software-defined vehicle (SDV) development, containerization has emerged as a key technology for ensuring reproducibility, portability, and scalability. While Kubernetes often dominates discussions about container orchestration in cloud-native environments, many HPC practitioners and SDV developers are turning to lighter, more focused alternatives—one of which is Podman. This article explores why Podman can be a better fit than Kubernetes as a container orchestrator in HPC settings tailored for SDV applications.

Understanding the HPC and SDV Landscape

High-Performance Computing (HPC) environments are engineered for intensive computational tasks. Whether running large-scale simulations, data analytics, or complex numerical computations, HPC systems demand:

• Low Overhead: Every additional layer of abstraction can impact performance.
• Fine-Grained Resource Control: Direct integration with specialized job schedulers.
• Enhanced Security: Many HPC installations operate in sensitive or restricted settings where security is paramount.

Container Orchestration: Kubernetes Versus Podman

The Kubernetes Approach

Kubernetes has become the go-to container orchestration platform for many cloud-based applications because of its robust features, including:

• Scalability: It can manage thousands of containers across distributed environments.
• Resilience: Built-in mechanisms for self-healing and load balancing.
• Extensive Ecosystem: A large community, rich tooling, and extensive integrations.

However, when applied to HPC and SDV scenarios, Kubernetes can introduce several challenges:

• Complexity and Overhead: Kubernetes is designed to manage microservices across distributed clusters. The associated control plane components (e.g., etcd, API server, scheduler) add layers of complexity that may not be necessary—or even optimal—in HPC settings.
• Resource Utilization: The extra computational and memory overhead can impact the performance of compute-intensive jobs.
• Integration with HPC Schedulers: HPC environments often rely on specialized scheduling systems that are not natively designed to interact with Kubernetes. This can lead to conflicts or the need for additional integration layers.

Why Podman Stands Out

Podman offers an alternative approach that aligns well with the specific needs of HPC for SDV:

1. Lightweight and Daemonless Architecture: Unlike Kubernetes (or even Docker, which requires a background daemon), Podman runs daemonless. This design minimizes resource overhead, allowing HPC systems to devote more resources to actual computational workloads rather than managing orchestration overhead.
2. Rootless Containers for Enhanced Security: Security is a critical consideration in HPC environments. Podman’s rootless mode enables users to run containers without elevated privileges, reducing the risk of security breaches—an essential feature when handling sensitive SDV development environments.
3. Seamless Integration with Existing HPC Schedulers: HPC centers typically employ job schedulers like SLURM or PBS to manage workloads. Podman’s simplicity and flexibility mean that it can be integrated into these traditional scheduling workflows with fewer modifications, offering a more natural fit than Kubernetes, which often requires an additional orchestration layer.
4. Familiarity and Ease of Use: Podman’s command-line interface is designed to be compatible with Docker. This familiar syntax can reduce the learning curve for developers and operations teams transitioning from conventional container management tools to an HPC-centric solution.
5. Pod Support without the Complexity: While Kubernetes introduced the concept of “pods” to group related containers, Podman also supports pods—but with a focus on local grouping rather than distributed orchestration. This feature enables developers to manage related containers together, providing an effective balance between isolation and coordination without the extensive overhead of a full Kubernetes setup.

Podman in Action: Use Cases for HPC and SDV

Imagine an SDV development team that needs to run intensive simulation models to test various vehicle control algorithms. These simulations require:

• High Throughput and Low Latency: Minimizing the orchestration overhead is critical.
• Consistency Across Environments: Developers, testers, and production environments must all run identical containerized simulations.
• Secure Execution: With sensitive intellectual property at stake, running containers rootlessly is a significant advantage.

By using Podman, the team can deploy containerized simulation workloads directly within their existing HPC scheduler framework. The lack of a persistent daemon and the ability to run containers as non-root users not only streamline deployment but also reduce the risk of security vulnerabilities—providing a tailored solution for the high demands of SDV simulation.

Conclusion

While Kubernetes remains a powerful tool in the realm of distributed, cloud-native applications, its complexity and overhead can be counterproductive in HPC environments where performance and integration with traditional schedulers are paramount. Podman offers a compelling alternative for SDV and HPC workflows by delivering:

• A lightweight, daemonless, and secure container engine.
• Native compatibility with HPC scheduling systems.
• A simplified operational model that reduces overhead and complexity.

Developing and testing of software-defined vehicles within high-performance computing frameworks, embracing Podman can lead to more efficient resource utilization, enhanced security, and smoother integration with existing HPC workflows—ultimately driving innovation forward in an increasingly competitive field.