# HPC Workload Scheduling, with Ricardo Rocha

Page: https://stenobird.com/podcast/kubernetes-podcast-from-google/hpc-workload-scheduling-with-ricardo-rocha
Text version: https://stenobird.com/podcast/kubernetes-podcast-from-google/hpc-workload-scheduling-with-ricardo-rocha.md
Podcast: [Kubernetes Podcast from Google](https://stenobird.com/podcast/kubernetes-podcast-from-google)
Published: 2025-07-09T00:46:00+00:00
Episode link: https://e780d51f-f115-44a6-8252-aed9216bb521.libsyn.com/hpc-workload-scheduling-with-ricardo-rocha
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JSON: https://stenobird.com/v1/public/podcasts/kubernetes-podcast-from-google/episodes/hpc-workload-scheduling-with-ricardo-rocha
Duration seconds: 2578

## Resource

Ricardo Rocha from CERN explains how to bridge the gap between traditional High-Performance Computing (HPC) and cloud-native Kubernetes environments. The discussion focuses on using specialized schedulers like Kueue to manage expensive, high-demand resources like GPUs without reinventing the Kubernetes core.

## Highlights
- Main idea: Kubernetes is evolving from a stateless web-app orchestrator to a platform capable of handling complex, resource-intensive scientific workloads
- Practical takeaway: Use 'out-of-tree' controllers like Kueue to implement specialized logic (like pod suspension) while leveraging native Kubernetes primitives to avoid maintenance debt
- Failure mode: Building parallel execution systems that bypass Kubernetes semantics leads to massive technical debt as you must manually reimplement every core upstream improvement
- Main idea: The rise of expensive, pre-committed GPU instances is shifting cloud computing from an 'on-demand' model back toward a 'reservation' model similar to on-premises hardware
- Practical takeaway: Focus on extending Kubernetes via the Gateway API and specialized controllers rather than fighting the existing system's architecture

## Topics

Kubernetes, HPC, CERN, GPU Scheduling, Kueue, Cloud Native, CNCF, Batch Workloads, Infrastructure Engineering

## Chapters
- 1:00 — Node Feature Discovery and Gemini CLI: An introduction to NFD for hardware-aware scheduling and the new Gemini CLI for interacting with AI from the terminal.
- 4:05 — CERN's Resource Challenges: Discussing the necessity of optimizing fixed budgets and managing massive datasets in scientific computing.
- 7:20 — Low-Level Optimization in HPC: The shift toward needing CPU pinning, NUMA awareness, and high-efficiency node usage for research workloads.
- 10:25 — The Evolution of Kubernetes for Research: Why it took time for scientific and research-oriented voices to integrate into the Kubernetes ecosystem.
- 13:50 — Managing Expensive GPU Resources: The difficulty of managing heterogeneous hardware and preventing over-provisioning of high-cost accelerators.
- 17:00 — The Role of Specialized Schedulers: A look at the history of grid computing and how projects like Volcano and UniKorn provide necessary extensions.
- 20:05 — The CNCF Ecosystem and Maturity: How the CNCF sandbox and incubation process supports the growth of specialized cloud-native projects.
- 23:20 — Future of Batch Workloads: Speculating on multi-cluster, multi-region management and the move toward resource reservation models.

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