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SLURM

Self-Service Slurm Clusters on Kubernetes with Rafay GPU PaaS

In the previous blog, we discussed how Project Slinky bridges the gap between Slurm, the de facto job scheduler in HPC, and Kubernetes, the standard for modern container orchestration.

Project Slinky and Rafay’s GPU Platform-as-a-Service (PaaS) combined provide enterprises and cloud providers with a transformative combination that enables secure, multi-tenant, self-service access to Slurm-based HPC environments on shared Kubernetes clusters. Together, they allow cloud providers and enterprise platform teams to offer Slurm-as-a-Service on Kubernetes—without compromising on performance, usability, or control.

Design

Project Slinky: Bringing Slurm Scheduling to Kubernetes

As high-performance computing (HPC) environments evolve, there’s an increasing demand to bridge the gap between traditional HPC job schedulers and modern cloud-native infrastructure. Project Slinky is an open-source project that integrates Slurm, the industry-standard workload manager for HPC, with Kubernetes, the de facto orchestration platform for containers.

This enables organizations to deploy and operate Slurm-based workloads on Kubernetes clusters allowing them to leverage the best of both worlds: Slurm’s mature, job-centric HPC scheduling model and Kubernetes’s scalable, cloud-native runtime environment.

Project Slinky

Introduction to Slurm-The Backbone of HPC

This is part-1 in a blog series on Slurm. In the first part, we will provide some introductory concepts about Slurm. We are not talking about the fictional soft drink in the world of Futurama. Instead, this blog is about Slurm (Simple Linux Utility for Resource Management), an open-source, fault-tolerant, and highly scalable cluster management job scheduler and resource manager used in high-performance computing (HPC) environments.

Slurm was originally conceptualized in 2002 at Lawrence Livermore National Laboratory (LLNL) and has been actively developed and maintained especially by SchedMD. In this time, Slurm has become the defacto workload manager for HPC with >50% of the Top-500 super computers using it.

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