It's a Scheduling Affair: GROMACS in the Cloud with the KubeFlux Scheduler

Misale, Claudia; Drocco, Maurizio; Milroy, Daniel J.; Gutierrez, Carlos Eduardo Arango; Herbein, Stephen; Ahn, Dong H.; Park, Yoonho

doi:10.1109/canopiehpc54579.2021.00006

Cited by 12 publications

(5 citation statements)

References 7 publications

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“…This design was chosen to mirror previous work. 18 LAMMPS is used by the Collaboration of Oak Ridge, Argonne, and Livermore (CORAL) as a representative scalable science benchmark as part of CORAL-2 . In testing, LAMMPS runs in parallel on MPI ranks (processes) across nodes, a molecular simulation 28 and the problem size 64×16×16 was chosen that would adequately test strong scalability across the chosen rank and node counts.…”

Section: Resultsmentioning

confidence: 99%

“…16 Enabling the Flux scheduler in Kubernetes would bring this same graph-based and hierarchical resource-aware approach to Kubernetes, and this early work demonstrated exactly thatimproved performance against the default Kubernetes scheduler. 18 More efficient scheduling was demonstrating by enabling MPIbased CORAL2 workloads to run and scale in Kubernetes that, by way of Fluence, avoided pathological resource mappings and resource starvation. 14 This work also demonstrated a valuable pointthat the scheduling provided by a workload manager must be able to concretely meet the resource demands of a workflow, but to do so efficiently and effectively to maximally utilize a set of computational resources.…”

Section: Introductionmentioning

confidence: 99%

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The Flux Operator

Sochat,

Culquicondor,

Ojea

et al. 2024

F1000Res

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Converged computing is an emerging area of computing that brings together the best of both worlds for high performance computing (HPC) and cloud-native communities. The economic influence of cloud computing and the need for workflow portability, flexibility, and manageability are driving this emergence. Navigating the uncharted territory and building an effective space for both HPC and cloud require collaborative technological development and research. In this work, we focus on developing components for the converged workload manager, the central component of batch workflows running in any environment. From the cloud we base our work on Kubernetes, the de facto standard batch workload orchestrator. From HPC the orchestrator counterpart is Flux Framework, a fully hierarchical resource management and graph-based scheduler with a modular architecture that supports sophisticated scheduling and job management. Bringing these managers together consists of implementing Flux inside of Kubernetes, enabling hierarchical resource management and scheduling that scales without burdening the Kubernetes scheduler. This paper introduces the Flux Operator – an on-demand HPC workload manager deployed in Kubernetes. Our work describes design decisions, mapping components between environments, and experimental features. We perform experiments that compare application performance when deployed by the Flux Operator and the MPI Operator and present the results. Finally, we review remaining challenges and describe our vision of the future for improved technological innovation and collaboration through converged computing.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Flux Operator

Sochat,

Culquicondor,

Ojea

et al. 2024

F1000Res

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“…In [9], the authors address the problem of running HPC workloads efficiently on Kubernetes clusters. They compare the Kubernetes' default scheduler with KubeFlux, a Kubernetes plugin scheduler built on the Flux graph-based scheduler, on a 34-node Red Hat OpenShift cluster on IBM Cloud.…”

Section: Related Workmentioning

confidence: 99%

A Methodology to Scale Containerized HPC Infrastructures in the Cloud

Grenèche

Menouer²,

Cérin

et al. 2022

Lecture Notes in Computer Science

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This paper introduces a generic method to scale HPC clusters on top of the Kubernetes cloud orchestrator. Users define their targeted infrastructure with the usual Kubernetes syntax for recipes, and our approach automatically translates the description to a full-fledged containerized HPC cluster. Moreover, resource extensions or shrinks are handled, allowing a dynamic resize of the containerized HPC cluster without disturbing its running. The Kubernetes orchestrator acts as a provisioner. We applied the generic method to three orthogonal architectural designs Open Source HPC schedulers: SLURM, OAR, and OpenPBS. Through a series of experiments, the paper demonstrates the potential of our approach regarding the scalability issues of HPC clusters and the simultaneous deployment of several job schedulers in the same physical infrastructure. It should be noticed that our plan does not require any modification either in the containers orchestrator or in the HPC schedulers. Our proposal is a step forward to reconciling the two ecosystems of HPC and cloud. It also calls for new research directions and concrete implementations for the dynamic consolidation of servers or sober placement policies at the orchestrator level. The works contribute a new approach to running HPC clusters in a cloud environment and test the technique on robustness by adding and removing nodes on the fly.

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“…They used the Kubernetes default scheduler. Misale et al [19] introduced KubeFlux, a Kubernetes plugin scheduler that is based on Flux graph-based scheduler. This plugin translates the Pod into a Flux job and uses the policy within Fluxion to allocate jobs.…”

Section: Deployment and Scheduling Schemes For Containerized Hpc Work...mentioning

confidence: 99%

Fine-Grained Scheduling for Containerized HPC Workloads in Kubernetes Clusters

Liu

Guitart

2022

2022 IEEE 24th Int Conf on High Performance Computing &Amp; Communications; 8th Int Conf on Data Science &Amp; Systems; 20th In

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Containerization technology offers lightweight OSlevel virtualization, and enables portability, reproducibility, and flexibility by packing applications with low performance overhead and low effort to maintain and scale them. Moreover, container orchestrators (e.g., Kubernetes) are widely used in the Cloud to manage large clusters running many containerized applications. However, scheduling policies that consider the performance nuances of containerized High Performance Computing (HPC) workloads have not been well-explored yet. This paper conducts fine-grained scheduling policies for containerized HPC workloads in Kubernetes clusters, focusing especially on partitioning each job into a suitable multi-container deployment according to the application profile. We implement our scheduling schemes on different layers of management (application and infrastructure), so that each component has its own focus and algorithms but still collaborates with others. Our results show that our fine-grained scheduling policies outperform baseline and baseline with CPU/memory affinity enabled policies, reducing the overall response time by 35% and 19%, respectively, and also improving the makespan by 34% and 11%, respectively. They also provide better usability and flexibility to specify HPC workloads than other comparable HPC Cloud frameworks, while providing better scheduling efficiency thanks to their multi-layered approach.

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It's a Scheduling Affair: GROMACS in the Cloud with the KubeFlux Scheduler

Cited by 12 publications

References 7 publications

The Flux Operator

The Flux Operator

A Methodology to Scale Containerized HPC Infrastructures in the Cloud

Fine-Grained Scheduling for Containerized HPC Workloads in Kubernetes Clusters

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