What Is Cluster Management Software?
Cluster management software is a tool that enables administrators to manage and monitor clusters of computer systems more effectively. Cluster management software can help to automate many of the tasks involved in managing clusters, such as software deployment, workload scheduling, and system monitoring.
Cluster management software typically provides a centralized interface for managing and monitoring cluster resources, allowing administrators to view the status of individual nodes, applications, and workloads, as well as to monitor system performance and resource utilization.
In this article:
Top 7 Cluster Management Tools
Run:ai Atlas is a platform that collects all computing resources into a centralized pool from cloud and on-premises systems. Using a Kubernetes-based smart workload scheduler, the platform ensures the dynamic allocation of resources. Integration with the NVIDIA AI stack allows for advanced sharing and fractioning of GPUs across distributed workloads to ensure optimal utilization.
The centralized control plane gives IT organizations complete control and visibility over all their resources, users, and workloads. Atlas provides AI practitioners with on-demand, self-service access to computing power to meet their evolving needs directly from their preferred ML tool.
NVIDIA Bright Cluster Manager
NVIDIA Bright Cluster Manager facilitates the swift deployment and comprehensive management of heterogeneous AI server and HPC clusters across various environments, including the edge, data center, and multi/hybrid-cloud. It streamlines the administration and provisioning processes of clusters that range in size from a few nodes to thousands of nodes, while accommodating both CPU-powered and accelerated NVIDIA systems with GPU. Additionally, it enables Kubernetes-based orchestration.
Bright Cluster Manager empowers users to install entire Linux clusters directly onto bare-metal infrastructure and ensures their reliable management, from the edge to the core to the cloud. As a cluster management solution, it caters to the modern high-performance computing era by offering provisioning, administration, and monitoring functionalities.
When using NVIDIA DGX workstations, the NVIDIA Bright Cluster Manager comes with Run.ai’s GPU virtualization technology built in. Learn more about the Run.ai & NVIDIA DGX Bundle.
Aspen Systems Cluster Management
Aspen Systems is experienced in the field of HPC software stacks. Aspen Cluster Management Environment (ACME) is a proprietary command-line-based cluster management tool. Aspen’s offerings also include commercial and open source solutions like NVIDIA’s Bright Cluster Manager, Warewulf, OpenHPC, and xCAT2. These solutions are equipped with configuration managing software, containers, monitoring capabilities, and package managers for software that are common in supercomputers.
Aspen Systems' HPC clusters come standard with the Aspen Cluster Management software and its accompanying service package, which incurs no additional cost. Moreover, Aspen’s Cluster HPC Management software can work in most Linux-based systems and is supported for the duration of the cluster's life.
Advanced Clustering Technologies
ClusterVisor, designed by Advanced Clustering Technologies (ACT), simplifies the deployment and management of your HPC cluster with a single GUI that handles hardware, operating system, software, and networking. This full-featured tool is highly customizable, allowing you to efficiently organize and manage your cluster over time.
ACT's eQUEUE is a software product that facilitates web-based forms for submitting jobs, enabling system admins to optimize the utilization of clusters. By removing the complexity involved in submitting jobs to the cluster, eQUEUE attracts more users who may have otherwise stayed away due to the scripting or Linux requirements. End users can now easily input their data into clearly defined fields, and the jobs are then queued to run on the cluster.
Azure HPC is an all-inclusive package of computing, storage, and networking resources, coupled with workload-orchestrating services that cater to HPC applications. Azure offers specially-designed HPC solutions, infrastructure, and application services optimized for high performance, while being cost-competitive compared to on-prem options.
Azure offers additional benefits in high-performance computing. Its repertoire includes next-gen machine-learning (ML) tools that drive more intelligent decision-making and enable smarter simulations.
Slurm is a job scheduling and cluster management system that is open-source, fault-tolerant, and highly scalable for Linux clusters, both large and small. It is self-contained and does not require any kernel modification to operate.
Slurm includes three main functions for managing cluster workloads. It allows users to allocate exclusive and non-exclusive access to compute nodes for a specific period so that they can carry out their tasks. It also offers a framework for initiating, executing, and observing the assigned work, which is typically a set of parallel jobs, on the allocated nodes. Finally, Slurm manages a pending work queue and arbitrates any resource contention to ensure that the work runs smoothly.
IBM Spectrum LSF
IBM Spectrum LSF is a software product created to enable the distribution of workloads across diverse IT resources, resulting in the development of a shared, scalable, and fault-tolerant infrastructure. This infrastructure is capable of delivering faster and more reliable performance while reducing costs.
LSF operates by providing a framework for resource management, which involves the identification of the ideal resources for running a particular job, as well as monitoring its progress. With LSF, jobs are always executed in line with host load and site policies.
Related content: Read our guide to cluster manager technology (coming soon)
How to Choose the Right Cluster Management Solution
Choosing the right cluster management tool depends on several factors, including the size and complexity of the cluster, the type of applications and workloads being run on the cluster, and the level of automation and monitoring required.
Here are some factors to consider when choosing cluster management tools:
- Scalability: Cluster management tools should be able to scale to support the size and complexity of the cluster. Consider the number of nodes in the cluster, the amount of data being processed, and the number of applications being run. It should be able to add or remove nodes to the cluster easily and without disrupting operations, to handle both current and future workloads.
- Cluster size: The size of your cluster will also affect the choice of cluster management solution. Some solutions may be better suited for small clusters, while others may be designed for larger clusters.
- AI model size: If your workload involves large AI models, you should choose a cluster management solution that can handle these models efficiently. Some solutions may have limitations on the size of the AI models that they can handle.
- Cost: Cluster management tools can be open-source or commercial, and the cost can vary depending on the features and level of support offered. Some solutions may be expensive to acquire or maintain, which may not be suitable for smaller budgets. Consider the total cost of ownership, including licensing fees, training, and support costs.
- GPU support: If your workload requires GPU resources, you should choose a cluster management solution that supports GPUs. Some solutions are specifically designed for GPU workloads, while others may have limited GPU support.
- Monitoring: Cluster management tools should provide comprehensive monitoring and reporting capabilities to help administrators identify and address issues in real-time.
- Support: It is crucial to choose a cluster management solution that provides good support. This includes documentation, user communities, and technical support to ensure that any issues are addressed promptly.
Managing your Cluster with Run:ai
Run:ai’s platform allows you to allocate, schedule, divide and pool your GPU Cluster. Run:ai is the software layer that sits between your GPU clusters and your AI workloads to ensure complete transparency, full control and faster deployment.