What Is Cluster Management?
Cluster management is the process of managing a group of interconnected computers, known as a cluster, in order to achieve a desired set of goals or objectives. Clusters are commonly used in large-scale computing applications, such as high-performance computing (HPC), big data processing, and cloud computing.
The primary goal of cluster management is to ensure that the cluster is running optimally and efficiently, and that resources are being used effectively to meet the needs of the applications running on the cluster. This involves a range of tasks, such as monitoring the performance of individual nodes and the overall cluster, managing the allocation of resources, and coordinating the scheduling and execution of jobs.
In this article, we’ll discuss cluster management in three types of computing clusters:
What Types of Resources are Managed in Compute Clusters?
Compute clusters are designed to efficiently allocate and manage various types of resources to perform complex calculations and tasks. Here are the most common resources managed in compute clusters:
- CPUs: Central Processing Units (CPUs) are the primary processing units in a compute cluster. They handle general-purpose computations and manage the flow of data between different components. CPUs are responsible for executing instructions in a computer program and can handle multiple tasks simultaneously through multithreading.
- GPUs: Graphics Processing Units (GPUs) are specialized processors designed to handle parallel processing tasks, particularly those related to graphics rendering. However, their ability to perform parallel computations also makes them suitable for other tasks, such as machine learning, deep learning, and scientific simulations. In compute clusters, GPUs can accelerate computations by offloading specific tasks from the CPU.
- TPUs: Tensor Processing Units (TPUs) are specialized ASICs (Application-Specific Integrated Circuits) developed by Google for accelerating machine learning workloads. They are particularly suited for running TensorFlow-based deep learning models. TPUs provide high-performance matrix processing capabilities, making them well-suited for handling large-scale computations in compute clusters.
- Memory: Memory in compute clusters is a crucial resource, as it temporarily stores data and instructions for the CPU, GPU, and TPU during processing. There are two primary types of memory: Random Access Memory (RAM) and cache memory. RAM is used for temporary storage of data and instructions, while cache memory is a smaller, faster memory that stores frequently accessed data. Compute clusters often need to manage large amounts of memory to handle complex workloads and data-intensive applications.
- Storage: Storage resources in a compute cluster include both local storage (hard drives, SSDs, etc.) and network storage (NAS, SAN, etc.). Storage is used for long-term retention of data, application files, and results of computations. Cluster management involves monitoring storage usage and ensuring data is distributed efficiently across the available storage devices. In some cases, high-performance storage solutions like parallel file systems are employed to provide low-latency access to large datasets.
Kubernetes Cluster Management
Kubernetes cluster management refers to the process of administering, maintaining, and optimizing a Kubernetes cluster, which is a group of interconnected nodes (computers or servers) running containerized applications. Kubernetes is an open-source container orchestration platform designed to automate the deployment, scaling, and management of containerized applications.
Kubernetes cluster management involves several aspects, including:
- Cluster setup and configuration: Installing and configuring the necessary components to create a Kubernetes cluster, such as setting up the control plane, worker nodes, and networking.
- Monitoring: Actively keeping track of the health and performance of the cluster, including resource utilization, application performance, and the overall stability of the system.
- Scaling: Adjusting the cluster's capacity, either by adding or removing nodes or by adjusting the number of replicas for a given application, to meet the changing needs of the deployed applications.
- Upgrades and updates: Ensuring that the cluster components, including the Kubernetes control plane, worker nodes, and the containerized applications, are up-to-date with the latest patches, security updates, and new features.
- Security and compliance: Implementing security best practices, such as role-based access control (RBAC), network policies, and secrets management, to protect the cluster and the applications running on it from unauthorized access and potential threats.
- Troubleshooting and maintenance: Diagnosing and resolving issues that may arise within the cluster, such as node failures, application crashes, or networking problems, to maintain the cluster's stability and availability.
Kubernetes cluster management can be performed using various tools and utilities, including the Kubernetes command-line interface (kubectl), graphical user interfaces (GUIs) like Kubernetes Dashboard, or third-party tools and platforms that provide additional features and capabilities.
Learn more in our guide to Kubernetes architecture
HPC Cluster Management
An HPC cluster, or high-performance computing cluster, is a combination of specialized hardware, including a group of large and powerful computers, and a distributed processing software framework configured to handle massive amounts of data at high speeds with parallel performance and high availability.
The architecture of a High-Performance Computing (HPC) cluster typically consists of the following key components:
- Core software: An HPC cluster relies on a foundational infrastructure to run applications and software that manages this infrastructure. The software operating the HPC cluster must be capable of handling enormous I/O traffic and multiple concurrent tasks, such as reading and writing data to the storage system from numerous CPUs.
- Network switch: An HPC cluster demands high bandwidth and low latency. As a result, it is typical to utilize a network switch like Infiniband or a high-performance Ethernet switch.
- Head/Login node: This node verifies user credentials and allows them to configure software on compute nodes.
- Compute nodes: These nodes are responsible for carrying out numerical computations and generally possess the highest possible clock speeds for the number of cores they contain. While these nodes may have limited persistent storage, they often have a large amount of dynamic random-access memory (DRAM).
- Accelerator nodes: These nodes may house one or more accelerators, although not all applications can leverage them. In some instances, every node in an HPC cluster has an accelerator, particularly in smaller clusters designed for a specific purpose.
- Storage System: HPC clusters can utilize storage nodes or a storage solution such as a parallel file system (PFS), which facilitates simultaneous communication between multiple nodes and storage drives. Robust storage is crucial for ensuring that the compute nodes can operate efficiently and with minimal latency.
HPC cluster management refers to the process of deploying, monitoring, maintaining, and managing a group of interconnected computers or servers (known as a cluster) designed to perform complex computational tasks at high speed. HPC clusters are widely used in scientific research, engineering simulations, financial modeling, data analytics, and other fields that require significant computing power.
HPC cluster management aims to maximize the performance, efficiency, and reliability of the cluster while minimizing downtime and resource wastage.
HPC cluster management can be a complex and resource-intensive task, requiring specialized skills and expertise. Organizations often use dedicated cluster management tools or software platforms to simplify the process and ensure efficient HPC cluster operations. These tools provide a comprehensive solution for deploying, monitoring, and managing HPC clusters, automating many of the tasks involved in cluster management and helping organizations get the most out of their HPC resources.
Learn more in our guide to HPC clusters
NVIDIA Cluster Management
NVIDIA DGX is a line of servers and workstations built by NVIDIA, which can run large machine learning workloads on interlinked clusters with up to thousands of graphical processing units (GPUs). DGX provides a large amount of computing power, between 1-5 PetaFLOPS in one DGX system.
DGX cluster architecture
A DGX cluster is based on a head node, a computer that acts as a central point of control for a cluster. It is responsible for managing the cluster resources such as scheduling jobs, managing user accounts, and providing access to shared storage. It can also serve as a user sign-in node for small clusters, enabling the creation and submission of jobs.
Head nodes are useful for various sized clusters, including DGX-1 and DGX-2. They allow the DGX system to focus on computing instead of an interactive login or a user’s post-processing activities. The more nodes are in the cluster, the more important it is recommended to have a head node.
In large clusters, there is typically a dedicated storage system, such as a file system or NFS. The head node can be used as an NFS server for smaller clusters, adding memory and storage. In this setup, the head node acts as a file server that stores data and makes it available to other nodes in the cluster. The InfiniBand network can be used to connect the nodes in the cluster and provide high-speed data transfer
Managing DGX GPU clusters
There are two primary ways to manage clustered DGX systems:
- DeepOps: An open-source, modular tool developed by NVIDIA and its supporting open source community. DeepOps incorporates the best deployment practices for GPU-accelerated Kubernetes and Slurm. Slurm is an open-source cluster resource management and job scheduling system that provides three key functions: allocating access to resources (computers), scheduling jobs (tasks), and managing resources.
- Bright Computing Cluster Manager: NVIDIA offers a proprietary cluster management software called Bright Cluster Manager that provides fast deployment and end-to-end management for DGX and other types of AI server clusters. It supports deployment at the edge, in the data center, and in multi/hybrid-cloud environments. Bright Computing Cluster Manager automates provisioning and administration for clusters ranging in size from a single node to hundreds of thousands, with a unified management interface for both hardware and software.
NVIDIA’s Bright Computing Cluster Manager comes with Run:ai’s GPU virtualization technology out of the box.
Learn more about the Run:ai & NVIDIA DGX Bundle.
7 Cluster Management Best Practices
While managing different types of compute clusters can be a very different task, there are some common best practices. These best practices should come in useful in most cluster management projects:
- Use automation tools: Cluster management can be complex and time-consuming, so it's important to use automation tools to simplify and streamline the process. Automation tools can help with tasks such as provisioning and deploying nodes, configuring services and applications, and managing resources.
Learn more in our detailed guide to cluster management software.
- Monitor and analyze performance: It's important to monitor the performance of the cluster and the applications running on it, and to analyze the data to identify performance bottlenecks and areas for improvement. This can help to optimize resource usage and improve overall cluster performance.
- Plan for scalability: Clusters should be designed with scalability in mind, so that they can grow and expand as needed to meet changing business requirements. This may involve adding new nodes or clusters, or using cloud-based resources to augment on-premises resources.
- Use standard hardware and software: Clusters should use standard hardware and software components to simplify maintenance and support. This can help to reduce costs and minimize the risk of compatibility issues.
- Implement security measures: Clusters should be secured with appropriate security measures, such as firewalls, intrusion detection systems, and access controls. It's also important to ensure that all software and firmware is kept up-to-date with security patches and updates.
- Test and validate changes: Before making any changes to the cluster, it's important to test and validate the changes in a non-production environment. This can help to identify any issues or compatibility problems before deploying the changes to the production environment.
- Document processes and procedures: Clusters should be documented with clear and comprehensive processes and procedures, including recovery and backup plans. This can help to ensure that the cluster can be managed and maintained effectively, even in the event of a failure or outage.
Related content: Read our guide about cluster manager components (coming soon)
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.