What Is a GPU Cluster?
When multiple computers have a GPU connected to every node, this is called a GPU cluster, where a node is defined as a point of connection within a data communication network. A GPU cluster can be used to take the parallelization power of GPUs to the next level. By combining multiple GPUs together, their impressive parallel processing capabilities become even greater, efficiently handling tasks that are too large to handle for a CPU or a single GPU. GPU clusters are used by research institutions and other organisations that require massive amounts of computing for research, data analysis, and other AI-related tasks.
GPU clusters can be used to:
- Divide workloads between multiple GPUs, enabling them to handle larger volumes of data.
- Ensure GPU availability, by rerouting requests to different nodes should one GPU fail
- Increase performance, by providing high amounts of compute power for more demanding tasks.
GPU Cluster Management
As with standalone GPUs, you can choose to build your own GPU cluster or outsource to a cloud provider. In this post, we’ll address building your own GPU cluster. For immediate access to cloud GPU clusters, you can always create a UbiOps account or reach out to our team.
Build your own cluster
If you choose to build your own GPU cluster there are a number of things that you need to take into account before you get started. NVIDIA describes seven steps for how to build a GPU cluster, but for this article we’ll narrow them down to three steps.
- Choose your hardware:
- Determine what specifications you want for each computer in your cluster. This includes the CPU, motherboard, required ports, RAM, the power supply unit and secondary storage which fits your needs. Some of these specifications are mandatory, like a motherboard with 2x PCIe x16 Gen2/3 connections if you want to make use of a Tesla GPU, while others have more flexibility. NVIDIA recommends 16-24 GB DDR3 RAM, but more is definitely better.
- Decide what kind of GPUs you want to use in your cluster. For example, NVIDIA delivers GPUs with or without active cooling. Those with active cooling (C series) come installed with a fan cooler to keep the GPU cool and can be slotted into your home desktop PC. M series GPUs have no active cooling and are placed in standard servers.
According to NVIDIA there are three ways you can add GPUs to your cluster:
- Buy C series GPUs (with active cooling) and install them in existing workstations or servers with enough space
- Buy workstations from a vendor that already have C-series GPUs installed
- Buy whole servers with M series GPUs installed
- Allocate space, power and cooling:
Now is the time to start thinking about the physical infrastructure. This includes the space, cooling needs, networking, power, and storage requirements. Also take into account that you might need to increase your computing power in the future, so your GPU cluster should have room to grow.
- Deploy your GPU cluster:
After you have selected all your hardware it is time to fit it all together. Every cluster has two types of nodes:
- The head node, which serves as the external interface to the cluster. This is the node that is connected to the external network. It processes incoming requests and assigns work to the compute nodes. Larger clusters and production clusters usually have a dedicated node that handles incoming traffic, in which case the head node just manages workloads for the compute nodes.
- Compute nodes, which perform the actual computations. Using compute nodes as head nodes is possible but not advised as this can lead to drops in a cluster’s performance and create security issues.
Both node types can be installed to your cluster using the open source Rocks Linux distribution. Rocks offers an extensive guide on how to install a head node and compute nodes. Note that you will need to install the head node first, followed by the compute nodes.
Take your GPU cluster further
GPU clusters provide immense computing power. For organizations needing heavy computing capabilities, building a custom GPU cluster allows workloads to be efficiently divided across nodes for maximum speed and availability. With careful planning and execution, GPU clusters enable immense computing power to drive innovation across many fields.
UbiOps can be linked to your GPU clusters to quickly enhance your AI infrastructure. UbiOps serves as a single control plane with powerful MLOps functionality as well as adaptive hybrid and multi cloud orchestration capabilities. Boost your local computing resources with cloud GPU clusters when you need them to maintain availability during high traffic spikes. With UbiOps, clients can drive innovation on their own GPU clusters while optimizing resources.
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