How To Build Your GPU Cluster For AI And Deep Learning

Artificial Intelligence (AI) and Machine Learning (ML) are poised to spark innovation and upend existing research methodologies. Forward-looking enterprises are solving a lot of tumultuous problems by harnessing AI/ ML in an appropriate way.

However, the constant evolution of Deep Learning applications pre-supposes powerful processing units. Industries that fail to adapt to technological upheavals like the proliferation of ML/ DL risk falling behind the competition.

Enterprises today comprehend that AI/ DL applications demand heavy computation capabilities. Afterall, leveraging terabytes of data for model development and training is exceedingly resource-intensive. Thus, the dependence on Graphics Processing Units (GPUs) for accelerating AI/ DL workloads.

The semiconductor chip market (which includes GPUs, besides CPUs, ASICs, etc.) for exclusively running Deep Learning applications was pegged at USD 4.5 billion in 2020 and is expected to balloon to USD 81 billion by 2030. This increase in demand will touch enviable horizons with 35% CAGR during said period!

Future AI/ DL projects will be more extensive in terms of scale and use cases. These will not only demand discrete GPU systems, but even larger and more advanced GPU clusters for enhanced processing.

The article will explore GPU clusters, their types and benefits. We will then touch upon the key considerations involved when building a GPU cluster.

AI/ DL and the Need for GPU-acceleration

Artificial Intelligence is a multi-disciplinary science that involves developing intelligent machines capable of mimicking human intelligence. Such machines can perform a variety of tasks and/ or make fundamental decisions without human intervention.

Deep Learning is a specialized sub-domain of AI that uses complex algorithms inspired by animal brain structure to impart human-like comprehension and decision-making to machines. These algorithms are called Artificial Neural Networks (ANNs). The global Deep Learning market was worth USD 35 billion in 2021.

DL algorithms leverage colossal datasets for two purposes – (1) to understand the underlying data nodes, form correlations, assign classifications and labels, and (2) to employ this understanding for making future predictions or identifying similar/ anomalous data nodes from among billions of other data nodes.

DL models can be developed to recommend purchases, offer market predictions, undertake data analytics, create meaningful media content, assist in traffic management, and so on. With every action and experience, while interacting, DL models become more accurate.

However, processing such complex algorithms and training them through vast amounts of data requires tremendous computation power. Traditional CPU systems cannot keep up. They offer slower processing speeds, and thus the AI/ ML model training lags and stutters, leading to inaccurate results. Thus, the introduction of GPU-accelerated systems over the last few decades.

Many research organizations and AI-development enterprises have now graduated to deploying arrays of GPUs for magnifying the available processing power. These systems can process complex structured and unstructured data for a variety of projects like image/ video analysis, computer vision-based object detection and recognition, neural network training, ML model development, DL-assisted behavior analysis, etc.

What are GPU Clusters?

These are high-end computing systems powered by Graphics Processing Units (GPUs) on every node. These clusters harness GPUs’ power of parallel processing, high-thoroughput computation, and multiple workload management to render rapid calculations.

Multiple GPUs clustered together as a single unit provide accelerated computing capability for comprehensive tasks such as computer vision-based biometrics/ object recognition analysis, predictive analytics, real-time market/ eCommerce insight generation, ML/ DL model training, AI-assisted healthcare, etc. Such GPU clusters are best optimized for AI and DL workloads.

We can classify GPU clusters based on their hardware as –

1. Homogeneous cluster, wherein every single GPU confirms to the same hardware model, class, and manufacturer.
2. Heterogeneous cluster, wherein the GPUs as well as other supporting hardware can be sourced from multiple independent vendors/ manufacturers and may even have different RAM and core specifications.

Read: How To Find The Best GPU For Deep Learning

Advantages of GPU Clusters in AI/ DL Projects

GPU clusters offer numerous benefits for large-scale and complex AI/ DL projects –

High Performance

Connecting several GPUs multiplies their processing capabilities, delivering unparalleled computation for complex, resource-intensive AI/ ML tasks. AI/ ML/ DL projects utilize terabytes of data for developing synthetic intelligence and training it to perform near-human comprehension.

Running such projects on GPU clusters with hundreds of parallel-processing slave nodes boosts computation speed for more demanding but embarrassingly parallel tasks.

Low Latency

Most modern CPUs can undertake parallel processing but on a smaller scale vis-a-vis GPUs. CPU-based systems thus introduce substantial latency in the overall training process, thereby delaying the eventual incorporation of the AI/ ML system in business processes.

Homogeneous GPU clusters can assist in reducing latency, provided that the AI/ ML training is not dependent on highly sequential calculations.

High Availability

The availability of extensive processing resources on-demand is another significant factor when dealing with humongous datasets or undertaking sophisticated AI/ DL projects. Dependence on a single GPU instance can prove to be a potential blindside for a project of such scale in case of any failure.

Deploying GPU clusters or subscribing to Cloud GPU services from reputed providers like Ace Cloud Hosting remedies such scenarios by assuring persistent processing availability.

Efficient Load Balancing

Load balancing is a key factor when handling colossal AI/ DL projects. Such projects often invoke structured/ unstructured data, millions of lines of code and complex algorithms and calculations. These end up drawing unprecedented computational power which might cause the entire system to collapse.

Relying on a single GPU might create an inescapable dawdle or loop in case of excess pressure on that GPU. Deploying a GPU cluster spreads the workloads evenly across multiple slave nodes, thus enabling the system to seamlessly handle gargantuan projects with billions of calculations and large volumes of data.

Read: CUDA Cores vs. Tensor Cores

Building a GPU Cluster for AI/ DL

Step 1: Determining the hardware

1. Motherboard: The foremost component to consider is the GPU motherboard. This includes the semiconductor die size, the number of cores (CUDA/Tensor) embedded, the clock speed and temperature regulation requirements (overclocking), PCI/ PCI-express connections (PCI/ PCIe), network port availability, InfiniBand card interconnection between GPUs, etc.
2. RAM: The GPUs deployed in a cluster should have access to on-chip memory depending on workload requirements. Various categories of workloads such as those dealing with very high-res graphics or million-node ANNs/GNNs necessitate sufficient memory availability.
3. Additional storage: SSD storage in line with the nature of the workload
4. Robust power supply: GPU clusters with data center-grade GPUs are extremely power-hungry. Building such clusters on-premise pre-supposes consistent electricity supply not only for running these heavy systems, but also for cooling/ temperature regulation to avoid thermal throttling.
5. GPU form factor: An important consideration when building GPU clusters, form factor (GPU dimensions, single/dual/multiple-slot, water-cooled or actively/ passively-cooled, etc.) can have an irreversible bearing on the data center design. Form factor also influences the number of GPUs running per rack.

Step 2: Space Allocation and Facilities

Allocating physical space, purchasing racks, and provisioning power supply mechanism and air-conditioning resources for GPU clusters.

Companies can own their own facility or house their GPU cluster within an existing data center. Racks can be purchased off-shelf or custom-built depending on GPU form factor.

Leveraging multiple GPU nodes in a unified manner also necessitates interconnectivity between GPUs, thus additional expenditure on high-end switches and exorbitant network cables.

Lastly, security resources, biometric-based access controls, unauthorized access management protocols, etc. for data center safety in line with the prevailing government/ global legislations for sensitive data retention and security.

Step 3: Final Deployment

Post-finalizing technical configuration, physical space allocation and manpower recruitment, GPU clusters are good to go for developing AI/ DL models. If you have a plan in place for what the AI/ DL model should achieve, GPU clusters are your friend for making said AI/ DL model a reality. So, fire off all engines and get your programmers churning out quality codes and algorithms for streamlining your AI model.

You can also get in touch with the tech wizards at Ace Cloud Hosting to help you subscribe to highly customizable, scalable, on-demand, pay-as-you-go Cloud GPU resources. Ace Cloud Hosting offers top-of-the-line Nvidia A100 GPUs which can outperform CPUs by over 200 times across AI/ ML benchmarks!

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