HPC GPU: Taking HPC Clusters To The Next Level With GPUs in [2023]

We know that some of the fastest computing applications in the world are actually accomplished through computer clusters. These applications can range from real-time weather forecasting to vaccine development during pandemics.

The computer clusters powering these multifarious applications leverage High-Performance Computing resources (HPC) and Cloud GPU servers to minimize latency and deliver lightning-fast processing capabilities.

This article will provide a holistic view of High-Performance Computing, its applications as well as the benefits that GPU acceleration can provide. We will also dig into how HPC GPU clusters can help build better AI applications.

Introduction to HPC

High-Performance Computing is an umbrella term that includes large-scale conglomeration of compute servers (called nodes), storage resources (including on-chip high-bandwidth storage) and networking infrastructure interweaved to function in tandem and deliver highly-optimized performance.

Such conglomerations bring together massive-scale data processing, AI/ML-assisted data comprehension, sophisticated ML model training & deployment, and complex analytics operations. This makes them very well-suited for different industries.

Within the HPC cluster, its underlying software runs hundreds or thousands of nodes for reading and writing data. To improve the HPC cluster’s performance, the enterprise must be specific in determining the hardware resources, node installation and space allocation, power supply and cooling mechanism, and resource monitoring.

Seamless communication through enhanced bandwidth is essential to facilitate data transmission between multiple CPU/GPU nodes, storage systems and web servers.

HPC and GPUs

Graphics Processing Units (GPUs) are incredibly powerful, multi-core electronic devices that can be deployed on-premise or accessed via Cloud. Within HPC clusters, GPUs act as hardware accelerators powering resource-intensive computing operations like ML/ DL model training, Big Data analytics, GNN algorithm development, and so on.

HPC clusters depend on both CPUs and GPUs to simultaneously perform diverse operations. There are two main workflows through which HPCs process information:

• Serial Processing: This is the process followed by constituent CPUs within an HPC cluster. Herein, each CPU core deals with one task at a time, such as running the HPC Operating System and other essential apps including calculation tools and word processors. Since the CPU manages the OS, it indirectly handles complex tasks. But a single CPU cannot perform parallel processing.
• Parallel Processing: A GPU comprises thousands of specialized processing cores. All these cores independently and concurrently run extensive operations on different datasets. This has application in mathematical/ graphics operations like matrix-multiplication which require output across the matrix points to be generated simultaneously. The parallel processing thus accomplished by GPUs enhances processing speed by operating on multiple data planes concurrently.

Enterprises can take their HPC clusters to the next level by embracing parallel processing. Aggregating multiple CPUs, GPUs and memory modules with high interconnectivity bandwidth can deliver heterogeneous computing, thereby optimizing performance and energy efficiency.

The global HPC market was valued at USD 36 billion in 2022 and is expected to reach USD 50 billion by 2027. The HPC industry adopted the use of GPUs in non-graphic applications such as General-Purpose Computing (GPGPUs) over the last decade, and it is predicted that GPUs will continue to dominate the industry in the near-foreseeable future despite the introduction of more advanced hardware technologies such as FPGA.

GPU-powered HPC Clusters for Better AI Applications

HPC clusters for AI projects generally involve multiple nodes (16 to 64 at least) and two or more CPUs per node. Multiple CPUs attached to each node ensure superior processing vis-a-vis traditional single-device systems.

HPC clusters that leverage GPU processing enhance the processing power even further. Hence, most AI-HPC clusters deploy one or more GPU as co-processors functioning parallel to constituent CPUs.

This is called Hybrid computing. It is an excellent method to elevate an HPC cluster’s processing capabilities to the next level. Apart from this, GPUs deliver various other benefits to HPC clusters:

• Purpose-built processing – Purpose-built HPC GPUs can handle Machine Learning and Deep Learning projects (including ML/ DL model training and deployment for prediction and inference applications), Big Data analytics, Artificial Neural Networks (ANNs), high-res image and video processing, etc.
• Data volume expansion – Since GPU uses parallel processing, where every core dedicatedly accomplishes a single task, substantial data volumes can be managed efficiently without any hiccups or latency. AI projects that require colossal datasets for ML/ DL model training can incorporate powerful GPUs with high memory bandwidth in their HPC infrastructure.
• Resource efficiency – Distributing the workload across multiple CPU/GPU nodes wherein each machine undertakes tasks and enhances concurrent resource utilization.
• Time efficiency – Parallel processing and co-processing using GPUs permits enterprises to effortlessly process structured, unstructured and semi-structured data and run dynamic AI experiments in quick timeframes.
• Cost efficiency – HPC clusters employing multiple GPUs are akin to supercomputing and significantly reduce the training time for AI/ ML projects. This also engenders tremendous power consumption savings. Furthermore, HPC clusters employing Cloud GPU infrastructure authorize users to reduce upfront costs by subscribing to pay-per-use models and scaling up/down as/when required by operational needs.

HPC Clusters in the Cloud

An exceptional way to elevate your HPC cluster to the next level is by utilizing Cloud technology and GPU-as-a-Service model (GPUaaS). Enterprises can rent-deploy HPC GPU clusters for batch-processing and scientific workloads on private or public clouds.

Many cloud providers will allow you to build your HPC service and integrate it with their GPU Cloud servers. It will help develop ready-to-use HPC applications in a hybrid environment.

HPC clusters running in the Cloud are capable of handling usage spikes with high reliability and availability. Running HPCs in the Cloud offers several other benefits:

1. Scalability – Large, multi-user projects often experience sudden spikes in the utilization of resources like memory, processing power and storage. HPC clusters running on the cloud are highly scalable and reliant, thus fostering dual benefits – reducing performance bottlenecks and simultaneously allowing users to avoid paying for unutilized resources.
2. Distributed workloads –  Cloud HPC clusters use container-based microservices for orchestrating workload distribution. Via containerization, enterprises can easily manage their DevOps processes, identify coding and programming errors, enhance network security posture, and avoid software vendor lock-in.
3. High availability – Projects involving AI/ ML and real-time market analytics require high computation and cannot afford downtime. Cloud-based HPCs inspire high availability and minimize interruption risks.

Components of Cloud HPC Clusters

Following are the components of cloud HPC clusters:

1. GPU instances and Virtual machines (VMs), including virtualization and containerization programs
2. Communication network, including web servers for end-user interface
3. Support for bare metal infrastructure
4. Batch scheduling features
5. High internet bandwidth for fast interconnection between CPU/GPU nodes
6. Massive memory bandwidth for seamless data handling
7. Storage systems with high-throughput and low latency

Use Cases of GPU HPC Clusters

Some well-known applications of GPU-accelerated HPC clusters are:

AI/ ML/ DL projects

Enterprises require HPC capabilities for large-scale AI/ ML projects. ML algorithms, especially those involving Graph Neural Networks and Reinforcement models, artificial neural systems designed to mimic animal brains, visual identification and language comprehension.

Such algorithms function by segregating complex instructions into simple, similar jobs, which can be accomplished by the numerous nodes within an HPC system.

Blockchain/Cryptocurrency development

Cryptocurrencies work on the distributed ledger principle. All cryptocurrency users provide computing power for solving cryptographic hashes that verify blockchain transactions. For encouraging the sharing of compute power to help solve more of these hashes, several cryptocurrencies offer monetary rewards.

GPU-powered HPC clusters can power blockchain and cryptocurrency development, but it needs to be noted that most cryptocurrencies are disincentivizing crypto-mining given its deleterious effects on the environment as well as overall health of the blockchain ecosystem.

Internet-of-Things (IoT) facilitation

Manufacturing units and large logistics and e-commerce businesses rely on IoT networks for integrated inventory and human resources management. Enterprising healthcare companies have also begun injecting IoT technology across their operations.

These complex entangled networked devices and sensors running AI algorithms require rapid computing and decision-making for optimized performance.

HPCs enable IoT systems to instantaneously respond to any development, execute actionable instructions and make decisions based on said information. GPUs further expand IoT HPC’s accuracy and responsiveness.

Real-time analytics

Today, every sector uses data analytics to extract meaningful insights from granular data. Modelling and analyzing complex datasets demand enormous processing power. This is where GPU-powered HPC clusters can fill the gap.

Whether we work with genome sequencing, market trends, e-commerce transactions, social media advertising, or sales spreadsheets, GPU-facilitated parallel processing of multiple data points helps glean analytics insights quickly.

Read: How to Build GPU Cluster for AI

Best GPUs for HPC Clusters

Deploying a HPC cluster is an exorbitant venture and requires thoughtful planning across the entire gamut of infrastructure and IT manpower setup. GPUs constitute a massive fraction of this expenditure and must be chosen carefully.

We must consider the workload the GPU will handle before purchasing/renting it. Top-notch GPUs that can handle heavy HPC workloads are:

Nvidia A30

Nvidia A30 is a robust GPU is based on Nvidia’s proprietary Ampere architecture and comes equipped with Multi-Instance GPU technology (MIG) for high-scale processing. It boosts image processing, ML training, and complex Deep Neural Network projects that demand resource-intensive processing power.

It can also render UHD-quality images and videos through interactive Ray Tracing technology, making it especially well-suited for unstructured image/video-form data handling.

Basic specs: 

• Architecture: Ampere
• CUDA Cores: 8192
• Tensor Cores: 256
• Memory: 24 GB GDDR6
• Memory Bandwidth: 696 GB/s
• TDP: 320W

Nvidia A100

Another enterprise-grade Ampere-architecture GPU that comes with powerful Tensor cores, the Nvidia A100 is Nvidia’s flagship offering. Enterprises and institutions across the world have been using it in HPC clusters tasked with developing top-of-the-line AI/ML projects, scientific simulations, vaccine breakthroughs and other cutting-edge innovations.

It offers significant speedups across industry-standard ML benchmarks vis-a-vis traditional CPU-based systems.

Basic specs: 

• Architecture: Ampere
• CUDA Cores: 6912
• Tensor Cores: 432
• Memory: 40 GB HBM2 or 80 GB HBM2
• Memory Bandwidth: 1555 GB/s or 1558 GB/s
• TDP: 400W

Nvidia V100

This was the first GPU to breakthrough the 100 TFLOPs ceiling in ML/ DL performance. Incorporating 640 Tensor cores, this Volta-class GPU enables researchers and programmers to accomplish AI training in mere days which would have otherwise taken previous GPU generations weeks.

This dramatic reduction in training time is further supplemented by up to 300 GB/s interconnectivity with other V100 GPUs in the HPC via NVLink switch.

Basic specs: 

• Architecture: Volta
• CUDA Cores: 5120
• Tensor Cores: 640
• Memory: 16 GB or 32 GB HBM2
• Memory Bandwidth: 900 GB/s or 1,124 GB/s
• TDP: 250W or 300W

Note that these specifications are just the basic ones and there are many other factors that can influence the performance of these GPUs in different applications.

Conclusion

We hope this article gives a crisp idea of the numerous advantages that enterprises can derive by including GPUs in HPC clusters. Cloud technology and advanced GPUs have changed the way HPC and AI/ ML have been shaping the world. We also took some space to talk about the use cases of GPU-powered HPC clusters.

If you are looking to develop GPU-powered HPC clusters that are dynamically scalable, versatile and supremely well-suited for handling massive AI/ML projects, look no further. Connect with our Consultants at Ace Cloud today!