GPUs' parallel processing capabilities have sparked a revolution in high-performance computing, delivering massive speed boosts for tasks ranging from artificial intelligence to scientific modelling and graphics rendering. Cloud computing platforms have swiftly embraced these advancements by democratising access to GPU-equipped resources for everyone, no matter the industry. From healthcare and finance to entertainment and manufacturing, you name it.
What’s more fascinating is the growth, according to the latest reports the global GPU as a Service market will grow from $3.16 billion in 2023 to $25.53 billion by 2030. This surge reflects the growing demand for access to cutting-edge computational power. By renting state-of-the-art data centre GPUs, you can now accelerate heavy workloads without hefty investments in GPU infrastructure.
As everyone knows CPUs have traditionally been the driving force for computing workloads. However, for specific workloads like graphics, artificial intelligence and scientific simulations - specialised processors like GPUs can provide orders of magnitude speedups. Let's examine what makes GPUs architecturally distinct and how their integration into cloud computing is impacting capabilities.
The key difference lies in how GPUs and CPUs are designed at a hardware level to handle processing tasks. CPUs consist of a few cores optimised for sequential serial processing of tasks. On the other hand, GPUs contain thousands of smaller yet efficient cores to enable massively parallel processing by simultaneously executing many lightweight threads.
This fundamental difference stems from their origins - CPUs were conceived as general computing devices meant for a wide diversity of tasks. GPUs were graphics accelerators purpose-built for mathematically intensive operations involved in rendering images or 3D graphics. However, it soon became apparent that the high parallelism led to amazing throughput for linear algebra and floating point math was also highly beneficial for computational domains like simulation and machine learning.
GPUs have a massively parallel architecture specifically designed for handling compute-intensive workloads with high levels of concurrent mathematical operations. They possess CUDA cores designed to execute thousands of lightweight execution threads simultaneously. These tiny cores are organised into larger streaming multiprocessors (SMs) - each SM consists of 32, 64, or more stream processors sharing instruction and memory caches.
The SMs feature extremely high memory bandwidth to load and store data for the stream processors as quickly as possible to keep them saturated with threads for execution. Each stream processor contains just enough logic for basic computations like floating point math, foregoing complex control logic. The cumulative power of thousands of these simple cores working in lockstep gives GPUs their unprecedented throughput rated in tera and petaflops per second. So while a CPU core outperforms an individual GPU core, the highly parallel architecture of GPUs allows them to massively outscale serial processors for suitable workloads. For example, NVIDIA's Tensor Cores accelerate key mathematical operations for domains like neural networks and graphic ray tracing.
GPUs are increasingly being integrated into cloud environments to enhance performance, reduce operational costs and scale resources dynamically based on demand. Several factors underpin the integration of cloud computing with GPU:
Accelerate Compute-Heavy Workloads: Deep learning, graphics rendering, genomics analysis, and computational fluid dynamics are among high-impact domains hugely accelerated by supplementing GPU cloud services.
Improve Economies of Scale: Consolidating access to expensive hardware like GPUs into cloud data centres allows efficient sharing of them between hundreds of distributed customers via virtualisation and multi-tenancy.
Enable New Capabilities: Cloud GPUs help projects from game streaming services to computational research by providing versatile, scalable HPC on tap without major upfront Capex.
Drive Innovation: By lowering barriers to leveraging AI, data analytics and immersive graphics apps, GPUs multiply innovation velocity across organisations and industries leaning into cloud computing.
When deployed alongside virtual machines in the cloud, GPU in cloud computing offer dramatic speedups. The scale of performance enhancement depends on specific workloads like:
Training deep neural networks leverages GPUs' massively parallel architecture to dramatically shorten timeframes that would take weeks or months on CPUs to mere hours. Complex natural language processing (NLP), computer vision, and recommendation system models can be rapidly iterated on. As cloud-based machine learning platforms provide on-demand access to GPU clusters for everything from model experimentation to deploying GPU-optimised production environments for low-latency inference.
For example, Microsoft and NVIDIA engineers used 64 NDv2 instances on a pre-release version of the cluster to train BERT in roughly three hours. This was achieved in part by taking advantage of multi-GPU optimisations provided by an NVIDIA CUDA-X library and high-speed Mellanox interconnects.
From climate science to molecular biology, GPU-powered simulations are driving discovery across scientific domains. Numerical weather and climate modelling with higher spatial granularity becomes tractable using GPU infrastructure. Computational fluid dynamics simulations that model airflow and hydrodynamics for applications from aircraft design to astrophysics are made interactive by the number-crunching parallel performance of GPUs. Even intricate molecular dynamics simulations that determine protein folding structures and drug interactions by modelling atomic level interactions for hundreds of thousands of particles are accelerated to run in reasonable times using cloud-hosted GPU based computing offerings. By providing flexible access to cutting-edge accelerated simulation capabilities, GPUs in the cloud are profoundly advancing scientific research.
GPUs were originally optimised for rendering pipelines and they continue excelling at workloads from 3D visualisation to video processing. Game development studios leverage GPU-based cloud rendering to create immersive high-fidelity environments within budget. GPU-accelerated video transcoding services convert raw media files into streaming formats for delivery to millions of consumers in parallel. Cloud workstations with supplemented GPU horsepower speed up video production and editing, 3D modelling, and architectural visualisation workloads. The burgeoning metaverse ecosystem is driven by advancements in real-time 3D graphics fuelled crucially by GPU innovation, including via cloud delivery for end users lacking high-end hardware.
While the sheer performance gains from GPU acceleration are impressive - optimisations in cloud delivery combined with economies of scale make access to these capabilities cost-efficient like never before. Cloud changed the economics of high-performance GPU based computing - slashing timelines from months to minutes while saving tremendously in expenditure compared to owning depreciating hardware.
Cloud computing GPU provides flexibility to request exactly the quantity of GPU power needed for workloads. For example, at Hyperstack, we deliver flexible access to GPU-accelerated computing with a pricing model tailored for cost optimisation. You can get instant access to NVIDIA GPUs on-demand and only pay per hour based on actual usage, with no long-term commitments required. This allows you to easily scale GPU resources up and down to match your real-time workload needs. We offer a range of cutting-edge GPUs like NVIDIA SXM H100 in Supercloud environments.
Our transparent pay-as-you-go model for cloud GPU pricing starts at just $0.43 per hour, allowing you to leverage these high-powered capabilities without breaking the bank. We have built a whole ecosystem around enterprise-grade GPUs. Everything, down to our platform, networking, and hardware, is optimised to provide the highest efficiency and speed at the most competitive cost for GPU cloud workloads. This results in both unmatched performance and the most competitive TCO compared to conventional on-premises infrastructure for graphics, AI and HPC applications.
The future of cloud computing and AI acceleration is driven by groundbreaking advancements in energy-efficient and powerful hardware architectures. NVIDIA's Hopper GPU architecture has already demonstrated remarkable leaps in large AI model training speeds while delivering twice the performance per watt, paving the way for scalable and sustainable cloud infrastructure.
As emerging use cases spanning AI, metaverse, and self-driving vehicles continue to gain traction, the demand for cloud computing GPU will surge across diverse sectors, such as media and entertainment, academia, finance, research, and enterprise.
Now, startups to big corporations can smash through previously impractical complexity barriers and supercharge innovation by leveraging cloud GPUs. Do you have a mountain of data for training computer vision models? Get a GPU cloud to the rescue. Need to iterate endless design prototypes? Accelerated cloud workstations have your back. It's truly exciting to see what becomes possible as GPU cloud adoption spreads further - costs keep falling, and hardware keeps getting better.
And here's the cherry on top - this is still just the beginning of the potential of accelerated computing power in the cloud. Hyperstack is heavily invested in making state-of-the-art GPUs even easier to deploy across more real-world applications. By democratising access to what was once only military-grade processing capabilities, the GPU cloud is undoubtedly transforming industries right in front of our eyes. The future looks very bright and fast!
Reduce your compute overheads by 75%! Sign up at Hyperstack today and experience the power of AI, graphics, and HPC without breaking the bank.
GPUs enable massively parallel processing which dramatically speeds up workloads like scientific computing, data analytics, AI workloads and graphics rendering. We offer GPU-enabled virtual machines that allow these accelerated workloads to run efficiently in the cloud.
Cloud solutions like GPU-based virtual machines, containers, managed services, and application platforms help easily deploy scalable cloud GPU computing without needing to invest in on-premises GPU servers.
Computationally demanding workloads like simulations, medical imaging, and oil and gas modelling require immense parallel processing power. Cloud-based GPU infrastructure provides an affordable way for GPU acceleration at scale for these workloads.
As algorithms grow more complex, we can expect higher interconnect bandwidth between GPUs, optimisations like multi-instance GPUs and virtual memory for more efficient distributed training and computing across future-generation GPU clouds.