State of Cloud HPC (High Performance Computing) in 2024

High-performance computing (HPC), also known as supercomputing, can operate in cloud-based environments or on-site. Given that HPC tasks are often data-intensive, which can drive up expenses and demand substantial computational resources, cloud computing offers a solution by reducing initial setup costs and supplying the necessary computational power through the service provider. 

This article delves into the benefits of utilizing cloud-based HPC, addresses frequently asked questions about HPC, and explores various applications of cloud HPC across different industries.

What is HPC?

High Performance Computing (HPC) involves the use of supercomputers and parallel processing techniques to solve complex computational problems. HPC systems are designed to enhance performance by aggregating computing power, allowing for higher processing speeds and greater data throughput than standard computers. These systems are crucial for tasks that require processing large datasets, performing complex simulations, or analyzing intricate algorithms rapidly.

What are the challenges of on-premise HPC deployments?

• Capital expense: Setting up an on-premise HPC infrastructure requires significant upfront investment in specialized hardware, facilities, and cooling systems.
• Maintenance and upgrades: On-premise HPC clusters necessitate ongoing maintenance and periodic upgrades, adding to the total cost of ownership.
• Scalability: Scaling on-premise HPC resources to meet fluctuating demand can be challenging and expensive.
• Expertise: Maintaining an on-premise HPC setup requires access to skilled personnel with expertise in high-performance computing, which can be a scarce resource.

Why HPC on cloud?

Cloud High-Performance Computing (HPC) leverages cloud computing models to provide users with access to HPC resources without the need for significant upfront investment in physical infrastructure. Cloud providers help organizations tap into powerful computing resources on demand, paying only for what they use.

If you are looking for a cloud GPU provider, you can take a look at our benchmark study.

HPC cloud environments are offered through models like Infrastructure as a Service (IaaS), Platform as a Service (PaaS), and Software as a Service (SaaS), with varying levels of control, flexibility, and ease of use.

Here’s how HPC can be deployed across these models:

1- Infrastructure as a Service (IaaS)

Infrastructure as a Service (IaaS) provides the foundational layer of cloud HPC, offering virtualized computing resources over the Internet. In an HPC context, IaaS gives users access to high-performance servers, storage, and networking hardware, allowing them to configure and manage their HPC environment according to specific project needs. This model is ideal for users who require flexibility in setting up their computing environment but want to avoid the capital expense and complexity of managing physical hardware. Users can scale resources up or down based on demand, ensuring optimal performance and cost-efficiency.

Key Features of HPC on IaaS

• Access to customizable virtual machines (VMs) with high CPU/GPU capabilities.
• Scalable storage solutions for large datasets.
• Advanced networking for high-speed data transfer and connectivity.
• Users maintain control over the operating system, software, and HPC applications.

2- Platform as a Service (PaaS)

Platform as a Service (PaaS) offers a more managed environment, where users can develop, run, and manage applications without dealing with the underlying infrastructure. For HPC, PaaS can provide a framework and tools to simplify the deployment of HPC applications, including pre-configured development environments, libraries, and software stacks optimized for high-performance tasks. This model is particularly beneficial for developers and researchers focusing on the computational aspects of their projects without needing deep expertise in infrastructure management.

Key Features of HPC on PaaS

• Pre-built HPC environments and tools for specific computational tasks.
• Managed services for database management, messaging queues, and more, which can be integrated into HPC workflows.
• Automatic scaling and load balancing with cloud resources to efficiently handle computational loads.

3- Software as a Service (SaaS)

SaaS delivers software applications over the internet, on a subscription basis. In the context of HPC, SaaS can provide users with access to specialized HPC applications and tools without requiring installation or maintenance of software on local machines. This model is highly accessible, enabling users to perform complex simulations, data analyses, and more through a web interface or API, with the cloud provider managing the HPC infrastructure and software.

Key Features of HPC on SaaS

• Access to specialized HPC applications for industries like bioinformatics, engineering, and financial modeling.
• No need for users to install, update, or maintain software or hardware.
• Collaborative and accessible from anywhere, facilitating distributed research and development teams.

Cloud HPC vs HPC on-premise

• Cloud HPC refers to the deployment of high-performance computing resources over a cloud computing platform. This model offers flexibility, scalability, and cost-efficiency, as users can access a wide range of HPC resources on-demand without the need for significant upfront investment in physical infrastructure.
  
• On-Premise HPC, on the other hand, involves the deployment of HPC infrastructure within an organization’s physical premises. This traditional model offers direct control over the hardware and software, potentially resulting in optimized performance for specific tasks. However, it requires substantial capital investment and ongoing maintenance costs.
  

> FAQs on HPC

1- Is HPC a CPU or GPU?

High-Performance Computing (HPC) systems leverage both Central Processing Units (CPUs) and Graphics Processing Units (GPUs) tailored to the specific needs of computational tasks. CPUs excel at executing sequential tasks, making them ideal for processes that unfold step-by-step. On the other hand, GPUs are adept at handling parallel processing, capable of managing thousands of threads at once, which suits tasks that can be divided and computed simultaneously. By integrating both CPUs and GPUs, an HPC setup can achieve optimal performance across a diverse array of applications.

2- Is HPC the same as quantum computing?

Quantum computing, despite its transformative potential and significant investment, is progressing more slowly than the IT industry’s expectations, facing challenges like data input limitations and the scarcity of high-speed algorithms. These issues suggest quantum computing won’t soon replace traditional high-performance computing (HPC) but could complement it in specific areas.

3- How to decide between cloud or on-premise HPC?

Deciding between cloud or on-premise HPC depends on several factors:

• Cost: Cloud HPC can be more cost-effective for organizations that require HPC capabilities intermittently or are unable to afford the upfront investment in on-premise infrastructure.
• Scalability: Cloud HPC offers scalability on demand without the need for physical infrastructure expansion.
• Security and Compliance: For industries with strict data security and compliance regulations, on-premise HPC might be preferred to ensure control over the physical and network security aspects.
• Performance Requirements: Some highly specialized HPC workloads might require custom hardware configurations that are more feasible with on-premise solutions.

4- Will cloud computing subsume HPC?

A recent report indicates a dramatic growth in HPC within the cloud but also notes that this growth is expected to stabilize and align with overall HPC industry growth.¹ Enterprise AI and deep learning demands are reshaping HPC, with IT hyperscalers potentially outdoing on-premise supercomputers if they can scale their hardware production economically. This shift could centralize HPC in the cloud, offering cost benefits but risking the neglect of specialized HPC workloads like dense 64-bit linear algebra, highlighting a trade-off between cost efficiency and specialized computational capability.²

Use cases of HPC in different industries

1.1- Manufacturing

In manufacturing, HPC is used for simulations and modeling processes such as computational fluid dynamics (CFD), finite element analysis (FEA), and digital prototyping. This allows for the design and testing of products virtually, reducing the need for physical prototypes and speeding up the development process.

1.2- Financial services

HPC enables the analysis of large datasets for risk management, fraud detection, real-time trading, and financial modeling. It helps financial institutions make more informed decisions quickly and identify fraudulent activities more efficiently.

1.3- Energy

In the energy sector, HPC is used for seismic processing, reservoir simulation, and energy exploration to locate and extract oil, gas, and renewable resources more efficiently. It allows for more accurate modeling of complex geological formations and the assessment of potential environmental impacts.

Further Reading

• In-Depth Guide to Cloud Large Language Models (LLMs)
• What is Accelerated Computing? Benefits & Use Cases
• Ultimate Guide to Insurance Cloud Computing
• Cloud Quantum Computing & Top Cloud QC Vendors
• Why Should You Use Cloud Inference (Inference as a Service)?

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External Links

• 1. “Worldwide HPC and AI Training Market 2021 Actuals, 2022-26 Forecast.” (PDF). Intersect360 Research. May, 2022. Retrieved February 6,2024.
• 2. Matsuoka, S., Domke, J., Wahib, M., Drozd, A., & Hoefler, T. (2023). “Myths and legends in high-performance computing.” The International Journal of High Performance Computing Applications, 10943420231166608. Retrieved February 6,2024.