Enter the New Era of AI-Accelerated HPC
For years, end users, system builders, solution providers, and developers have harnessed the power of HPC to solve the world’s toughest and most complex problems. However, the persistent growth of data, the need for faster time to value, demands for greater and deeper insights for scientific discovery, and the added constraints of time and costs are pushing the limits of current systems.
At the same time, AI algorithms are increasing in sophistication and can handle much larger datasets than in previous years, making them an ideal fit for addressing growing scientific workloads. Organizations that leverage the power of AI and HPC together can reduce their time to insight while meeting, or exceeding, the same levels of accuracy, ultimately enabling them to tackle some of the world’s most-complex and -pressing problems.
For example, the Argonne National Laboratory’s Argonne Leadership Computing Facility (ALCF) in Illinois, the future home to the Aurora exascale HPC system, is helping to advance scientific research through a convergence of HPC, high-performance data analytics, and AI. The latest projects slated for ALCF will use AI to model fusion energy reactor conditions; develop noninvasive, patient-specific fluid models to understand the progression and localization of different human diseases; and better understand the multiphysics in a fusion reactor.
Explore our collection of customer success stories to discover how other organizations and research institutions are leveraging AI-accelerated HPC to drive accurate and impactful scientific innovation.
Understand the Challenges of AI in HPC
As you start the process of getting your own AI-accelerated HPC initiative running, it’s important to understand common challenges you may face.
- For AI and HPC configurations, traditionally there is a trade-off between AI and HPC requirements within the CPU architecture. AI-heavy workloads typically exchange core count for speed, while HPC workloads often prefer greater compute performance with a high core count and more core-to-core bandwidth.
- Increasingly data-intensive workloads, such as modeling, simulation, and AI, create performance bottlenecks that require solutions with high-bandwidth memory that’s architected to unlock and accelerate them.
- The high level of complexity of AI in HPC is a major source of friction for adoption. The skill sets for AI and HPC are very domain specific, and finding talent skilled in both areas is difficult. However, without this talent, AI-accelerated HPC initiatives might not move forward.
To help customers overcome these obstacles, we collaborate closely with the HPC community on AI usage to share expertise and ideas and offer innovative solutions, using our leading HPC technologies.
Create Your AI-Accelerated HPC Deployment Plan
An essential step to accelerating your HPC projects with AI is the creation of a comprehensive deployment plan that covers your organization’s needs and requirements to ensure you have the right technologies in place for research and discovery.
As you look to add robust AI capabilities to your HPC environment, here are some questions to ask so you can make more-informed technology decisions:
- What time and accuracy requirements does your output need to meet?
- What types of algorithm bias should you be aware of and avoid?
- What trade-off concessions are acceptable to achieve your sensitivity or specificity requirements?
- Will your model choice, dataset, and output change in size and direction?
- Where and how will code changes occur for projects?
- What’s the best way for you to achieve code changes?
- Will a significant amount of rewriting code be required from use case to use case?
- What types of workloads, and how many, will be run? How often will workloads need to run? Will they run continuously?
The answers to these questions can provide you with a solid foundation of requirements to use when exploring system design options with your technology partner.
Choose Tech That Enables AI-Accelerated HPC Discoveries
The key to realizing the promise of AI in HPC is selecting the right technologies that work together to maximize memory bandwidth and compute to match the demands of your dynamic workload profiles.
Intel offers a comprehensive set of HPC and AI technologies built on an open standards‒based, cross-architecture framework to simplify deployment and provide the flexible power and performance you need to meet the demands of your unique workloads. Additionally, our robust, open source software tools help accelerate code development, as developers can write code once and deploy on any system across the data center and cloud.
Pick Hardware with High Performance and Efficiency
To begin building your unique combination of AI-accelerated HPC technologies, we suggest starting with a strong hardware foundation, such as one powered by Intel® Xeon® Scalable processors. These CPUs feature integrated Intel® Accelerator Engines for AI and HPC, including Intel® Advanced Matrix Extensions (Intel® AMX) and Intel® Advanced Vector Extensions 512 (Intel® AVX-512), to deliver outstanding performance to support demanding HPC and AI workloads.
If your work involves highly complex workloads focused on large-scale training and inference, you may want to consider more-specialized hardware that delivers higher levels of throughput.
- Intel® Gaudi® AI accelerators provide high-efficiency, scalable compute to enable data scientists and machine learning engineers to accelerate training and build new or migrate existing models with just a few lines of code. Intel® Gaudi® AI accelerators also provide incredible power efficiency to help lower costs and increase sustainability.
- Intel® Xeon® CPU Max Series processors deliver the breakthrough performance you need for future AI-HPC capabilities while unblocking the bottlenecks for memory-bound workloads. The Intel® Xeon® CPU Max Series is the first and only x86-based processor supercharged with high-bandwidth memory that can deliver up to 4.8x better performance compared to competition on real-world HPC and AI workloads. 1To maximize the impact of the Intel® Max Series CPUs and to take on your most challenging workloads, the Intel® Data Center GPU Max Series can be integrated as a discrete GPU. It packs over 100 billion transistors into one package and includes the Intel® Xe Link high-speed, coherent, unified fabric to give you the flexibility to run any form factor to enable scale-up and scale-out.
Organizations across the world are currently using these Intel® technologies to advance their work. For example, the Texas Advanced Computing Center (TACC) is using Intel® Xeon® CPU Max Series, Intel® Data Center GPU Max Series, and Intel® Xeon® Scalable processors to support academic research across the US. While Argentina’s Servicio Meteorológico Nacional (SMN), home to the most powerful supercomputer in Latin America for academic research, is built on Intel® Max Series CPUs and GPUs.
Accelerate Your HPC and AI Projects with Robust Software Tools
As the demand for AI and HPC grows, developers face several challenges when looking for ways to build fast HPC apps that scale easily across architectures. Transitioning software to function on HPC clusters and efficiently programming high-performance parallel computing can require a significant time investment for developers. At the same time, developers need to accelerate specialized workloads across architectures while ensuring their code works with as many hardware types and computing models as possible—also a time-consuming and costly endeavor.
To help developers overcome these challenges, Intel takes an open approach to HPC software and HPC optimization, offering open language Intel® oneAPI Toolkits that work across heterogeneous networks. This allows developers to build high-performance, parallel computing‒optimized, cross-architecture applications faster and easier.
The Intel® oneAPI Base Toolkit and Intel® oneAPI HPC Toolkit allow developers to build, analyze, optimize, and scale HPC applications across multiple types of architectures easier and faster. For developers, data scientists, and researchers working with AI and analytics workloads, Intel offers the Intel® oneAPI AI Analytics Toolkit, which features familiar Python tools and AI frameworks to accelerate AI pipelines, maximize performance, and provide interoperability for more-efficient development. Additionally, both the HPC and AI toolkits are built using oneAPI libraries for low-level compute optimizations. By building HPC applications with oneAPI, developers can avoid proprietary programming code lock-in to maximize discovery and uncover new opportunities.
Accelerate Your HPC and AI Workloads with Intel
As you take the next steps toward implementing AI in HPC, our leading technologies, vast partner ecosystem, and deep community connections can help you simplify and accelerate your journey. To learn more about what Intel offers for your organization and to get started, connect with your Intel® representative or any Intel® AI or HPC technology partner.