Convergence of HPC and AI: Driving innovation at a rapid pace

In today’s rapidly changing environment, getting high-quality products to market faster is essential for success. Many industries rely on high-performance computing (HPC) to achieve these goals.

Enterprises are increasingly turning to generative artificial intelligence (gen AI) to increase operational efficiency, accelerate business decisions, and drive growth. We believe that the convergence of HPC and artificial intelligence (AI) is key to helping companies remain competitive.

These innovative technologies complement each other, allowing organizations to leverage their unique value. For example, HPC provides high levels of computing power and scalability that are critical for running performance-intensive workloads. Likewise, AI allows organizations to handle workloads more efficiently and intelligently.

In the era of Gen AI and hybrid cloud, IBM Cloud® HPC provides the computing power your organization needs to succeed. An integrated solution for critical components of compute, network, storage and security, the platform aims to help enterprises address regulatory and efficiency requirements.

How AI and HPC Deliver Results Faster: Industry Use Cases

At the heart of it all is data that helps businesses gain valuable insights and accelerate innovation. Organizations with data virtually everywhere often have existing repositories acquired by running traditional HPC simulation and modeling workloads. These repositories can come from a variety of sources. By using these sources, organizations can apply HPC and AI to the same challenges to generate deeper, more valuable insights that drive innovation faster.

AI-based HPC applies AI to streamline simulation, known as intelligent simulation. In the automotive industry, intelligent simulation accelerates the innovation of new models. As vehicle and component designs often evolve from previous iterations, the modeling process undergoes significant changes to optimize qualities such as aerodynamics, noise, and vibration.

With millions of potential changes, assessing these qualities under different conditions, such as road type, can significantly extend the time to delivery of a new model. However, in today’s market, consumers demand rapid introduction of new models. Long development cycles can hurt automakers’ sales and customer loyalty.

Automakers with a wealth of data related to existing designs can use this large-scale data to train AI models. This helps identify the best areas for vehicle optimization, reducing the problem space and allowing traditional HPC methods to focus on more targeted design areas. Ultimately, this approach can help you produce better quality products in less time.

AI and HPC are driving innovation in electronic design automation (EDA). In today’s rapidly changing semiconductor environment, chip designs must be verified through billions of verification tests. However, if an error occurs during verification, it is impractical to re-run the entire verification test due to the resources and time required.

For EDA companies, using AI-infused HPC methods is critical to identifying which tests need to be rerun. This saves significant amounts of compute cycles and helps keep manufacturing schedules on schedule, ultimately allowing the company to deliver semiconductors to customers faster.

How IBM supports HPC and AI compute-intensive workloads

IBM designs infrastructure that provides the flexibility and scalability needed to support compute-intensive workloads such as AI and HPC. For example, managing the massive amounts of data associated with modern, high-fidelity HPC simulations, modeling, and AI model training can be critical and require high-performance storage solutions.

IBM Storage Scale is designed to be a high-performance, highly available distributed file and object storage system that can respond to the most demanding applications that read or write large amounts of data.

As organizations aim to scale their AI workloads, IBM watsonx™ on IBM Cloud® helps companies scale their workloads while training, validating, tuning, and deploying AI models. IBM also offers a graphics processing unit (GPU) option with NVIDIA GPUs on IBM Cloud, providing innovative GPU infrastructure for enterprise AI workloads.

However, it is important to note that GPU management is still required. Workload schedulers such as IBM Spectrum® LSF® efficiently manage the flow of work to GPUs, while IBM Spectrum Symphony®, a low-latency, high-performance scheduler designed for risk analysis workloads in the financial services industry, also supports GPU workloads. .

GPUs are used in a variety of industries that require intensive computing performance. For example, financial services organizations use Monte Carlo methods to predict the outcome of scenarios such as financial market movements or product pricing.

Monte Carlo simulations, which can be divided into thousands of independent tasks and run simultaneously on multiple computers, are well suited to GPUs. This allows financial services organizations to run simulations iteratively and quickly.

As businesses seek solutions to their most complex challenges, IBM is committed to helping them overcome obstacles and achieve success. With security and controls built into the platform, IBM Cloud HPC enables customers across industries to address third- and fourth-party risk using HPC as a fully managed service. The convergence of AI and HPC can help organizations stay competitive by generating intelligence that adds value and accelerates results.

Learn how IBM can help you accelerate innovation with AI and HPC.