Maximizing Efficiency and Productivity with High Performance Computing Systems

In an interaction with Asia Business Outlook, Vinay Sinha, Corporate Vice President & Managing Director, India Sales, AMD, shares his views on how the high-performance computing systems can be utilized to maximize efficiency and productivity, the key considerations that organizations should consider when implementing a high-performance computing system, the most common resources needed to deploy high-performance computing systems and more.

How can high-performance computing systems be utilized to maximize efficiency and productivity in business operations?

High-performing computing (HPC) is a broad class of powerful compute systems ranging from simple servers to world-class supercomputers, which are being used to solve everyday challenges as well as the world's most complex problems. HPC has become a critical piece of the digital world today as it helps run complex simulations and calculations that are needed in diverse sectors including scientific research, engineering, security, space exploration etc. Traditionally, HPC was utilized in national labs and research environments. But today these powerful systems are being installed by large corporates across industries to draw real time, actionable insights from very large amounts of business data. For example, in India, large IT organizations are using HPC to drive simulations, large-scale data analytics, and AL/ML workloads. Telecom is another industry where we can find a lot of use cases for HPC.

What are some key considerations that organizations should consider when implementing a high-performance computing system?

Organizations need to understand that building and operating an HPC environment is a large undertaking. These systems need high network bandwidth, HPC-specific software for orchestration, job scheduling and managing, and security. Understanding these parameters, identifying the use cases within the organization, and efficient utilization can lead to successful implementation of HPC.

How can cloud-based high-performance computing systems manage scalability and flexibility, and what are their potential limitations?

HPC on cloud manages scalability and flexibility by allowing organizations to provision computing resources on-demand and scaling them up or down as needed. This flexibility enables businesses to dynamically allocate resources based on workload requirements, ensuring optimal performance and cost efficiency.

However, the very definition of cloud implies that we do not have control over the location of the servers. They could be together at one site or geographically dispersed. If it is the latter, then latency becomes unmanageable. Network latency can impact performance, particularly when dealing with large data sets or real-time processing requirements. For HPC to work optimally, it is desirable to have all the servers at one data center for maximum efficiency.

The cloud based HPC systems also rely on network connectivity to transfer data between the cloud infrastructure and the organization’s premises. Any kind of bandwidth limitations can impact performance. In addition, HPC is often used for highly confidential workloads. So, HPC via cloud must address security and any data compliance requirements.

What are some of the most common resources needed to deploy high-performance computing systems?

To deploy HPC, organizations need to have a high-performance IT infrastructure as well as the right skill sets to procure, install, configure, deploy, scale, and manage this infrastructure. When selecting the HPC systems, take five important considerations – workloads, compute, performance tools, storage, and deployment. You should audit the workloads and make sure the HPC systems are speed optimized for your work to get fast results. Secondly, select processors with the optimal core count, frequency, and memory to give you the best compute power.

Third are the performance tools. Make sure your workloads can take advantage of system functionality by using software and tools that are designed for your systems to get additional output. Then comes storage. Depending on your requirements, and the scale of deployment, decide how much shared storage is needed; do you need a local or network scratch storage and what would you do for network backup and recovery.

Finally, it comes to deployment. Understanding how you will power and cool your system will influence how you size a cluster. In addition, as the demand for HPC compute increases, we are running into a scenario where energy consumption is a gating factor. Organizations must look for options that focus on accelerating server energy efficiency.

What is the organizational imperative for investing in HPC?

HPCs are quite an investment. However, businesses should look at it from a time, consumption, and efficiency lens. If it takes two years for a company to come up with a product, the same product can be designed much faster, maybe in six months, using HPC because these systems can solve complex problems through faster data processing, running complex simulations, and quicker analysis. This saves time, money and gives any organization the competitive advantage to grow business.