Artificial Intelligence (AI) is fast becoming the cornerstone of business analytics, allowing companies to generate value from the ever-growing datasets generated by today’s business processes. At the same time, the sheer volume and velocity of data demand high-performance computing (HPC) to provide the power needed to effectively train AIs, do AI inferencing, and run analytics. According to Hyperion Research, HPC-enabled AI, growing at more than 30 percent, is projected to be a $3.5 billion market in 2024.
This rising confluence of HPC and AI is being driven by businesses and organisations honing their competitive edge in the global marketplace as digital transformation is accelerated and brought to the next level through IT transformation processes.
“We’re seeing HPC-enabled AI on the rise because it extracts and refines data quicker and more accurately. This naturally leads to faster and richer insights, in turn enabling better business outcomes and facilitates new breakthroughs and better differentiation in products and services while driving greater cost savings,” said Mike Yang, President at Quanta Cloud Technology, better known as QCT.
While HPC and AI are expected to benefit most industries, the fields of healthcare, manufacturing and higher education and research (HER) and Finance stand to gain perhaps the most due to the high-intensity nature of the workloads involved.
Application of HPC-enabled AI in the fields of next-generation sequencing, medical imaging and molecular dynamics have the potential to speed drug discoveries and improve patient care procedures and outcomes. In manufacturing, finite element analysis, computer vision, electronic design automation and computer-aided design are facilitated by AI and HPC to speed product development, while analysis generated from Internet-of-Things (IoT) data can streamline supply chains, enhance predictive maintenance regimes and automate manufacturing processes. HER utilises technology to explore fields such as dynamic structure analysis, weather prediction, fluid dynamics and quantum chemistry in an ongoing quest to solve global problems like climate change and achieve breakthroughs and deeper exploration through cosmology and astrophysics.
Optimising HPC and AI Workloads
The AI and Machine Learning (ML) algorithms underlying these business and scientific advances have become significantly more complex, delivering faster yet more accurate results, but at the cost of significantly more computational power. The key challenge now facing organisations is building HPC, AI, HPC-enabled AI, and HPC-AI converged workloads—while shortening project implementation time. Ultimately, this will allow researchers, engineers, and scientists to concentrate fully on their research.
IT support would also need to actively manage their HPC and AI infrastructure, leveraging the right profiling tool for optimisation of HPC and AI workloads. Optimised HPC/AI infrastructure should deliver the right resources at the right time for researchers and developers to accelerate computational processes.
In addition, understanding workload demands and optimising performance helps IT avoid additional workload and extra labour for finetuning, significantly reducing the total cost of ownership (TCO). To optimise HPC and AI workloads effectively and quickly, organisations can consider the following steps:
- Identify key workload applications and data used by the customer, as well as the customer’s expectations and pain points
- Design infrastructure and building the cluster, ensuring that hardware and software stack can support the workloads
- Continue the process of always adjusting and finetuning
QCT leverages Intel’s profiling tool Intel Granulate gProfiler to reveal the behaviour of the workload before tapping its deep own deep expertise to analyse the behaviour and design a fine-tuning plan to help with optimisation. Through this process, organisations can ensure rapid deployment, simplified management, and optimised integrations—all at cost savings.
AI continues to offer transformational solutions for businesses and organisations, but the growing complexity of datasets and algorithms is driving greater demand on HPC to enable these power-intensive workloads. Workload optimisation effectively enhances the process and, at the heart of it, enables professionals in their fields to focus on their research to drive industry breakthroughs and accelerate innovation.
To discover how workload profiling can transform your business or organisation, click here.