November 17, 2021
Enterprise Tech enterprise&cloud Innovation Technology

Scaling AI Workloads With HPC Environment

High-performance computing (HPC) is the ability to process data and perform complex calculations at high speeds. HPC uses clusters of machines connected together to enable workloads to be processed in parallel. One of the best-known types of HPC solutions is the supercomputer. A supercomputer contains thousands of compute nodes that work together to complete multiple tasks.  

As artificial intelligence applications evolve, the size and amount of data that organizations have to work with are growing exponentially. In many areas that AI uses vast amounts of data, such as data analytics, streaming a live sporting event, tracking weather, testing new products, or analyzing stock trends, the ability to process data in real-time is crucial. To make it happen, organizations have leveraged the HPC environments to scale their AI workloads because HPC enables developers, organizations to train highly complex machine learning algorithms, businesses to process streaming data in real-time, and researchers to perform predictive analyses.

Microsft’s Azure is one of the players that solve compute-intensive deep learning workloads on an InfiniBand network or bare-metal Cray supercomputer. In Azure, one of the case studies is with Audi’s self-driving part, which is one of the areas that AI applications run. Audi relies on simulations for their driving studies, and these simulations use enormous volumes of data generated by sensors in the vehicles. These sensors and functions must be faultless and able to handle any driving situation, for example, detecting pedestrians regardless of the light, weather or traffic conditions. As the level of automation continues to increase and once vehicles become completely autonomous with no human driver at all, the way in which these sensors cooperate will become more sophisticated. In that phase, a robust HPC environment is needed to scale this use case because, in that example, Audi would have to acquire and operate approximately 200 petabytes of storage capacity and the same amount of computing capacity to run the simulations in real-time.

On scaling HPC workloads aspect, AI can benefit immensely from HPC systems that can scale to a massive degree. Applying deep learning to HPC workloads is known as HPC-on-AI. Deep learning is a great match for problems commonly addressed by HPC that involve very large, multidimensional data sets. These include tasks like pattern classification, pattern clustering, and anomaly detection. For example, deep learning on HPC systems can help identify fraudulent credit card transactions or help predict which patients are at risk for heart disease. And Intel is one of the players in this field, addresses this as well in one of the reports as “Unlike past approaches, AI empowers HPC systems beyond simplistic rule-based instructions. Instead, AI evaluates data using an instruction set of ‘theories’ and algorithms. By learning from these theories, AI can better predict and understand the context using inference to fill in data gaps. AI models complement more traditional HPC solutions to reveal insights faster, and more comprehensively, than data-processing and analytics-based applications can by themselves.”

One of the enterprises that are leading in this field, Northern Data, is providing sustainable and green high-performance computing through proprietary AI that involves thousands of advanced processors working in parallel to process billions of pieces of data in real-time. Aroosh Thillainathan, CEO of Northern Data, states that “Today, the integration of HPC and AI has become an exciting, innovative paradigm. IT organizations are designing HPC architectures to accommodate increasing AI workloads. AI frameworks and developer tools are getting optimized to address performance needs, allowing for much larger batch sizes to be processed on industry-standard CPUs. This shift to AI-focused infrastructure is happening today as organizations roll out systems that bring together the capabilities of HPC, data analytics, and AI.”

There is no doubt that AI will contribute to the future definition of HPC, and there will be more AI applications with having heavy data load to evolve on HPC as increasing global data creation is increasing drastically.