With all that noise and development, it is now believed that artificial intelligence (AI) needs HPC-level computing power to eliminate the latency which is inevitable in cloud environments due to the movement of data between cloud centres and create a cost-friendly cloud environment. The computational power and scalability of HPC clusters are critical enablers of AI-based software. On the other hand, AI can easily process workloads and simplify HPC-based infrastructures. The synergy of HPC and AI is expected to boost data analysis in various fields and simplify complex decision-making processes. “ This enables complex AI algorithms, especially the deep learning models to benefit from the immense computational power, enabling quicker training times and rapid insights. HPC facilitates simulations and modelling, and AI can optimise decision-making processes, improve product designs, and enhance overall operational efficiency, among others, ” Sudha KV, VP, Dell Technologies India, told FE-TransformX, adding that the collaboration of HPC and AI has the potential to redefine the future of business transformation.
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Synergy between HPC and AI
Apart from this big technology platforms are believed to have incorporated generative AI into all its offerings and bringing AI-enabled capabilities to enterprises of all sizes through its cloud offerings. Google, Microsoft and Amazon, among others, have all significantly revamped their cloud services with offerings to enable building AI apps, as per insights from ‘The AIdea of India: Generative AI’s potential to accelerate India’s digital transformation,’ report by EY India. “AI with its ability to automate tasks, enhance communication, and increase productivity, among others, can reshape workplaces into powerhouses of efficiency and flexibility. However, to fully unlock the potential of AI, businesses need to use High Performing Compute (HPC) devices to process and analyse the vast amounts of data required by AI algorithms,” Nishant Rajawat, founder, CEO, Cybernetyx, an assistive intelligence technology company, explained.
Use cases include NVIDIA Modulus, a neural network framework, that blends the power of physics and partial differential equations (PDEs) with AI to build more robust models for better analysis. Modulus is expected to solve multi-physics problems to perform automatic design space exploration 1,000X faster than traditional simulation.
AI in securing HPC
Industry experts believe that high-performance computing can be ‘high-performing’ for some traditional security techniques as they cannot keep up with the system. This might affect the speed, accuracy and safety standards of computing big data. Case in point, a mechanism designed to enforce a particular policy considered essential for security by one site might be considered a denial of service to legitimate users of another site. So, there is a need to mention the desired functioning of the system for customised security. It is believed that generative AI and responsible AI users can customise safety structures as per businesses’ needs. “ AI algorithms can analyse large datasets to understand individual customer preferences. This information, processed rapidly with HPC, enables businesses to offer highly personalised products, services, and marketing strategies, enhancing customer satisfaction and loyalty,” Ashher Zafar, co-founder, CTO, PlanckDOT, a cybersecurity platform, highlighted.
Interestingly, AI with HPC, can create more developed predictive models. This allows businesses to anticipate market trends, customer behaviours and operational needs more accurately, aiding in proactive planning. It is believed that the synergy between HPC and AI will be vital in discovering new drugs and treatments for various diseases. Researchers get to screen vast chemical libraries, predict drug binding affinities, and optimize drug candidates far more efficiently than with any legacy system. Case in point AstraZeneca, a pharmaceutical company, collaborated with Cerebras, an AI chip designer, to train HPC-powered AI to quickly read biomedical research papers, streamlining research and cutting down the time needed to develop new drugs. “ HPC accelerates risk modelling and algorithmic trading, while AI optimises early detection and customer service. Healthcare benefits from HPC-enabled genomics research, while AI aids diagnostics and personalised treatment plans. Pharmaceuticals can see improved efficiency through HPC-driven simulations and predictive maintenance, complemented by AI-driven supply chain optimisation,” Sumit Singh, co-founder and head of growth, Dashloc, a business solutions provider, said.
The road ahead
HPC uses rapid data processing and complex calculations, while AI creates intelligent systems that learn, reason, and make human-like decisions. This integration is expected to allow businesses to extract insights from big data, identify patterns, and make data-driven decisions, among others, in real-time. “ The integration of HPC and AI can enhance business performance by automating repetitive tasks, improving operational efficiency, and reducing costs. AI algorithms can be trained to perform tasks such as data analysis, customer service, and supply chain optimisation, among others. This can eventually free up human resources to focus on more strategic and creative initiatives,” Rakesh Ravuri, CTO, SVP engineering, Publicis Sapient, a digital business consulting platform, concluded.
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