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High-performance computing (HPC) has long been pivotal for carrying out data and compute-intensive large-scale scientific simulations, analytic workloads for advanced scientific progress and product innovations, in turn making HPC infrastructure more essential and valuable than ever. The convergence of HPC and big data technologies/machine learning (ML)/ deep learning (DL) is prescribed owing to the multifold growth of data across all HPC domains. HPC application experts are either already working or looking forward towards ML solutions to their applications, as it is proven successful in many scientific and commercial domains. Modern CPU/GPU-based HPC architectures are equipped with support for ML/DL promising the adaptability of AI capabilities in the HPC territory. Artificial intelligence (AI) enabled neuromorphic chips to add another ladder towards the convolution of AI on HPC. In this paper, we bring forth the merits of AI on HPC infrastructure for scientific applications in the shortlisted domains, viz weather and climate, astrophysics, agriculture, and bioinformatics. The paper discusses the current scenarios of wide adoption and merits of AI in the said domains. The survey lists the applications that are well received by the user communities for their performance, handling big unstructured data, improved results, etc., while solving the domain-specific problem. |
