Earlier this year, Microsoft announced its supercomputer hosted in Azure Cloud which it developed in collaboration with OpenAI. The company said that the supercomputer could train various artificial intelligence models and comes with more than 2,85,000 CPU cores, 10,000 GPUs, and 400Gbps of network connectivity for each GPU server.
Not just this, Hewlett Packard Enterprise recently acquired the supercomputing leader Cray, following which it has introduced the HPE Cray supercomputing line that can perform data-centric AI workloads with exceptionally high speed. It also built the new TX-GAIA (Green AI Accelerator) computing system at the Lincoln Laboratory Supercomputing Center which has been ranked as the most powerful AI supercomputer at any university in the world. With a performance of 100 AI petaflops, it can perform complex deep neural network operations with much ease.
Fugaku by Fujitsu is another supercomputer recently built which is tailored to run machine learning algorithms. Still, in testing mode, it is expected to be operational from 2020 and will prove crucial to scale up machine learning algorithms at a quick pace.
Furthermore, the US Department of Energy (DoE) also announced plans to build the world’s fastest supercomputer – Frontier, which will be jointly developed by AMD and Cray. With a computing speed of 1.5 quintillion calculations per second, it is expected to join Intel’s Aurora to become the second of the two exascale systems planned by US DoE for 2021. It is expected to perform advanced tasks in AI, cancer treatment research, nuclear physics and more. Aurora, which is speculated to be worth $500 million, is five times faster than IBM’s summit, and was also designed from ground up with AI in mind.
In fact, one of the latest developments in the field of supercomputers is NEC’s SX-Aurora TSUBASA which is one of the smallest supercomputers designed for AI and ML workloads. NEC has been renowned for its vector processors which gives an advantage while performing AI tasks. We recently covered an article comparing it with Nvidia’s A100, which is one of the most preferred systems today to carry AI workloads.
Not just the tech giants, but startups are also venturing into the domain of supercomputers. For instance, Canada-based Q Blocks uses peer to peer computing technology to build affordable supercomputers for small organisations and individual researchers that cannot afford expensive supercomputers. It is offering powerful GPUs at a reasonable cost to carry out tasks in AI and data science.
Back in India too, it has begun to take a lead in the supercomputers market. It has supercomputers such as PARAM, Anupam, Pratush, Mihir and others which have been developed for tasks such as data analysis, weather forecasting, and more. In fact, the Council of Scientific and Industrial Research had joined hands with Nvidia to set up the Centre of Excellence at CEERI which was speculated to house India’s first-ever AI supercomputer.
These and many more instances suggest that there is a flooding in the number of supercomputers across the globe. The computing beasts which were once owned by few select companies such as Nvidia and AMD, are now being explored by many other companies, research organisations and even startups.
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