How expensive and difficult does hyperscale-class AI training have to be for a maker of self-driving electric cars to take a side excursion to spend how many hundreds of millions of dollars to go off and create its own AI supercomputer from scratch? …
When you have 54.2 billion transistors to play with, you can pack a lot of different functionality into a computing device, and this is precisely what Nvidia has done with vigor and enthusiasm with the new “Ampere” GA100 GPU aimed at acceleration in the datacenter. …
It is hard to remember sometimes way back when, in 2008, as Nvidia first took a stab at GPU compute in the datacenter with the original Tesla GPU accelerators and a very rudimentary CUDA programming environment for offloading parallel algorithms from CPUs to GPUs. …
It is not inconceivable, but probably also not very likely, that the datacenter business at GPU juggernaut Nvidia could at some point in the next one, two, or three years equal that of the core and foundational gaming sector. …
Indiana University is the proud owner of the first operational Cray “Shasta” supercomputer on the planet. …
Like their US-based counterparts, Google and Amazon, the Chinese Internet giants Baidu and Alibaba rely on GPU acceleration to drive critical parts of their AI-based services. …
The San Diego Supercomputing Center is overdue for a new supercomputer and thanks to a $10 million grant from the National Science Foundation, next year it will finally get one. …
If you were expecting Nvidia to start talking about its future “Ampere” or “Einstein” GPUs for Tesla accelerated computing soon just because AMD is getting ready to roll out “Navi” GPUs next year and Intel is working on its Xe GPU cards for delivery next year, too, you are going to have to wait a bit longer. …
The alliance between high performance computing and artificial intelligence is proving to be a fruitful one for researchers looking to accelerate scientific discovery in everything from climate prediction and genomics to particle physics and drug discovery. …
Creating the Tesla GPU compute platform has taken Nvidia the better part of a decade and a half, and it has culminated in a software stack comprised of various HPC and AI frameworks, the CUDA parallel programming environment, compilers from Nvidia’s PGI division and their OpenACC extensions as well as open source GCC compilers, and various other tools that together account for tens of millions of lines of code and tens of thousands of individual APIs. …
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