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. …
The in-person GPU Technical Conference held annually in San Jose may have been canceled in March thanks to the coronavirus pandemic, but behind the scenes Nvidia kept on pace with the rollout of its much-awaited “Ampere” GA100 GPU, which is finally being unveiled today. …
The one thing that AMD’s return to the CPU market and its more aggressive moves in the GPU compute arena have done, as well as Intel’s plan to create a line of discrete Xe GPUs that can be used as companions to its Xeon processors, has done is push Nvidia and Arm closer together. …
The datacenter business at Nvidia hit a rough patch a little less than a year ago, but it is starting to pick up again after a few quarters of declines and may soon be in positive growth territory. …
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. …
GPU chip maker Nvidia doesn’t just make the devices that end up in some of the largest supercomputers in the world. …
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. …
We have to admit that it is often a lot more fun watching an upstart carve out whole new slices of business, or create them out of what appears to be thin air, in the datacenter than it is to watch how it will respond to intense competitive pressures and somehow manage to keep growing despite that. …
More than five years ago, Nvidia, driven by its co-founder and CEO, Jensen Huang, turned its considerable focus to developing technologies for the revitalized and burgeoning artificial intelligence space. …
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