Nvidia is not the only company that has created specialized compute units that are good at the matrix math and tensor processing that underpins AI training and that can be repurposed to run AI inference. …
With each passing generation of GPU accelerator engines from Nvidia, machine learning drives more and more of the architectural choices and changes and traditional HPC simulation and modeling drives less and less. …
It has taken untold thousands of people to make machine learning, and specifically the deep learning variety, the most viable form of artificial intelligence. …
There are a lot of things that compute engine makers have to do if they want to compete in the datacenter, but perhaps the most important thing is to be consistent. …
At this point in supercomputing, it’s becoming an anomaly to see an upcoming double-digit petaflops system not using AMD for CPU and GPU, but the National Renewable Energy Laboratory will be taking a more traditional route for the “Kestrel” machine. …
Software maker VMware has always been about tight partnerships with other tech vendors. …
If not for delays, the long-awaited Aurora supercomputer at Argonne National Lab would likely just be coming online. …
SPONSORED Mention GPUs these days, and you will naturally think about how they can accelerate the most challenging AI and machine learning workloads as well as how they are used in gaming platforms. …
Although there are now well-engineered systems that tightly package compute, acceleration, and data movement for deep learning training, for some users, working on time-critical AI training (and constant retraining), the backend applications and frameworks require a different way of thinking. …
About 15 years ago, as Swami Sivasubramanian was making his way from grad school back into the working world, he saw that developers and builders at enterprises were being held back not by their skills or their ideas, but by their inability to access the technology needed to bring those ideas to the fore. …
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