The nexus of traditional high performance computing and artificial intelligence is a fact, not a theory, and the exascale-class machinery installed in the United States, Europe, China, and Japan will be a showcase for how these two powerful simulation and analytical prediction techniques can be brought together in many different ways. …
If you were going to build an electronic brain in 2022, it might look something like the Neocortex supercomputer at the Pittsburgh Supercomputing Center at Carnegie Mellon University. …
Industry benchmarks are important because, no matter that comparisons are odious, IT organizations nonetheless have to make them to plot out the architectures of their future systems. …
There may not be as much structured data in the world as there is unstructured data, but one could easily argue that the structured data – mostly purchasing transactions and other kinds of historical data data stored in systems of record – is at least of equal value. …
Paid Feature Over the last several years, the limiting factors to large-scale AI/ML were first hardware capabilities, followed by the scalability of complex software frameworks. …
Normally, when we look at a system, we think from the compute engines at a very fine detail and then work our way out across the intricacies of the nodes and then the interconnect and software stack that scales it across the nodes into a distributed computing platform. …
Regular readers of The Next Platform know there are few more attention-grabbing tech events than Nvidia’s GPU Technology Conference (GTC). …
To hardware or not to hardware, that is a real question for vendors peddling all kinds of software in the datacenter. …
Paid Post There’s no doubt that the repurposing of GPU silicon has accelerated the development of artificial intelligence technology over the last decade. …
When any new abstraction layer comes to compute, it can only think in integers at first, and then it learns to do fractions and finally, if we are lucky – and we are not always lucky – that abstraction layer learns to do multiplication and scale out across multiple nodes as well as scaling in – slicing itself into pieces – within a single node. …
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