We have written much about large-scale deep learning implementations over the last couple of years, but one question that is being posed with increasing frequency is how these workloads (training in particular) will scale to many nodes. …
In the ideal hyperscaler and cloud world, there would be one processor type with one server configuration and it would run any workload that could be thrown at it. …
Over the last year in particular, we have documented the merger between high performance computing and deep learning and its various shared hardware and software ties. …
While the machine learning applications created by hyperscalers and the simulations and models run by HPC centers are very different animals, the kinds of hardware that help accelerate the performance for one is also helping to boost the other in many cases. …
Over the course of the last five years, GPU computing has featured prominently in supercomputing as an accelerator on some of the world’s fastest machines. …
Chip maker Nvidia was founded by people who loved gaming and who wanted to make better 3D graphics cards, and decades later, the company has become a force in computing, first in HPC and then in machine learning and now database acceleration. …
It is not news that offloading work from CPUs to GPUs can grant radical speedups, but what can come as a surprise is that scaling of these workloads doesn’t change just because they run faster. …
In the public cloud business, scale is everything – hyper, in fact – and having too many different kinds of compute, storage, or networking makes support more complex and investment in infrastructure more costly. …
When it comes to deep learning innovation on the hardware front, few other research centers have been as forthcoming with their results as Baidu. …
It is a coincidence, but one laden with meaning, that Nvidia is setting new highs selling graphics processors at the same time that SGI, one of the early innovators in the fields of graphics and supercomputing, is being acquired by Hewlett Packard Enterprise. …
All Content Copyright The Next Platform