We have written much over the last few years about the convergence of deep learning and traditional supercomputing but as the two grow together, it is clear that the tools for one area don’t always mesh well with those of the other. …
Now that deep learning at traditional supercomputing centers is becoming a more pervasive combination, the infrastructure challenges of making both AI and simulations run efficiently on the same hardware and software stacks are emerging. …
While deep learning models might not be able to simulate large-scale physical phenomena in the same way purpose-built supercomputers and their application stacks do, there is more research emerging that shows how traditional HPC simulations can be augmented, if not replaced in some parts, by neural networks. …
Containerization as a concept of isolating application processes while sharing the same operating system (OS) kernel has been around since the beginning of this century. …
On today’s episode of “The Interview” with The Next Platform we talk about the use of petascale supercomputers for training deep learning algorithms. …
If the hype is to be believed, there is no computational problem that cannot be tackled faster and better by artificial intelligence. …
One of the luckiest coincidences in the past decade has been that the hybrid machines designed for traditional HPC simulation and modeling workloads. …
Big data, data science, machine learning, and now deep learning are all the rage and have tons of hype, for better—and in some ways, for worse. …
Intel recently demonstrated a new type of DIMM memory technology called Multiplexer Combined Rank (MCR), also referred to as MRDIMMs, that provides up to 2.3X better performance for HPC workloads and up to a 2X better on AI inference workloads in comparison to 5th Gen Intel Xeon processors, based on internal Intel analysis. …
Sponsored Feature: Humans have wanted to know what the weather is going to do since first we understood how the forces of nature determined our expectations of survival. …
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