There is a rumor going around that a certain hyperscaler is going to be augmenting its GPU-based machine learning training and will be adopting Intel’s Nervana Neural Network Processor (NNP) for at least some of its workloads. …
In the modern distributed computing world, which is getting ever more disaggregated and some might say discombobulated, as every day passes, the architecture of the network in the datacenter is arguably the most important factor in determining if applications will perform well or not. …
The hyperscalers and cloud builders of the world build things that often look and feel like supercomputers if you squint your eyes a little, but if you look closely, you can often see some pretty big differences. …
If there is one rule of computing, is that it gets increasingly more expensive and more difficult to scale up performance within a single device or a single server node. …
Gentlemen (and women), start your inference engines.
One of the world’s largest buyers of systems is entering evaluation mode for deep learning accelerators to speed services based on trained models. …
Hyperscalers have billions of users who get access to their services for free, but the funny thing is that these users act like they are paying for it and expect for these services to be always available, no excuses. …
Here at The Next Platform, we tend to keep a close eye on how the major hyperscalers evolve their infrastructure to support massive scale and evermore complex workloads. …
Distributed applications, whether they are containerized or not, have a lot of benefits when it comes to modularity and scale. …
Scaling the performance of machine learning frameworks so they can train larger neural networks – or so the same training a lot faster – has meant that the hyperscalers of the world who are essentially creating this technology have had to rely on increasingly beefy compute nodes, these days almost universally augmented with GPUs. …
There has been much discussion about the “black box” problem of neural networks. …
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