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. …
Over the last few years we have detailed the explosion in new machine learning systems with the influx of novel architectures from deep learning chip startups to efforts from vendors and hyperscalers alike. …
Enterprises that want to leverage the huge amounts of data they are generating to gain useful insights and make faster and better business decisions are going to have to use machine learning at scale for modeling and training. …
If there is anything the hyperscalers have taught us, it is the value of homogeneity and scale in an enterprise. …
FPGA maker Xilinx has acquired Chinese deep learning chip startup DeePhi Tech for an undisclosed sum. …
For the past several years, machine learning as evolved by the hyperscalers has been trickling down from on high, through frameworks and services, into enterprises. …
Six years ago, when Google decided to get involved with the OpenPower consortium being put together by IBM as its third attempt to bolster the use of Power processors in the datacenter, the online services giant had three applications that had over 1 billion users: Gmail, YouTube, and the eponymous search engine that has become the verb for search. …
There is no question right now that if you have a big computing job in either high performance computing – the colloquial name for traditional massively parallel simulation and modeling applications – or in machine learning – the set of statistical analysis routines with feedback loops that can do identification and transformation tasks that used to be solely the realm of humans – then an Nvidia GPU accelerator is the engine of choice to run that work at the best efficiency. …
If the hype is to be believed, there is no computational problem that cannot be tackled faster and better by artificial intelligence. …
Here at The Next Platform, we’ve touched on the convergence of machine learning, HPC, and enterprise requirements looking at ways that vendors are trying to reduce the barriers to enable enterprises to leverage AI and machine learning to better address the rapid changes brought about by such emerging trends as the cloud, edge computing and mobility. …
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