
The Buck Stops – And Starts – Here For GPU Compute
Ian Buck doesn’t just run the Tesla accelerated computing business at Nvidia, which is one of the company’s fastest-growing and most profitable products in its twenty five year history. …
Ian Buck doesn’t just run the Tesla accelerated computing business at Nvidia, which is one of the company’s fastest-growing and most profitable products in its twenty five year history. …
A major transformation is happening now as technological advancements and escalating volumes of diverse data drive change across all industries. …
When Nvidia co-founder and chief executive officer Jensen Huang told the assembled multitudes at the keynote opening to the GPU Technology Conference that the new DGX-2 system, weighing in at 2 petaflops at half precision using the latest Tesla GPU accelerators, would cost $1.5 million when it became available in the third quarter, the audience paused for a few seconds, doing the human-speed math to try to reckon how that stacked up to the DGX-1 servers sporting eight Teslas. …
If you are running applications in the HPC or AI realms, you might be in for some sticker shock when you shop for GPU accelerators – thanks in part to the growing demand of Nvidia’s Tesla cards in those markets but also because cryptocurrency miners who can’t afford to etch their own ASICs are creating a huge demand for the company’s top-end GPUs. …
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. …
Neural networks live on data and rely on computational firepower to help them take in that data, train on it and learn from it. …
The combination of the excitement for new video games, the machine learning software revolution, the buildout of very large supercomputers based on hybrid CPU-GPU architectures, and the mining of cryptocurrencies like Bitcoin and Ethereum have combined into a quadruple whammy that is driving Nvidia to new heights for revenues, profits, and market capitalization. …
Energy is not free, not even to energy companies, and so they are just as concerned with being efficient with their supercomputers as the most penny pinching hyperscaler or cloud builder where the computing is the product. …
The differences between peak theoretical computing capacity of a system and the actual performance it delivers can be stark. …
Generally speaking, the world’s largest chip makers have been pretty secretive about the giant supercomputers they use to design and test their devices, although occasionally, Intel and AMD have provided some insight into their clusters. …
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