As the world is now aware, China is now home to the world’s most powerful supercomputer, toppling the previous reigning system, Tianhe-2, which is also located in the country. …
This week at the International Supercomputing Conference (ISC ’16) we are expecting a wave of vendors and high performance computing pros to blur the borders between traditional supercomputing and what is around the corner on the application front—artificial intelligence and machine learning. …
Computing for neuroscience, which has aided in our understanding of the structure and function of the brain, has been around for decades already. …
As we have noted over the last year in particular, GPUs are set for another tsunami of use cases for server workloads in high performance computing and most recently, machine learning. …
Nvidia made a lot of big bets to bring its “Pascal” GP100 GPU to market and its first implementation of the GPU is aimed at its Tesla P100 accelerator for radically improving the performance of massively parallel workloads like scientific simulations and machine learning algorithms. …
Whether in the brain or in code, neural networks are shaping up to be one of the most critical areas of research in both neuroscience and computer science. …
Just a few years ago, the promise of ultra-low power, high performance computing was tied to the rather futuristic-sounding vision of a “brain chip” or neuromorphic processor, which could mimic the brain’s structure and processing ability in silicon—quickly learning and chewing on data as fast as it could be generated. …
For more than a decade, graphics processor maker Nvidia has been championing the adoption of GPU accelerators as heavy-lifting compute engines for an increasing array of applications that can take advantage of the parallel processing inherent in a GPU. …
It’s possible to count the number of major FPGA vendors on one hand, and despite a desire to differentiate, they are flocking together around some key areas where FPGA market growth seems most promising. …
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