Over at the Star Tribune, Curt Brown has posted a brief history on the life of Seymour Cray, the Father of the Supercomputing Industry. “It seems impossible to exaggerate the effect he had on the industry,” said Joel Birnbaum, a Hewlett-Packard executive. “Many of the things that high performance computers now do routinely were at the furthest edge of credibility when Seymour envisioned them. He ranks up there with Edison and Bell.”
By October 2015, the capacity of each of NOAA’s two operational supercomputers will jump to 2.5 petaflops, for a total of 5 petaflops – a nearly tenfold increase from the current capacity. “NOAA is America’s environmental intelligence agency; we provide the information, data, and services communities need to become resilient to significant and severe weather, water, and climate events,” said Kathryn Sullivan, Ph.D., NOAA’s Administrator.
“This presentation will highlight the use of GPU ray tracing for visualizing the process of photosynthesis, and GPU accelerated analysis of results of hybrid structure determination methods that combine data from cryo-electron microscopy and X-ray crystallography atom molecular dynamics with all- simulations.”
“HPC is transforming our everyday lives, as well as our not-so-ordinary ones. From nanomaterials to jet aircrafts, from medical treatments to disaster preparedness, and even the way we wash our clothes; the HPC community has transformed the world in multifaceted ways. For its 27th anniversary, the annual SC Conference will return to Austin, TX, a city that continues to develop new ways of engaging our senses and incubating technology of all types, including supercomputing.”
“Deep neural networks have recently emerged as an important tool for difficult AI problems, and have found success in many fields ranging from computer vision to speech recognition. Training deep neural networks is computationally intensive, and so practical application of these networks requires careful attention to parallelism. GPUs have been instrumental in the success of deep neural networks, because they significantly reduce the cost of network training, which then has allowed many researchers to train better networks. In this talk, I will discuss how we were able to duplicate results from a 1000 node cluster using only 3 nodes, each with 4 GPUs.”