Nvidia Lead Details Future Convergence of Supercomputing, Deep Learning

April 15, 2016 Nicole Hemsoth Deep learning could not have developed at the rapid pace it has over the last few years without companion work that has happened on the hardware side in high performance computing. While the applications and requirements for supercomputers versus neural network training are quite different (scalability, programming, etc.) without the rich base of GPU computing, high performance interconnect development, memory, storage, and other benefits from the HPC set, the boom around deep learning would be far quieter. In the midst of this convergence, Marc Hamilton has watched advancements on the HPC side over the years, beginning in the mid-1990s at Sun, where he spent 16 years, before becoming VP of high performance computing at HP. Now the VP of Solutions Architecture and Engineering, he says…


Link to Full Article: Nvidia Lead Details Future Convergence of Supercomputing, Deep Learning