“Deep Learning is a new area of Machine Learning research, which has been introduced with the objective of moving Machine Learning closer to one of its original goals: Artificial Intelligence. At the 2015 GPU Technology Conference, you can join the experts who are making groundbreaking improvements in a variety of deep learning applications, including image classification, video analytics, speech recognition, and natural language processing.”
The inaugural ISC Cloud & Big Data conference has announced its Call for Research Papers. The event takes place Sept. 28-30 in Frankfurt, Germany. The organizers are looking forward to welcoming international attendees – IT professionals, consultants and managers from organizations seeking information about the latest cloud and big data developments. Researchers in these two […]
Bridges is a uniquely capable supercomputer designed to help researchers facing challenges in Big Data to work more intuitively. Called Bridges, the new system will consist of tiered, large-shared-memory resources with nodes having 12TB, 3TB, and 128GB each, dedicated nodes for database, web, and data transfer, high-performance shared and distributed data storage, Hadoop acceleration, powerful new CPUs and GPUs, and a new, uniquely powerful interconnection network.
According to IDC, SGI has shipped approximately 8 percent of of all the Hadoop servers in production today. In fact, did you know that SGI introduced the word “Big Data” to supercomputing in 1996? Jorge Titinger, SGI President and CEO, shares SGI’s history in helping to design, develop, and deploy Hadoop clusters. (NOTE: Straw was substituted for actual hay to avoid any potential allergic reactions.)
CloudyCluster allows you to quickly set up and configure a cluster on Amazon Web Services (AWS) to handle the most demanding HPC and Big Data tasks. You don’t need access to a data center and you don’t have to be an expert in the ins and outs of running computationally intensive workloads in a cloud environment.
“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.”