In this video, Dan Reed from the University of Iowa describes the era of Exascale Computing and Big Data. In a recent paper co-authored with Jack Dongarra, Reed makes an impassioned plea for hardware and software integration and cultural convergence.
Daniel Gutierrez, Managing Editor, of insideBIGDATA has put together a terrific Guide to Scientific Research. The goal of this paper is to provide a road map for scientific researchers wishing to capitalize on the rapid growth of big data technology for collecting, transforming, analyzing, and visualizing large scientific data sets.
“This annual meeting aims to accelerate data-driven discovery and innovation by bringing together researchers, developers and end-users from academia, industry, utilities and state and federal governments. Jointly organized by Brookhaven National Laboratory (BNL), Stony Brook University (SBU), and New York University (NYU) the theme of this year’s conference is “Frontiers in Scientific Data.”
“We present a state-of-the-art image recognition system, Deep Image, developed using end-to-end deep learning. The key components are a custom-built supercomputer dedicated to deep learning, a highly optimized parallel algorithm using new strategies for data partitioning and communication, larger deep neural network models, novel data augmentation approaches, and usage of multi-scale high-resolution images.”
In this video from WestGrid in Canada, Dr. Yussanne Ma from the Michael Smith Genome Sciences Centre describes how high performance computing supports her research group’s work, highlighting a recent project where a bioinformatics pipeline was built for the personalized onco-genomics project (POG) at the BC Cancer Agency.