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The Importance of Vectorization Resurfaces

Vectorization offers potential speedups in codes with significant array-based computations—speedups that amplify the improved performance obtained through higher-level, parallel computations using threads and distributed execution on clusters. Key features for vectorization include tunable array sizes to reflect various processor cache and instruction capabilities and stride-1 accesses within inner loops.

Five Reasons to Attend a New Kind of Developer Event

In this special guest feature, Ubuntu Evangelist Randall Ross writes that the OpenPOWER Foundation is hosting an all-new type of developer event. “The OpenPOWER Foundation envisioned something completely different. In its quest to redefine the typical developer event the Foundation asked a simple question: What if developers at a developer event actually spent their time developing?”

Dr. Eng Lim Goh on New Trends in Big Data and Deep Learning for Artificial Intelligence

In this video from SC16, Dr. Eng Lim Goh from HPE/SGI discusses new trends in HPC Energy Efficiency and Deep Learning for Artificial Intelligence. “Recently acquired by Hewlett Packard Enterprise, SGI is a trusted leader in technical computing with a focus on helping customers solve their most demanding business and technology challenges.”

Data Analytics, Machine Learning, and HPC in Today’s Changing Application Environment

In this video from the Intel HPC Developer Conference, Franz Kiraly from Imperial College London and the Alan Turing Institute describes why many companies and organizations are beginning to scope their potential for applying rigorous quantitative methodology and machine learning.

insideBIGDATA Guide to Scientific Research

In this new insideBIGDATA Guide to Scientific Research, the goal 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.

Cornell to Lead Aristotle Cloud Federation for Research

Today Cornell University announced a five-year, $5 million project sponsored by the National Science Foundation to build a federated cloud comprised of data infrastructure building blocks (DIBBs) designed to support scientists and engineers requiring flexible workflows and analysis tools for large-scale data sets, known as the Aristotle Cloud Federation.

Video: Data-driven Education and the Quantified Student

In this video from the PyData Seattle Conference, Lorena Barba from George Washington University presents: Data-driven Education and the Quantified Student. “Education has seen the rise of a new trend in the last few years: Learning Analytics. This talk will weave through the complex interacting issues and concerns involving learning analytics, at a high level. The goal is to whet the appetite and motivate reflection on how data scientists can work with educators and learning scientists in this swelling field.”

Primary Motivators of Big Data vis-à-vis Scientific Research

This article is the second in an editorial series with a goal 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.

DataFest Competition Brings Big Data to College Students

Students from more than 20 prestigious colleges and universities recently tried their hand at “Big Data” analysis at seven different campuses around the country during DataFest, an annual month-long data-analytics competitive event sponsored by the American Statistics Association.

Statistics is the Fastest Growing Undergraduate STEM Degree

Statistics—the science of learning from data—is the fastest-growing science, technology, engineering and math (STEM) undergraduate degree in the United States over the last four years, an analysis of federal government education data conducted by the American Statistical Association (ASA) revealed.