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Interview: Dr. Michael Ernst from Brookhaven National Laboratory

I recently caught up with Dr. Michael Ernst, Director of the RHIC and ATLAS Computing Facility at Brookhaven National Laboratory, to discuss how Brookhaven National Laboratory has found an innovative and inexpensive way to use AWS cloud spot instances when working with CERN’s LHC ATLAS experiment in order to speed up research during critical time periods.

Video: Machine Learning Overview from NERSC

In this video from the HPC User Forum in Tucson, Prabhat from NERSC presents: Machine Learning. “Prabhat leads the Data and Analytics Services team at NERSC. His current research interests include scientific data management, parallel I/O, high performance computing and scientific visualization.”

Best Practices – Big Data Acceleration

“This talk will provide an overview of challenges in accelerating Hadoop, Spark and Memcached on modern HPC clusters. An overview of RDMA-based designs for multiple components of Hadoop (HDFS, MapReduce, RPC and HBase), Spark, and Memcached will be presented. Enhanced designs for these components to exploit in-memory technology and parallel file systems (such as Lustre) will be presented. Benefits of these designs on various cluster configurations using the publicly available RDMA-enabled packages from the OSU HiBD project (http://hibd.cse.ohio-state.edu) will be shown.”

Adler Planetarium Uses Attendance Data to Visualize and Enhance Visitor Experience

Adler Planetarium enlisted the help of Chicago-based Inquidia Consulting, a leading data engineering and data science firm to select and build an advanced data management and visualization platform

Case Studies: Big Data and Scientific Research

This is the fifth and final article 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.

Big Data and Open Science Data

This article is the fourth 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.

Big Data Technology for Scientific Research

This article is the third 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.

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.

Brightspace Insights™ Empowers Instructors with Data From Across the Education Ecosystem

D2L (formerly “Desire2Learn”), a software leader that makes learning experiences better, unveiled the newest version of its Brightspace Insights analytics suite, marking a significant step forward in technology’s role in advancing learning globally. With this release, D2L is the first LMS provider to aggregate and capture streamed student data from across the entire learning ecosystem.

AstroCompute in the Cloud Grant Program Launches

The Square Kilometer Array (SKA) Organisation and AWS are launching the AstroCompute in the Cloud grant program to accelerate the development of innovative tools and techniques for processing, storing and analyzing the global astronomy community’s vast amounts of astronomic data in the cloud.