Over at NERSC, Linda Vu writes that the SciDB open source database system is a powerful tool for helping scientists wrangle Big Data. “SciDB is an open source database system designed to store and analyze extremely large array-structured data—like pictures from light sources and telescopes, time-series data collected from sensors, spectral data produced by spectrometers and spectrographs, and graph-like structures that illustrate relationships between entities.”
“Sea level rise is one of the most visible signatures of our changing climate, and rising seas have profound impacts on our nation, our economy and all of humanity,” said Michael Freilich, director of NASA’s Earth Science Division. “By combining space-borne direct measurements of sea level with a host of other measurements from satellites and sensors in the oceans themselves, NASA scientists are not only tracking changes in ocean heights but are also determining the reasons for those changes.”
Today Intel Corporation and BlueData announced a broad strategic technology and business collaboration, as well as an additional equity investment in BlueData from Intel Capital. BlueData is a Silicon Valley startup that makes it easier for companies to install Big Data infrastructure, such as Apache Hadoop and Spark, in their own data centers or in the cloud.
Geert Wenes writes in the Cray Blog that the next generation of Grand Challenges will focus on critical workflows for Exascale. “For every historical HPC grand challenge application, there is now a critical dependency on a series of other processing and analysis steps, data movement and communications that goes well beyond the pre- and post-processing of yore. It is iterative, sometimes synchronous (in situ) and generally more on an equal footing with the “main” application.”
Today Intel released Intel Parallel Studio XE 2016, the next iteration of its developer toolkit for HPC and technical computing applications. This release introduces the Intel Data Analytics Acceleration Library, a library for big data developers that turns large data clusters into meaningful information with advanced analytics algorithms.
“CDSW’s organizers are professional programmers and data scientists and several of us have experience teaching data science in more traditional university and corporate settings. Our talk will describe how “democratized” data science is similar to — and sometimes extremely different from — these more traditional approaches. We will talk about some of the challenges we have faced and highlight some of our most inspirational successes.”
“Researchers at the U.S. Department of Energy’s Argonne National Laboratory will be testing the limits of computing horsepower this year with a new simulation project from the Virtual Engine Research Institute and Fuels Initiative (VERIFI) that will harness 60 million computer core hours to dispel those uncertainties and pave the way to more effective engine simulations.”