Today ISC 2017 announced that data scientist, Prof. Dr. Jennifer Tour Chayes from Microsoft Research will give the opening keynote at the conference. “I’ll discuss in some detail two particular applications: the very efficient machine learning algorithms for doing collaborative filtering on massive sparse networks of users and products, like the Netflix network; and the inference algorithms on cancer genomic data to suggest possible drug targets for certain kinds of cancer,” explains Chayes.
Intel DAAL is a high-performance library specifically optimized for big data analysis on the latest Intel platforms, including Intel Xeon®, and Intel Xeon Phi™. It provides the algorithmic building blocks for all stages in data analysis in offline, batch, streaming, and distributed processing environments. It was designed for efficient use over all the popular data platforms and APIs in use today, including MPI, Hadoop, Spark, R, MATLAB, Python, C++, and Java.
Over at TACC, Faith Singer-Villalobos writes that researchers are using the Rustler supercomputer to tackle Big Data from self-driving connected vehicles (CVs). “The volume and complexity of CV data are tremendous and present a big data challenge for the transportation research community,” said Natalia Ruiz-Juri, a research associate with The University of Texas at Austin’s Center for Transportation Research. While there is uncertainty in the characteristics of the data that will eventually be available, the ability to efficiently explore existing datasets is paramount.
In this week’s Sponsored Post, Nicolas Dube of Hewlett Packard Enterprise outlines the future of HPC and the role and challenges of exascale computing in this evolution. The HPE approach to exascale is geared to breaking the dependencies that come with outdated protocols. Exascale computing will allow users to process data, run systems, and solve problems at a totally new scale, which will become increasingly important as the world’s problems grow ever larger and more complex.
In this video, Jonathan Allen from LLNL describes how Lawrence Livermore’s supercomputers are playing a crucial role in advancing cancer research and treatment. “A historic partnership between the Department of Energy (DOE) and the National Cancer Institute (NCI) is applying the formidable computing resources at Livermore and other DOE national laboratories to advance cancer research and treatment. Announced in late 2015, the effort will help researchers and physicians better understand the complexity of cancer, choose the best treatment options for every patient, and reveal possible patterns hidden in vast patient and experimental data sets.”
“For many urban questions, however, new data sources will be required with greater spatial and/or temporal resolution, driving innovation in the use of sensors in mobile devices as well as embedding intelligent sensing infrastructure in the built environment. Collectively, these data sources also hold promise to begin to integrate computational models associated with individual urban sectors such as transportation, building energy use, or climate. Catlett will discuss the work that Argonne National Laboratory and the University of Chicago are doing in partnership with the City of Chicago and other cities through the Urban Center for Computation and Data, focusing in particular on new opportunities related to embedded systems and computational modeling.”
“Atos is determined to solve the technical challenges that arise in life sciences projects, to help scientists to focus on making breakthroughs and forget about technicalities. We know that one size doesn’t fit all and that is the reason why we studied carefully The Pirbright Institute’s challenges to design a customized and unique architecture. It is a pleasure for us to work with Pirbright and to contribute in some way to reduce the impact of viral diseases”, says Natalia Jiménez, WW Life Sciences lead at Atos.
In this presentation from Nvidia, top AI experts from the world’s most influential companies weigh in on predicted advances for AI in 2017. “In 2017, intelligence will trump speed. Over the last several decades, nations have competed on speed, intent to build the world’s fastest supercomputer,” said Ian Buck, VP of Accelerated computing at Nvidia. “In 2017, the race will shift. Nations of the world will compete on who has the smartest supercomputer, not solely the fastest.”
The 3rd annual International Workshop on High-Performance Big Data Computing (HPBDC) has issued its Call for Papers. Featuring a keynote by Prof. Satoshi Matsuoka from Tokyo Institute of Technology, the event takes place May 29, 2017 in Orlando, FL.
In this special guest feature, Daniel Gutierrez from insideBIGDATA offers up his 2017 roundup industry predictions from Big Data thought leaders. “AI, ML, and NLP innovations have really exploded this past year but despite a lot of hype, most of the tangible applications are still based on specialized AI and not general AI. We will continue to see new use-cases of such specialized AI across verticals and key business processes. These use-cases would primarily be focused on the evolutionary process improvement side of the digital transformation.”