For centuries, scientific research has been about data, and as data in research continues to grow exponentially, so does the importance of how it’s stored. A key example of how the scientific field can tackle Big Data storage is DESY, a scientific research organization dedicated to providing scientists worldwide faster access to insights into samples, making optimal data management in a high-volume environment extremely critical.
On September 20, early-bird pricing for the ISC Cloud and ISC Big Data registrations will be...
The National Center for Atmospheric Research (NCAR) has recently implemented an enhanced...
IBM has announced significant advances in Watson's cognitive computing capabilities that are...
In the age of big data, visualization tools are vital. With a single glance at a graphic display, a human being can recognize patterns that a computer might fail to find even after hours of analysis. But what if there are aberrations in the patterns? Or what if there’s just a suggestion of a visual pattern that’s not distinct enough to justify any strong inferences? Or what if the pattern is clear, but not what was to be expected?
Florida Polytechnic University, Flagship Solutions Group and IBM have announced a new supercomputing center at the University composed of IBM high performance systems, software and cloud-based storage, to help educate students in emerging technology fields. Florida Polytechnic University is the newest addition to the State University System and the only one dedicated exclusively to science, technology, engineering and mathematics (STEM).
NCSA’s Blue Waters project will offer a graduate course on High Performance Visualization for Large-Scale Scientific Data Analytics in Spring 2015 and is seeking university partners who are interested in offering the course for credit to their students. This semester-long online course will include video lectures, quizzes and homework assignments and will provide students with free access to the Blue Waters supercomputer.
In a society that has to understand increasingly big and complex datasets, EU researchers are turning to the subconscious for help in unraveling the deluge of information. Big Data refers to large amounts of data produced very quickly by a high number of diverse sources. Data can either be created by people or generated by machines, such as sensors gathering climate information, satellite imagery, digital pictures and videos...
The Michael J. Fox Foundation for Parkinson's Research (MJFF) and Intel have announced a collaboration aimed at improving research and treatment for Parkinson's disease — a neurodegenerative brain disease second only to Alzheimer's in worldwide prevalence. The collaboration includes a multiphase research study using a new big data analytics platform that detects patterns in participant data collected from wearable technologies.
Prof. Dr. Stefan Wrobel, M.S., is director of the Fraunhofer Institute for Intelligent Analysis and Information Systems (IAIS) and Professor of Computer Science at University of Bonn. He studied Computer Science in Bonn and Atlanta, GA, USA (M.S. degree, Georgia Institute of Technology), receiving his doctorate from University of Dortmund.
Dirk Slama is Director of Business Development at Bosch Software Innovations. Bosch SI is spearheading the Internet of Things (IoT) activities of Bosch, the global engineering group. As Conference Chair of the Bosch ConnectedWorld, Dirk helps shaping the IoT strategy of Bosch. Dirk has over 20 years experience in very large-scale application projects, system integration and Business Process Management. His international work experience includes projects for Lufthansa Systems, Boeing, AT&T, NTT DoCoMo, HBOS and others.
Scientists from IBM have unveiled the first neurosynaptic computer chip to achieve an unprecedented scale of one million programmable neurons, 256 million programmable synapses and 46 billion synaptic operations per second per watt. At 5.4 billion transistors, this fully functional and production-scale chip is currently one of the largest CMOS chips ever built, yet, while running at biological real time, it consumes a minuscule 70mW.
Cambridge UK-based start up Optalysys has stated that it is only months away from launching a prototype optical processor with the potential to deliver exascale levels of processing power on a standard-sized desktop computer. The company will demonstrate its prototype, which meets NASA Technology Readiness Level 4, in January of next year.
Big Data, it seems, is everywhere, usually characterized as a Big Problem. But researchers at Lawrence Berkeley National Laboratory are adept at accessing, sharing, moving and analyzing massive scientific datasets. At a July 14-16, 2014, workshop focused on climate science, Berkeley Lab experts shared their expertise with other scientists working with big datasets.
Enabling Innovation and Discovery through Data-Intensive High Performance Cloud and Big Data InfrastructureJuly 29, 2014 2:34 pm | by George Vacek, DataDirect Networks | Blogs | Comments
As the size and scale of life sciences datasets increases — think large-cohort longitudinal studies with multiple samples and multiple protocols — so does the challenge of storing, interpreting and analyzing this data. Researchers and data scientists are under increasing pressure to identify the most relevant and critical information within massive and messy data sets, so they can quickly make the next discovery.
In an age of “big data,” a single computer cannot always find the solution a user wants. Computational tasks must instead be distributed across a cluster of computers that analyze a massive data set together. It's how Facebook and Google mine your Web history to present you with targeted ads, and how Amazon and Netflix recommend your next favorite book or movie. But big data is about more than just marketing.
Music fans and critics know that the music of the Beatles underwent a dramatic transformation in just a few years. But, until now, there hasn’t been a scientific way to measure the progression. Computer scientists at Lawrence Technological University have developed an artificial intelligence algorithm that can analyze and compare musical styles, enabling research into their musical progression.
Ensemble forecasting is a key part of weather forecasting. Computers typically run multiple simulations using slightly different initial conditions or assumptions, and then analyze them together to try to improve forecasts. Using Japan’s K computer, researchers have succeeded in running 10,240 parallel simulations of global weather, the largest number ever performed, using data assimilation to reduce the range of uncertainties.
IBM is making high performance computing more accessible through the cloud for clients grappling with big data and other computationally intensive activities. A new option from SoftLayer will provide industry-standard InfiniBand networking technology to connect SoftLayer bare metal servers. This will enable very high data throughput speeds between systems, allowing companies to move workloads traditionally associated with HPC to the cloud.
The second ISC Big Data conference themed “From Data To Knowledge,” builds on the success of the inaugural 2013 event. A comprehensive program has been put together by the Steering Committee under the leadership of Sverre Jarp, who retired officially as the CTO of CERN openlab in March of this year.
The Cray XC30 system will be used by a nation-wide consortium of scientists called the Indian Lattice Gauge Theory Initiative (ILGTI). The group will research the properties of a phase of matter called the quark-gluon plasma, which existed when the universe was approximately a microsecond old. ILGTI also carries out research on exotic and heavy-flavor hadrons, which will be produced in hadron collider experiments.
Registration is now open for the 2014 ISC Cloud and ISC Big Data Conferences, which will be held this fall in Heidelberg, Germany. The fifth ISC Cloud Conference will take place in the Marriott Hotel from September 29 to 30, and the second ISC Big Data will be held from October 1 to 2 at the same venue.
How using CPU/GPU parallel computing is the next logical step - My work in computational mathematics is focused on developing new, paradigm-shifting ideas in numerical methods for solving mathematical models in various fields. This includes the Schrödinger equation in quantum mechanics, the elasticity model in mechanical engineering, the Navier-Stokes equation in fluid mechanics, Maxwell’s equations in electromagnetism...
IBM Announces $3B Research Initiative to Tackle Chip Grand Challenges for Cloud and Big Data SystemsJuly 9, 2014 4:58 pm | by IBM | News | Comments
IBM has announced it is investing $3 billion over the next five years in two broad research and early stage development programs to push the limits of chip technology needed to meet the emerging demands of cloud computing and Big Data systems. These investments are intended to push IBM's semiconductor innovations from today’s breakthroughs into the advanced technology leadership required for the future.
Moab HPC Suite-Enterprise Edition 8.0 (Moab 8.0) is designed to enhance Big Workflow by processing intensive simulations and big data analysis to accelerate insights. It delivers dynamic scheduling, provisioning and management of multi-step/multi-application services across HPC, cloud and big data environments. The software suite bolsters Big Workflow’s core services: unifying data center resources, optimizing the analysis process and guaranteeing services to the business.
Fully automated “deep learning” by computers greatly improves the odds of discovering particles such as the Higgs boson, beating even veteran physicists’ abilities.
To be able to use huge amounts of data, we have to understand them and before that we need to categorize them in an effective, fast and automatic manner. Two researchers have devised a type of Cluster Analysis, the ability to group data sets according to their "similarity," based on simple and powerful principles, which proved to be very efficient and capable of solving some of the most typical problems encountered in this type of analysis.
Machine learning, in which computers learn new skills by looking for patterns in training data, is the basis of most recent advances in artificial intelligence, from voice-recognition systems to self-parking cars. It’s also the technique that autonomous robots typically use to build models of their environments. That type of model-building gets complicated, however, in cases in which clusters of robots work as teams.
Jets resulting from particle collisions, like those taking place at the Large Hadron Collider (LHC) housed at CERN near Geneva, Switzerland, are quite possibly the single most important experimental signatures in high-energy physics. Virtually every final-state, high-energy particle produced will be part of a jet.
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