We may not yet have the flying cars we’ve hoped for, but automobiles have progressed in other directions, even with all four wheels still firmly on the ground. Touch screens, GPS and voice control are among a few features new cars are sporting. Connectivity is the next big step, bringing enhanced user control and situational awareness.
NVIDIA is ready to push forward in the automotive industry. The company has opened a new center in Ann Arbor, MI dedicated to advancing automotive tech. In the remnants of the rust belt, silicon is slowly taking the place of steel and automotive manufacturing. Continue reading
The University of Oxford and a consortium of UK-based academic institutions have deployed that country’s most powerful GPU-accelerated supercomputer. Named Emerald, the system was unveiled at the new Center for Innovation in High Performance Computing at the Science and Technology Facilities Council’s STFC Rutherford Appleton Laboratory in Didcot. Continue reading
I have a full plate of meetings at Supercomputing 2011 this year. Below are some of the points of interest I am learning about along the way that I wanted to share.
Yesterday I attended the 7:30 breakfast meeting for Platform Computing’s MapReduce. This is a policy-driven workload manager and scheduler that handles mixed types of workloads running on the same cluster. It uses an open architecture to support multiple applications for jobs built with Hadoop MapReduce technology. Applications include Pig, Hive, Java, Oozie, Cumbo, and natively written Java MapReduce programs.
The scheduler can give the high-performance computing (HPC) manager options to schedule job submissions with a Fairshare Scheduler, Preemptive Scheduler, Threshold-based Scheduler or Task Scheduler. It also helps to work with resource draining.
MapReduce sends the application to a Job Controller, which decides what data should be mapped to an input folder and what mapped tasks should go to the local storage. It can split data so only that data that needs to be in the compute schedule goes to the CPU. It also uses Resource Groups to move data between local workstations.
Platform Computing’s MapReduce can allow clusters to be grouped into one large resource. Platform is used as a massive scheduler. The user has control of the job not just when it is in the queue, but while the job is running. Continue reading