Health care analytics is an emerging application area that promises to help cut costs and provide better patient outcomes. To reach that goal though requires sophisticated software that can mimic some of the intelligence of real live physicians. In Sweden, researchers are attempting to do just that by building a model of heart-transplant recipients and donors to improve survival times.
Languages like R and MATLAB, which were once unofficially reserved for technical computing domains are slowly finding their way into enterprises due to the rise in demand for large-scale data analytics. This demand is coupled with recent announcements about cloud-based ways to use these languages, opening new doors to access and use.
The MATLAB on the TeraGrid initiative, deployed last year at SC09, is still going strong.
Last year Cornell University and Purdue University received funding from the National Science Foundation to undertake their MATLAB on the TeraGrid project. Since its inception a number of researchers have been making use of the resource and Cornell’s Center for Advanced Computing is demonstrating that the resource might have a permanent place in the TeraGrid resource provider collection in the future.
MATLAB users with a taste for GPU computing now have a perfect reason to move up to the latest version. Release R2010b adds native GPGPU support that allows user to harness NVIDIA graphics processors for engineering and scientific computing. The new capability is provided within the Parallel Computing Toolbox and Distributed Computing Server.
New generation of HPC programmers embracing higher level languages.
A rundown of the most popular math tools.
The nascent GPGPU computing world received another boost today with the commercial release of Jacket 1.0, a GPU engine designed to accelerate computing and visualization for MATLAB users.
Scientific computing is quickly moving to parallel platforms and most software vendors are following suit. The MathWorks, which started parallelizing MATLAB and the company’s other numerical and scientific computing products four years ago, is now setting its sights on cluster and grid computing — and even computing in the cloud.