April 10, 2013
Data scientists and those adept at scientific computing are numerous, but not quite numerous enough to meet the demands of the computing marketplace.
Further, as science progresses to more complex and data-intensive questions, such as researching the beginning of the universe or getting more in-depth genomic results, it becomes imperative for more scientists and researchers to learn these HPC techniques to cut down on query times.
Randall J. Leveque, Professor of Applied Mathematics and Adjunct Professor of Mathematics at the University of Washington in Seattle, will be conducting a free course that brings the principles of parallelism in high performance computers to the people who are running applications on multi-processor laptops and desktops or on cloud services.
Leveque’s principle is that a person’s time is more valuable than a computer’s time. As such, any research query or scientific question that you can parallelize on your computer’s multiple processors or via a cloud server is a benefit. However, a fast program is of course useless if that program produces inaccurate results.
“The goal is not to teach the most advanced techniques with supercomputers, but rather techniques that you can use immediately on your own laptop, desktop, cluster, or even in the cloud,” Leveque remarked in his introductory video below.
The ten week course, which will require ten to twelve work hours per week, will cover both serial and parallel computing and the computing languages that dictate them, such as Fortran 90, OpenMP, MPI, and Python. The full list of what it is to be covered is below:
The course, which again is free to participate in, is scheduled to start on May 1st.
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