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May 16, 2016 by Larry Medsker (lrm)
DATS 6402 : High Performance Computing and Parallel Computing
Mon, 16 May 2016 08:48:19 GMT
Wed, 06 Apr 2016 17:59:43 GMT
Catalog Pages referencing this course
Data Science (DATS)
Programs referencing this course
DS-MS: Data Science
Columbian College of Arts and Sciences
Data Science (DATS)
Long Course Title
High Performance Computing and Parallel Computing
Short Course Title
HPC and Parallel Computing
Number of Credits
Default Grading Method
candidates for the MS or graduate certificate in data science; permission of the instructor may be substituted
Repeatable for Credit?
DATS 6101, DATS 6102, and DATS 6103
Frequency of Offering
Are there Course Equivalents?
Are Fees Applicable?
Explanation and Description of Fees
Are Additional Resources Required?
Explanation of Additional Resources
Justification for Additional Resources
Describe any Sources of Additional Funding
This course is a practical approach to high performance computing specifically for the data science professional. It covers topics such as parallel architectures and software systems, and parallel programming.
Students should be able to explain fundamental ideas and concepts of high performance computing hardware and software techniques, demonstrate the knowledge of where HPC and parallel computing can be applied to problems in data science, and be able to use tools such as parallel programming languages, libraries, and OpenCL to solve typical problems addressed in HPC and parallel computing.
Uploaded a Course Syllabus
DATS6402 DS_High Performance Computing_syllabus_Spring_2016.pdf
Explanation of how the course differs from similar GW courses
Students will gain practical skills typical of those of data scientists and gain experience applying the skills to real data.
This is to be a second-year course in the Master of Science in Data Science program.
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