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Viewing: DATS 6402 : High Performance Computing and Parallel Computing

Last approved: Mon, 16 May 2016 08:48:19 GMT

Last edit: Wed, 06 Apr 2016 17:59:43 GMT

Catalog Pages referencing this course
Programs referencing this course
Columbian College of Arts and Sciences
Data Science (DATS)
DATS
6402
High Performance Computing and Parallel Computing
HPC and Parallel Computing
Fall 2015
3
Course Type
Lecture
Default Grading Method
Letter Grade
candidates for the MS or graduate certificate in data science; permission of the instructor may be substituted
No
No
DATS 6101, DATS 6102, and DATS 6103
Corequisites

15
Glen MacLachlan
Frequency of Offering

Term(s) Offered

Are there Course Equivalents?
No
 
No
Fee Type


No


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.
Students will gain practical skills typical of those of data scientists and gain experience applying the skills to real data.
Course Attribute

This is to be a second-year course in the Master of Science in Data Science program.
Key: 9857