Course Announcement, Spring 2017

      Revised Course Syllabus and Schedule


This course is being offered as a 4-unit course in Spring 2017. The course will cover additional topics. Also, the course will include a large software project. The current focus of the course is on scientific computing (matrix computations, FFT, etc.) and traditional platforms (multi core, GPU, and cluster). In Spring 2017, emerging programming models and platforms (ex. Cloud, Map Reduce, SPARK, etc.) will also be emphasized. Big Data and Data Science will be covered in addition to traditional scientific computing applications. Lecture/Lab sessions will cover applications in Big Data and programming models and platforms. Throughout the lectures, we will augment additional examples from machine learning kernels and Data Mining and their applications. The course will also consist of a large software project based on topics from scientific computing, data science, graph analytics, big data, etc. Students can work in teams for the course project.


Project timeline:

a. Week 5-7: Identify team members and project topic

b. Week 8: Project proposal due

c. Week 13-14: Presentation in lab session

d. Week 15: Project report due


Grading breakdown of the course project:

a. Proposal: 25%

b. Final presentation: 25%

c. Final report: 50%


Please also register for 1 unit of Directed Research:

Grad Students register for EE 590 (1) and undergraduates EE 490 (1) both with Prof. Prasanna. Go to to sign up for Directed Research.

If you have any questions, please contact professor Prasanna at