CSE708: Programming Massively Parallel Computers

This course is offered remotely via Zoom. Attendance is required. Please see the LMS (UBLearns) to find the Zoom information. The course is listed as an HE course, so will satisfy the requirement for a course to be taken on-campus/in person.

This course is designed to be a follow on to CSE633, though it is not required. For those students who have a good command of MPI, they may choose to use OpenMP or CUDA. For those who are working with large-scale systems for the first time, it is typical that such students will use MPI. Please discuss your choice of a platform either in class or with Prof Miller.

The focus of this course is experimental (hands-on) parallel computing. Each student is responsible for a semester-long project. Grading will be based on the project, as well as two formal talks, using presentation software (e.g., PowerPoint), that covers your project, including a definition and justification of the problem, sequential and parallel solution strategies, and a significant set of running times on large parallel systems that allow for an analysis and explanation of Amdahl's and Gustafson's speedups. In particular, the first talk provides a brief explanation of the proposed project, goals, expectations, and a timeline of the work to be performed. The second talk provides a summary of accomplishments. Students are encouraged to look at the final talks from previous semesters, available below. Note that a successfully completed project satisfies the requirement for a project in the M.S. program. (The student who completes the project successfully is responsible for filling out the proper paperwork and presenting it to Dr. Miller for a signature.) NB: There will be a cap on the number of students allowed to enroll in the course, so that those who are enrolled will have a full experience and educational opportunity.

All Seminars in CSE are graded S/U. Grading is subjective, based on the quality of the following:

• Class Attendance and Participation
• Project chosen with respect to key parameters of projects, as discussed in class (ability to demonstrate both speedup and scaled speedup))
• Midterm Presentation
• Final Presentation

Presentations:

• Knapsack Algorithm, Shrishti Karkera.
• Disease Spread Simulation - OpenMP, Sumanth Reddy Dasi.
• Parallel Breadth-First Search Using MPI, Sandeep Kunusoth.
• Longest Common Subsequence, Saema Nadim.
• Min Coin Change, Sarthak Jain.
• Subset Sum Count (Using OpenMP in C), Kunal Chand.
• Parallel Breadth First Search Using MPI, Venkata Bala Vamsi.
• Parallel Stock Buy and Sell, Dheeraj Sai Gogineni.
• Sieve Parallel Algorithm, Shivangi Mishra.
• KMP Parallel Algorithm for Pattern Matching (Open MP), Rajesh Bammidi.
• Parallel Matrix Multiplication, Ashutosh Dubey.
• Parallelization of Prim's Algorithm to find the MST, Sarath Chandra Reddy Rayapu.
• Working with Prime Numbers, Kiran Radhakrishnan.
• MST using Kruskal's Algorithm, Sai Kiran Mundra.
• Parallelizing Page Rank, Venkata Hemanth Athota.
• Smith Waterman Algorithm, Sai Ram Gurram.
• Parallelization of Floyd Warshall Algorithm, Yashwanth Krishna Porandla.