Department of Computer Science	University of San Francisco

You should speak to me or send me email regarding your proposed project by Monday, April 18.

Projects can be team efforts. However, no one project can have more than two students working on it.

Duplicate projects won't be allowed, and projects will be assigned on a first-come, first-served basis. The first student (or team of students) to propose a project will be assigned the project. So let me know about your project as soon as possible.

Here's a list of ideas for possible projects. You don't have to choose a project from this list, but you must get your project approved.

1. Implement distributed memory preconditioned conjugate gradients for sparse matrices. Adrian.
2. Implement Gaussian elimination with submatrix partitioning and MPI. Compare pipelined and non-pipelined implementations. Compare block, cyclic, and block-cyclic distributions. Compare your solver with the ScaLAPACK solver. Srujana.
3. Implement a distributed memory preconditioned GMRES solver for sparse linear systems. Compare your solver with the PETSc GMRES solver.
4. Implement serial and distributed-memory parallel versions of Strassen's algorithm for matrix multiplication. Compare the performance of your parallel implementation to parallel matrix multiplication available in ScaLAPACK. Yumeng and Ludan.
5. Implement WaTor using MPI and dynamic load balancing. Discuss performance. Krichaporn and Pakkapon.
6. Write a distributed memory parallel program for repartitioning a distributed graph so that the weight of the edge-set that's cut is minimized. Include code for redistributing the graph. Compare the performance of distributed sparse matrix-vector multiplication before and after the redistribution. Include the cost of the redistribution.
7. Parallel sorting. Implement a variety of distributed memory parallel sorting algorithms. Discuss their relative performance. Puneet and Bashar.
8. Write an MPI program that uses asynchronous iteration with conjugate gradients to solve sparse systems of equations. Compare the performance of your solver to a ``conventional'' CG solver. Xintian and Lin.
9. Explore latency and bandwidth of shared memory communication between threads when the threads are assigned to different cores of the same processor and when the threads are assigned to cores on different processors. How does the performance of AMD systems (grolsch, penguin,chimay) compare to the performance of Intel systems (spaten, stella)? How does the addition of communicating threads affect performance? Kai and Chao.
10. Install one or more of the software transactional memory systems on grolsch and spaten or stella. Implement one or more of the dwarves with the transactional memory software, Pthreads, and OpenMP. How does the performance of the systems compare? Neal and Leo.
11. Implement a significant parallel algorithm on the cluster using OpenMP for intranode communication and MPI for internode communication. Also implement the algorithm using only MPI. How does the performance of the mixed implementation compare with the MPI-only implementation? How did the difficulty of implementation of the two systems compare? Shan and Bobby.
12. Implement LU factorization and back substitution on a GPU. How does its performance compare to a serial direct solver? Simao and Chen.
13. Implement a sorting algorithm using the GPU and using Pthreads or OpenMP. How does their performance compare to a good serial sorting algorithm? Calvin and Felix.