Groups can be either one or two students, but if two their contributions should be balanced.

Students will make short presentations of group project results in class, starting on Monday 28 April 2003, or earlier if any group is ready. Recall, that each student must give a least one presentation, but shared presentations may be all right.

CAUTION: Projects should have sufficient work to effective utilize the Platinum Cluster (pt.ncsa.uiuc.edu) and the Argo Cluster (argo.cc.uic.edu) with MPI, but should not be so time consuming as to severely affect the performance of other users. Write a group (1 < group < 2) with good load balancing among the group members) report that is a short paper (8 or 15 or so pages plus appendices) as if for publication, i.e., with

1. abstract (short description of problem and results)
2. executive summary (give an itemized brief summary of your paper)
3. introduction (motivate your problem for the class, citing prior work)
4. problem or method
5. results and discussion (should include theoretical explanations of interesting results and graphs; explain results whether good or bad)
6. conclusions (brief, emphasizing your main results)
7. acknowledgements (give thanks to others that helped you and to the National Center for Supercomputing Applications (NCSA) of use of the Platinum IA32 Cluster and the UIC ACCC Argo Beowulf Cluster)
8. references (list articles, books, guides, web pages and other documents that you used as sources)
9. appendices: (code used, compiler options, job scripts or command line execution format, sample output, and supporting timings.

WARNING: If you use test or sample floating point arrays in your project, make sure they are genuine and random floating point, i.e., do not use trivial integers or numbers with patterns. Consult the class local user's guide for how to run a scalar job to use as a reference measurement. Also, if your project is similar to the one you did on the PSC TSC, then you may want to give an extensive comparison to your TCS Project. Also, any other small scale local cluster such as the EVL or BIOE clusters can be substituted for Argo.

Platinum/Argo Project Suggestions

1. Own Project: The Best Choice. A NCSA Platinum - ACCC Argo with MPI project or your own design, such as optimization of some method connected with your thesis research area, graphical visualization, another course, or some interesting science-engineering area.
2. Statistics Project. Generate suitable sets of random numbers (make sure they are floating point), each with a different sample size N. The function `ranf' is a very good random number generator (RNG), but check it out yourself. See the Class TCS Local Guide or Platinum/Argo man pages. Describe how you tested the randomness of your data, e.g., test for a uniform random distribution. For each set, compute basic statistics, like mean, variance and Chi-Square test in as efficient vector manner as possible (i.e., make use of the extended Fortran90 intrinsic sum function `sum' on the Cray. Plot T versus N and T versus p. Estimate or compute and plot the Amdahl vector fraction as a function of N. Compare speedups and efficiencies relative to N. Is the Amdahl law operative as the problem size N becomes large? Develop your own performance model that is appropriate for the behavior of the timing data with number of processors p, sample size N and Chi-Square bin size Nb. Does your performance model account for deviations in Amdahl's law?
3. Iteration Methods. Make a comparison of the performance of Jacobi and Gauss-Seidel methods for Elliptic Partial Differential equations. Gauss-Seidel is better for serial computers, but what about parallel and vector computers? (See Ortega, "Intro. Parallel and Vector Solution of Linear Systems," 1988, or the newer Golub and Ortega "Scientific Computing: An Introduction with Parallel Computing," 1993, and related papers.) Revise the code for an arbitrary number of block/slice processes with a refined decomposition. See Class Sample Laplace-MPI C Code.
4. Two-Dimensional Block Decompositions. Implement the Jacobi 2D block decomposition sketched in class using the MPI_Cart functions and other features with blocking or non-blocking communication. Find other applications for the techniques.
5. Numerical Parabolic Differential Equations. Revised the Jacobi iteration block decomposition for marching in time instead of approximate iterations for the elliptic Laplace problem. Be sure that the parabolic mesh ratio is sufficiently small, i.e., Diffusion*delta(t)/delta{x)² < 0.5. If the drift term is significant, upwinding with forward/backward differences will be needed.
6. Two-Dimensional Block Decompositions. Implemment the Jacobi 2D block decomposition sketched in class using the MPI_Cart functions and other features with blocking or non-blocking communication.
7. Test whether higher or lower levels of optimization give higher performance. For instance, does the command `gcc -O[n] -[other_optimization_options]... cpgm.c' lead to faster executables for some values of Option Level `[n]' (e.g., -O, -O0, -O1, -O2, -O3, ... on platinum) for matrix multiplication or some other application. Use `man gcc' on pt.ncsa.uiuc.edu. Else compare the performance of other compilers like Intel's `icc' (use `icc -help' or `icpc -help'); or the Portland Group (PGI)'s C `pgcc' or `pgCC' (use `pgcc -help'). This suggestion can be combined with other suggestions for testing.
8. Compare Performance of MPI Functions/Subroutines. For instance, compare the Collective Communication routine MPI_Bcast with the Blocking Point to Point Communication routine MPI_Send along with MPI_Recv, and with the Nonblocking Point to Point Communication routine MPI_Isend along with MPI_Irecv. Use MPI_Wtime to measure performance times. (Note shmem is the Cray native message passing library. See `man shmem'.) Compare the Send and Recv functions with the sequence non-blocking functions Irec, ISend and Wait for MPI to see if computation and communication can be sufficiently overlapped to improve performance.
9. Advanced MPI Features and Libraries. Implement some of the advanced MPI functions with applications suggested in Chapters 5-7 in Using MPI: Portable Parallel Programming with the Message-Passing Interface by William Gropp, Ewing Lusk, and Anthony Skjellum. or in Peter Pacheco's Book or in Wilkinson and Allen's book.
10. Extensive TCS and Platinum/Argo Performance Comparison. Take some application and make a comparison between optimized performance on the PSC TCS and the Platinum/Argo pair with MPI.

Project Resources:

• NCSA Platinum IA-32 Linux Cluster Page
  • NCSA IA-32 Linux Cluster Technical Summary
  • Compile Programming Environment on NCSA IA32 and IA64 Linux Clusters.
  • Running Job Scripts and Interactive Jobs on NCSA IA32 and IA64 Linux Clusters.
  • MPICH -- A Portable Implementation of MPI (Flavor on Platinum).
  • Timing on NCSA Linux Clusters.
  • NCSA File Systems.
  • NCSA's Platinum Architecture Configuration.

• ACCC Argo Cluster Page

• Class MPI Help Page. Use the MPI_Wtime wall timer for measuring performance times, unless you can find a better timer.

• Class MPI Example Page. See Sample Job Scripts, Codes and Some Sample Output.

• Getting Started With MPI: A Message Passing Interface for Parallel Programming: An Introduction to MPI at SDSC.

• Class TCS Local Guide for complete guide information.

• Using MPI: Portable Parallel Programming with the Message-Passing Interface by William Gropp, Ewing Lusk, and Anthony Skjellum. MIT Press, 371 pages, 1999. Book link also gives link for obtaining the book's example code and errata.

• "Parallel Programming with MPI", by Peter S. Pacheco, Morgan Kaufmann Publishers, 1997. (See other Information includingMPI C Codes and MPI Fortran Codes).

• MPI Reference Card (Big Card: 13 Compact Pages.