ECE 563 Projects

Due dates (Fridays, 11:59 PM):
  • Project proposal draft, Week 2 (Jan 23)
  • Final Project proposal, Week 3 (Jan 30)
  • First intermediate report, Week 7 (Feb 27)
  • Second intermediate report, Week 11 (Apr 3)
  • Final Project report, Last week of class ( Friday, May 1, 11:59pm)

    Submission format

    Reports must be in PDF format. Combine all files into a tar file and email to eigenman@purdue.edu

    File name: your_name(s)-ece563-VERSION.tar
    VERSION = proposal | report1 | report2 | finalreport

    Include original (sequential) and current parallel program version in submission. Include all files needed for making and executing the program.

    Alternatively, you can submit a URL, at which you place all these files. All files "submitted" must remain unchanged at this site after the submission date. When your report is ready, send email to eigenman@purdue.edu stating that fact.

    Project descriptions should include

    In your final report, mention all of the following information. You may change the order, except for the first section (summary). If you don't have information to report, say "nothing to report" or "unknown".
  • Summary, including the goals you set for the project, a summary of the steps you took in the project and milestones you reached after each step.
  • Description of the application and algorithm(s)
  • Program description (language, #lines, #subroutines)
  • Names of developers and users of the code, if known.
  • Bibliographic references and web links to documents and papers that describe the application and the program, where available.
  • "make" information: machine used; compiler and compiler flags; libraries used, if any.
  • Data sets: describe input data in terms of their meaning from an application angle and their properties (size, format)
  • Execution time(s) of the original program version. Indicate the machines and data sets used.
  • Memory footprint(s).
  • Parallel programming models used.
  • "Performance diaries:" describe all steps of applied transformations and settings of environment parameters as well as the execution time before and after each step. This should correspond to the project roadmap described at the beginning of the report.
  • Discussion of the results. Include a comparison with the estimates made at the beginning of your project and a discussion of discrepancies.

    Projects

  • 1. Philip Livengood and Karl Herb: Parallelizing the Computation of Kernel Density Estimation
  • 2. Derrick Kearney: Discovering Transposable Elements in DNA
  • 3. Dheya Mustafa: Parallelizing Livermore Loops
  • 4. Adam Horton and Michael Sorensen: Parallelize the FLAC encoding program
  • 5. Syed Ali Raza Jafri: Parallelizing Computation for Tomographic Reconstruction
  • 6. Matt Kirleis and Gregory Aaron Wilkin : Parallelize K-means clustering
  • 7. J.A. Pienaar and David Collins: Music Classification using Self-Organizing Maps
  • 8. Heran Quan and Jiachen Xue : Parallelizing Advanced Audio Encoder
  • 9. Chirag Dave : Compiler-Aided Automatic Tuning of Loop Parallelization on Shared-Memory Machines
  • 10. Okwan Kwon : Performance analysis on OpenMP/MPI applications on a shared memory system
  • 11. Fahed Jubair : Singular Value Decomposition
  • 12. Olga Krachina : Jet Fuel Simulation on GPGPU
  • 13. Juan Vasquez and Christianna Leonhardt : Parallelizing the 458.Sjeng SPEC2006 Benchmark
  • 14. Bo Qiu and Chenguang Sun :
  • 15. Duo Chen and Jianfang Zhu : Electromagnetics-Based Analysis of Large-Scale Integrated Circuit and Package Problems