The project work for this year will be the (re)implementation and test of an existing clustering algorithm using one of the MPI, TBB or OpenCL technologies (possibly more than one, in some cases).

To this extent, a few papers are listed that explain parallel clustering algorithms, which the student should analyze and implement. In a few cases, there is already a parallel implementation that the student can attempt to parallelize using a different technology.

• The center for Ultra-Scale computing at Northwestern University has a page with references to parallel versions of the DBSCAN and OPTICS clustering algorithm (and PINK, a hierarchical clustering algorithm)
  http://cucis.ece.northwestern.edu/projects/Clustering/index.html
  Both MPI and OpenMP versions are provided there as source code and test data, start from the referenced papers and study the code in order to design a parallel version using TBB or OpenCL.
• The paper An Efficient MapReduce-Based Parallel Clustering Algorithm for Distributed Traffic Subarea Division
  describes a map-reduce parallelization of K-means. Starting from this structured approach, is it possible to write a parallel implementation of K-means in MPI and TBB.
• The paper G-DBSCAN describes a parallel GPU-based DBSCAN implementation.