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--- magistraleinformaticanetworking:spd:progetto15.16 [31/07/2016 alle 03:10 (8 anni fa)] – creata Massimo Coppola
+++ magistraleinformaticanetworking:spd:progetto15.16 [31/07/2016 alle 03:26 (8 anni fa)] (versione attuale) – Massimo Coppola
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 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.
+====What is expected from the project works====
+The proposed implementation
+  * must have a well recognizable, explicit parallel structure
+  * must have an (expected) parallel performance model
+  * must be verified with experiments
+  * must be accompanied with a short report explaining the problem tackled, the choices made, the performance model; the report shall contain the experments results and analysis.
+The student
+  * should be able to understand the parallel algorithm and implement/port it with the choosen support
+  * can exploit libraries and data structures as needed
+  * should be able to verify the program and its model with experimental data, identifying performance and scalability issues
+Please contact the teacher :
+  * In case you are stuck or miss datasets to test your program with.
+  * If you need computing resources for running the experiments.
+  * If you have a different proposal for you project
+//As stated many times before, obtaining the fastest program is not the absolute goal in this case. Being able to use a technology and understand its issues is as much important. Try to keep the balance.//
+====Links to algorithms and papers====
   * 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.