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GPU-based efficient data parallel formulation of the k-Nearest Neighbor (kNN) search problem which is a popular method for classifying objects in several fields of research, such as- pattern recognition, machine learning, bioinformatics etc.

How to run:

1. Unzip
2. Goto Default Folder
3. Open subdir.mk in a text editor and set boost library path (third line from the bottom)
4. run make
5. run disgpu1 Filename (e.g., ./disgpu TestData.bio.csv)

Maintained by Ahmed Shamsul Arefin (ahmed.arefin@uon.edu.au)

Copyright (C) 2011 Centre for Bioinformatics, Biomarker Discovery and Information-Based Medicine
http://www.newcastle.edu.au/research-centre/cibm/