We have implementated 7 applications on our framework. 
And they have been evaluated on both a Tesla C1060 GPU and a FX 5800 GPU.


The applications are:

1.  km, kmeans clustering.
    It clusters a large amount of points based on the distance from each cluster centroid.
    Our example only runs 1 iteration for this application.
    This is a reduction-intensive application, thus shared memory reduction objects
    are used.

2.  wc, word count.
    It counts the number of occurrences for each word in a file. 
    This is a reduction-intensive application, thus shared memory reduction objects
    are used.

3.  nbc, naive bayes classfier.
    Given two sets of observations, one with classi.cations, one without, the algorithm classifies
    the second set.
    This application has 30 distinct keys, thus tends to be a recution-intensive application.
    Shared memory reduction objects are used.

4.  knn, k-nearest neighbor search.
    Given n training samples and one unknown sample, the k-nearest neighbor classifier 
    searches the training samples by euclidean distance and finds the k nearest points to
    the unknown sample. Then it classifies the unknown sample according to the majority 
    vote of the k nearest samples.
    This application needs runtime sorting and frequent overflow handling. Shared
    memory reduction objects are used.

5.  pvc, page view count.
    Page View Count (PVC), obtains the number of distinct page views from web logs. 
    Each entry in the web log is represented as <URL, IP>, where URL is the URL of 
    the accessed page and IP is the IP address that accesses the page. This
    application involves two passes in MapReduce. The first one removes the duplicate 
    entries in the web logs, and the second one counts the number of views for each URL.
    Typically, the datasets have a lot of repeated entries, so the application is 
    reduction-intensive in nature. Shared memory reduction objects are used.

6.  mm, matrix multiplication.
    No reduction is involved, thus only a global reduction object is used.

7.  pca, principle component analysis.
    No reduction is involved, thus only a global reduction object is used.

For methods of compiling and running these applications, please
refer to the README file in each example folder.

Welcome to report bugs to us and discuss other related problems with us. 
Linchuan Chen, Email: chenlinc@cse.ohio-state.edu