Implementation of K-means on the Cell Broadband Engine Filip imek - PowerPoint PPT Presentation

IBM - CVUT Student Research Projects Implementation of K-means on the Cell Broadband Engine Filip Šimek (simekf1@fel.cvut.cz)

About K-means • a data clustering algorithm • places k centroids in an n-dimensional space so that intra-cluster variance (sum of distances between points ⇩ and their nearest centroid) is minimal • large number of points gives space for parallelization IBM - CVUT Student Research Projects 2

K-means application example – reducing number of colors of an image Truecolor original 16 colors found using K-means IBM - CVUT Student Research Projects 3

Implementation st step - scalar implementation 1 • the program runs on the PPU of Cell Broadband Engine with no parallelism • easy to implement or port from another scalar implementation • does not take full advantage of CBE performance IBM - CVUT Student Research Projects 4

Implementation nd step - vector implementation 2 • the PPU distributes work among one or more SPUs, which use SIMD instructions • a) multiple serial calls to one SPU thread, occupies only one SPE, the others can perform another operations in a pipeline • b) multiple SPUs running in parallel on multiple data IBM - CVUT Student Research Projects 5

What's done • scalar implementation of the algorithm running on the PPU • operation on n-dimensional data • organisation of data into vectors • PPU loop creating a single SPE thread as part of the implementation of step 2a) IBM - CVUT Student Research Projects 6

Future plans • code running on the SPU • implementation of step 2b) • possibility to separate learning and classification • call K-means multiple times to reduce error • use DMA effectively • implement a unified interface to integrate this code into a larger toolkit IBM - CVUT Student Research Projects 7