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By Ravi, Ma, Chiu, & Agrawal Presented by Julian Bui

“Compiler and Runtime Support for Enabling Generalized Reduction Computations on Heterogeneous Parallel Computations”. By Ravi, Ma, Chiu, & Agrawal Presented by Julian Bui. Outline. Goals The focus problems Key ideas Results. Goals. Programmability Performance

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By Ravi, Ma, Chiu, & Agrawal Presented by Julian Bui

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  1. “Compiler and Runtime Support for Enabling Generalized Reduction Computations on Heterogeneous Parallel Computations” By Ravi, Ma, Chiu, & Agrawal Presented by Julian Bui

  2. Outline • Goals • The focus problems • Key ideas • Results

  3. Goals • Programmability • Performance • Effective work distribution

  4. The Focus Problems • K-Means Clustering • Principal Components Analysis

  5. K-means clustering - 1

  6. K-means clustering - 2

  7. K-means clustering - 3

  8. K-means clustering - 4

  9. K-Means Clustering • K-Means Clustering 1. Randomly guess k cluster center locations 2. Each datapoint figures out which cluster center it's closest to 3. Center now "owns" a set of points 4. Cluster calculates the actual center of the points it owns 5. Cluster's center point now becomes the actual center point 6. Repeat steps 2-5 until the cluster's datset doesn't change between iterations

  10. K-Means Clustering Centroid of the cluster For all clusters

  11. Principal Component Analysis (PCA) • Goals • Dimensionality Reduction • INPUT: set of M-dimensional pts • OUTPUT: set of D-dimensional pts where D << M • Extract patterns in the data, machine learning • Transforms possibly correlated data into a smaller number of uncorrelated data • Principal components account for the greatest possible statistical variability

  12. PCA • Principal components are found by extracting eigenvectors from the covariance matrix of the data

  13. PCA - Simple Example

  14. PCA – Facial Recognition

  15. So how does this work apply to those problems? • Code generator input: • Reduction function • Variable list (data to be computed) • Code Generator output • Host functions • Kernel code

  16. System Architecture

  17. Work Distribution • Work Sharing vs. Work Stealing • Uniform vs. Non-uniform chunk sizes

  18. Experiments Machine: AMD Opteron 8350 w/ 8 cores and 16 GB of main memory, GeForce9800 GTX w/ 512MB memory K-Means: K = 125, 6.4GB file, 100M 3D points PCA: 8.5GB data set, covariance matrix width of 64

  19. GPU Chunk Size & Num. Threads vs. Performance

  20. K-Means, Hetero., Uni vs. Non-uniform chunk sizes

  21. PCA, Hetero., Uni vs. Non-uniform chunk sizes

  22. Idle Time

  23. Work Distribution – K-Means

  24. Work Distribution – PCA

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