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Multi-Layer Perceptron On A GPU
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  1. Multi-Layer Perceptron On A GPU Scott Finley ECE 539 Fall 2008 UW-Madison

  2. General Purpose GPU • Modern GPUs are have 100s of “stream processors” • Can now be used for non-graphics computing • nVida CUDA (used for this project) • openCL

  3. Three MLP Implementations • Basic Linear Algebra Subprograms (BLAS) • CPU-Only • nVidia’scuBLAS library • No explicit GPU use, library uses GPU “under the hood” • Lots of copies of data from CPU to GPU • cuBLAS with CUDA • Same cuBLAS use as above, non-BLAS operations done with CUDA.

  4. Classifying Forestry Data • Data from US forestry service • Large feature vectors: 54 • Large number of training samples: 500 per epoch • Two hidden layers • Number of neurons per layer varied

  5. Small, Contrived Data Set

  6. Cross-Platform GUI

  7. Conclusion • GPU is very powerful parallel processor • Up to two orders of magnitude improvement possible • Much more effective for large comutations • Many improvements possible • CUDA-only version needed