Multi-Layer Perceptron On A GPU

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Presentation transcript:

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

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

Three MLP Implementations Basic Linear Algebra Subprograms (BLAS) CPU-Only nVidia’s cuBLAS 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.

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

Small, Contrived Data Set

Cross-Platform GUI

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