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’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.

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

6. Small, Contrived Data Set

7. Cross-Platform GUI

8. 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