spnn

description

• spnn: simple parallelized neural network.
• A comparison of fully connected network (forward and backward propagation) implementations.
• Implementations are listed below,
  1. CPU single thread.
  2. CPU multiple threads using openmp.
  3. GPU single thread using cuda.
  4. GPU multiple threads using cuda.
  5. OpenBLAS.
• The task selected is digit classification on MNIST data.

code

• Code is written in C++/CUDA.
• OpenMP variant uses openmp library.
• OpenBLAS variant uses openblas library.
• include/ contains headers.
• src/ contains all variant implementations.
• data/ contains MNIST data.
• proposal.pdf contains the project proposal.
• presentation.pdf contains the presentation given at the end of the project.
• report.pdf contains details, experiments and analysis.
• Makefile is used to make target executables.

documentation

• The documentation of the code is itself.

usage

• Open a terminal in the directory containing Makefile.
• Use make all to build all targets.
• The targets are listed as follows,
  1. cpu_serial.out
  2. cuda_parallel.out
  3. openmp.out
  4. openblas.out
  5. cuda_serial.out
• To build a specific target use make <target-name>.
• To remove all targets use make clean.
• Use ./<target-name> to run a target.

demonstration

• Accuracy vs epochs for the fully connected network irrepective of implementation.

• Implementations comparision for a specfic model.

• Time taken vs Params size for different implementaions. Observe the GPU parallelized variant curve is flat at almost 0.

roadmap

• Things to consider during analysis
  • correctness (> 10% accuracy)
  • repeatablity (nothing fancy)
  • memory check (no mem leaks or other bad stuff using valgrind —tool=memcheck)
• Initialization done uniformly in -1 to 1
• Layers are numbered from 0 i.e. first hidden layer is layer 1
• Control size of name field
• Impl loss function
• Remove memleaks from step_train
• Batch gradient descent: fix loss decrement and check backprop
• Normalize
• Get MNIST data
• Profile
• Remove data loading from time taken