Neural network in CUDA/C++

• Description
• Usage

Description

This is an implementation of a neural net, completely from scratch, in CUDA/C++.

Usage

The code is by no means efficient and is meant as an introduction to CUDA only. Here is an overview of the various classes and functions:

Everything is implemented in both pure C++ (under CPU/) and CUDA/C++ (under GPU/). The syntax remains virtually identical, and there are only two points to bear in mind when switching between C++ and CUDA/C++:

1. C++ and CUDA/C++ modules end with the suffixes CPU and GPU respectively.
2. Don't forget to allocate and destroy CUDA arrays via cudaMallocManaged and cudaFree

• linear.h/Linear_SUFFIX:

  • Initialization:

    Required arguments:

    • _bs (int): Batch size.
    • _n_in (int): Number of input features.
    • _n_out (int): Number of output features.

    Optional arguments:

    • _lr (float): Learning rate.

  • forward: Runs a linear forward pass.

    Required arguments:

    • _inp (float*): Pointer to the input data.
    • _out (float*): Pointer for storing the output data.

  • update: Updates the weights and biases.

  • backward: Performs a backward pass, storing the gradients in _inp.

• relu.h/ReLU_SUFFIX:

  • Initialization:

    Required argument:

    • _sz_out (int): The number of input/output elements.

  • forward, backward: Like Linear_SUFFIX but for ReLU.

• mse.h/MSE_SUFFIX:

  • Initialization: Like ReLU.

  • forward: Dummy method for compatibility with the other modules and performing backpropagation; does not actually calculate the loss.

    Required arguments:

    • _inp (float*): Pointer to the predictions.
    • _out (float*): Pointer to the target values.

  • _forward: Calculates the MSE. This method is solely for calculating the loss and cannot be used during backpropagation.

    Required arguments: Like MSE_SUFFIX but _out must have an extra element for storing the loss.

  • backward: Performs a backward pass, storing the gradients in _inp.

• sequential.h/Sequential_SUFFIX:

  • Initialization:

    Required arguments:

    • layers (std::vector<Module*>): Layers to be chained together.

  • forward: Cascades the modules in layers.

    Required arguments:

    • inp (float*): Pointer to the input data.
    • out (float*): Dummy argument, only for compatibility with the other forward methods and doesn't get used. The output is accesible via the last layer's out attribute.

  • update: Updates every module in layers.

• train_SUFFIX: Trains a network with gradient descent.

  Required arguments:

  • seq (Sequential_SUFFIX): Sequential module to train.
  • inp (float*): Pointer to the input data.
  • targ (float*): Pointer to the target data.
  • bs (int): Batch size.
  • n_in (int): Number of input features.
  • n_epochs (int): Number of epochs.

For end-to-end training with speed benchmarks, please run main.cpp or main.cu for the CPU and GPU respectively.