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GPU Computing Assignment 3

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CS 179: GPU Computing
Labs 5-6: Introduction to Machine Learning and cuDNN

Submission:
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Instead of emailing us the solution, put a zip file in your home directory
on Titan, in the format:

lab5_2019_submission.zip

and

lab6_2019_submission.zip

Your submission for each lab should be a single archive file (.zip)
with your README file and all code.

Resources:
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You will be using the MNIST dataset of handwritten digits. This can be found on
Titan in the directory /srv/cs179_mnist, though it is included as a default
option for you. If you are running this on your own machine, you can download
the dataset yourself from http://yann.lecun.com/exdb/mnist/ and just put all 4
of the files into a single directory, which you should specify using the –dir
command line argument when running the executable.

To set up cuDNN on your own, create an NVIDIA developer account and visit
https://developer.nvidia.com/cudnn. Then, download the most recent release
corresponding to your version of CUDA (you may have to check archived releases
for versions of CUDA older than 9.0), download the archive for your OS, and
follow the installation instructions provided.

Usage:
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The executable for lab 5 will be bin/dense-neuralnet. For lab 6, it will be
bin/conv-neuralnet. To call them, you should use the command
bin/<type>-neuralnet [–dir <mnist_directory>] [–act <“relu” or “tanh”>]
The –dir argument sets the directory from which the MNIST data will be read,
while the –act argument sets the activation that this neural network will use
for intermediate layers (“tanh” or “relu”). The default activation is relu.

Overview:
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In labs 5 and 6, you will be writing a number of cuBLAS and cuDNN calls (as
well as a couple of kernels) to fill out the missing parts of this neural
network library. These calls will implement the core functionality of the
forward pass and backward pass for the network. Once implemented, you will be
able to run the main file to fit a neural network on the MNIST data set of
handwritten digits, and evaluate your network’s out-of-sample performance on a
separate subset of the data.

There are a number of TODO’s listed in the files layers.cpp and utils.cu. Your
job is to fill all of these out with the necessary code (either from the CUDA
libraries we discussed in class or via writing your own kernels). Some of these
TODO’s will be labeled as lab 5, and others will be labeled as lab 6, indicating
which assignment they are meant to be filled out for. The final executables for
labs 5 and 6 will be bin/dense-neuralnet and bin/conv-neuralnet, respectively.
Both executables should compile regardless of how much of lab 5 or 6 has been
completed, but obviously they won’t run correctly until the relevant code has
been written completely.

Note that lab 6 will not run correctly without lab 5 working fully! If your lab
5 doesn’t work by the time you’ve turned it in, contact the TA’s to receive a
fully functional lab 5 to build on for lab 6 (i.e. you need to turn in lab 5
for a grade before receiving solutions).

It’s also worth noting that the supporting code for this lab may be larger than
you are used to working with. The only files you really need to understand are
layers.hpp, layers.cpp, utils.cuh, and utils.cu. The basics of how these files
are meant to work are outlined in the README included with this lab.

Unfortunately, we haven’t had the time to come up with any systematic way to
help you debug your code for these assignments. That said, here are a few
guidelines to follow that will make your life much easier:

1)  Wrap all of your CUDA calls and CUDA library calls with the appropriate
error checking macros! These are defined in helper_cuda.h and have been
adapted from the CUDA examples.

2)  If your code is working 100% correctly, you should expect to see the loss
decreasing and accuracy increasing with every iteration of training. If
your loss is increasing or turning into NaN’s, check the following:

a)  Are you adding the gradient into the current values of the weights and
biases instead of subtracting them (don’t do gradient ascent instead
of descent, since we want to MINIMIZE the loss)?

b)  Make sure you’re not adding the gradients with respect to the data
(i.e. the input and output minibatches of a given layer) into the
data itself!

c)  Especially for lab 5, make sure that your cuBLAS calls match the linear
algebra operations described in the slides. We’ve made an effort to
have notation be relatively consistent between the lecture slides and
the code.

3)  Refer to the lecture slides frequently! There are slides in each of lectures
15 and 17 (for labs 5 and 6 respectively) that outline the actual algorithms
you need to implement.

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