Description
In this assignment, you will design and code a CUDA-C/C++ version of a Matrix normalization algorithm.
Reading the Wikipedia page on Standard Score is encouraged
http://en.wikipedia.org/wiki/Standard_score. In Data Analytics and Machine Learning, matrices usually
contain data points (rows) in a particular space defined by their attributes (columns). Sometimes, and
because attributes may represent things that are very different in nature, normalization by column is
required.
Your goal is to take advantage of the power of GPUs to perform this task as fast as possible. Realize that
column normalization is composed of three steps per column:
1. Calculating the mean of the column
2. Calculating the standard deviation (which requires the mean)
3. Finally, calculating the normalized value by performing the following calculation (where B is the
normalized matrix of A)
B[row][col] = (A[row][col] – mean) / standard_deviation
It is possible to see that the first two steps can be achieved with a REDUCTION algorithm (recall that we
saw the SUM-reduction algorithm in class). In this part, you need to be very careful with your design
decisions. Where you put the data, and how you perform the reductions matter. As a hint, you may
consider splitting the reductions in two: first, inside the values in each block and, second, reducing the
totals for every block. Once the mean and standard deviation are calculated, the third step is
straightforward.
The sequential code is provided with this assignment, and can be used as a reference for debugging and
performance comparison. Your code will be grade partially on the efficiency of your algorithm. You
should write a memo explaining your design decisions very clearly and your performance evaluation.
Even if your code does not work, or is not efficient enough, you should write the reasons you think that is
so.
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Note on cheating: This is an individual assignment. Copying problem solutions or code is cheating.
Both the person copying and the person giving the answers will be equally penalized. Make sure
you do your own work. Also, make sure your code can’t be copied by other students. To do that,
you can change every file permission with chmod 700 file.c, or you can protect your whole home
directory with chmod 700 /home/username.