GPU Lab: Implementing Dense Matrix Multiplication with CUDA

If you have already checked out the repo before 9/8 8PM, you need to rerun `git pull` to make sure everything is up to date.

To submit your code, you need to run `rai -p ./MP2 --submit MP2`. To check your submission history, run `rai history -p ./MP2` (last 20 enties) or `rai l-history -p ./MP2` (last 100 enties).

Objective

The purpose of this lab is to implement a basic dense matrix multiplication routine.

Prerequisites

Before starting this lab, make sure that:

* You have completed the "Vector Addition" MP (MP1)

* You have completed all week 2 lectures or videos

Instruction

Edit the code in `template.cu` to perform the following:

- allocate device memory

- copy host memory to device

- initialize thread block and kernel grid dimensions

- invoke CUDA kernel

- copy results from device to host

- deallocate device memory

Instructions about where to place each part of the code is

demarcated by the `//@@` comment lines.

You can test your code by running `rai -p ./MP2`. If your solution is

correct, you should be able to see the following output for each of

the 10 test datasets:

```

--------------

Dataset X

The dimensions of A are X x X

The dimensions of B are X x X

...

Solution is correct

```