Lab 3 Tiled Matrix Multiplication

DUE Tuesday, May 26

Overview

The objective of this assignment is to implement a tiled matrix multiplication kernel that can support arbitrary sized matrices.

Tiled Matrix Multiplication

  1. For this lab, we will be using Github Classroom.
    Please join the classroom by clicking the following link: https://classroom.github.com/a/Dh1hxzac Once you join the classroom, a private github repository will automatically be created with the starter code.
    Simply git clone to copy the starter code to Bender or the GPGPU-Sim container.

  2. Edit the source files kernel.cu and main.cu to complete the functionality of the matrix multiplication on the device. The two matrices could be any size, but we will not test your code with an output matrix size exceeding 65,536 elements (for example, 256 x 256 input matrices) on GPGPU-Sim.

  3. There are three modes of operation for the application. Check main() for a description of the modes (repeated below). You will support each of these modes using a Tiled matrix multiplication implementation.

    • No arguments: The application will create two randomly initialized matrices to multiply size (1000x1000). After the device multiplication is invoked, it will compute the correct solution matrix using the CPU, and compare that solution with the device-computed solution. If it matches (within a certain tolerance), if will print out "Test PASSED" to the screen before exiting.
    • One argument: The application will use the random initialization to create the input matrices (size mxm, where m is the argument. Start your testing with small matrices.
    • Three arguments m, k, and n: The application will initialize the two input matrices with random values. A matrix will be of size m x k while the B matrix will be of size k x n, producing a C matrix of size m x n
    • Note that if you wish, you may add a mode to accept input matrices from files, or to dump input and output matrices to files to facilitate testing. The first three modes must remain untouched.
  4. Commit and push your completed tiled matrix multiplication code to the private repository.

Answer the following questions:

  1. On Bender, compare the execution time of a 256 x 256 square matrix multiplication compared to a 1024 x 64 and 64 x 1024 rectangular matrix multiply. All input matricies have 65k entries. What do you observe? Which is faster? Can you explain the observed behavior? Tip: You may want to comment out the verify() function in main.cu when timing this question.

  2. Conceptual Question: For a 64 square tiled matrix multiplication, how many times is each element of the input matrices loaded from global memory? Assume 16x16 tiles.

  3. Conceptual Question: For a 64 square non-tiled matrix multiplication, how many times is each element of the input matrices loaded from global memory?
  1. Now let's compare the effect of tile size of a 256 x 256 square tiled matrix multiplication. Run your matrix multiply code with tile size of 8x8 and 16x16. Which tile size performed the fastest, which tile size performed the slowest? Why do you think that is?

Submission

  1. Commit and push your completed tiled matrix multiplication code to the github repository. (You only need to modify kernel.cu and main.cu.)
  2. Answer the previous questions by including a report document in the repository. Please name your report report.pdf or report.txt or report.docx, etc.

    Please include your name in the report.