Parallel Programming

Åbo Akademi University, Information Technology Department

Instructor: Alireza Olama

Homework Assignment 1: Naive Matrix Multiplication in C++

Due Date: 22/04/2025

Points: 100

Assignment Overview

Welcome to the first homework assignment of the Parallel Programming course! This is a warm-up exercise designed to get you comfortable with low-level memory management and basic matrix operations in C++. You will implement a naive matrix multiplication function (naive_matmul) that computes C = A * B using C-style arrays and manual memory management. This assignment focuses on file I/O, memory management, and understanding matrix storage in memory, setting the stage for parallelization topics later in the course.

Note: This assignment does not involve parallel programming. You will run your implementation locally on your machine.

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Technical Requirements

Matrix Multiplication

• Implement the naive_matmul function to perform matrix multiplication C = A * B using a triple nested loop (naive method).
• Input matrices ( A ) and ( B ) will be provided in two text files: input0.raw (for ( A )) and input1.raw ( for ( B )).
• The resulting matrix ( C ) must be written to a text file named result.raw.
• The first row of input0.raw and input1.raw contains matrix dimensions (number of rows and columns).

Memory Management

• Use only C-style arrays with manual memory allocation (malloc or new) and deallocation (free or delete).
• Do not use smart pointers, STL containers (e.g., std::vector), or other dynamic memory libraries.

Matrix Storage

• Store all matrices as 1D arrays in row-major order.
• Row-Major vs. Column-Major Explanation:
  • In row-major order, elements of a matrix are stored row-by-row in memory. For a matrix ( A ) with dimensions ( M * N ), the element ( A[i][j] ) is accessed at index ( i * N + j ) in a 1D array.
  • In contrast, column-major order stores elements column-by-column (used in languages like Fortran). Since C++ uses row-major order by default, we adopt it here.
  • For more details, refer to: Row- and Column-Major Order.

Validation

• A reference solution file (output.raw) is provided.
• Compare your result.raw with output.raw to validate correctness. The test passes if they match exactly.

Build Instructions

• Use CMake to configure and build your project. A sample CMakeLists.txt is provided below.
• Windows Users:
  • Option 1: Use CLion with CMake (recommended). Load the project and build/run.
  • Option 2: Use Visual Studio with CMake support. Open the folder and let VS configure it.
  • Alternatively, install MinGW and run cmake -G "MinGW Makefiles" followed by make.
• Linux/Mac Users:
  • Run cmake . to generate a Makefile, then make to build the executable.

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The input files for this assignment are located in the data folder, which contains 10 test cases organized into subfolders named 0 through 9. Each subfolder includes an input0.raw file (matrix ( A )), an input1.raw file ( matrix ( B )), and a reference output file output.raw (matrix ( C = A * B )). Your executable should accept a case number (an integer from 0 to 9) as a command-line argument, use it to locate the corresponding folder in data, and perform matrix multiplication using the input files in that folder. The resulting matrix should be written to result.raw within the same subfolder. This setup allows automated testing across all 10 cases.

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Submission Requirements

1. Fork this Repository

2. Clone the Fork

   git clone https://github.com/parallelcomputingabo/Homework-1.git
   cd Homework-1

3. Create a New Branch

   git checkout -b student-name

4. Implement Your Solution

5. Commit and Push

   git add .
   git commit -m "student-name: Implemented naive matrix multiplication"

   git push origin student-name
   

6. Submit a Pull Request (PR)

   • Go to your forked repository on GitHub.
   • Click on the "Pull Requests" tab.
   • Click on "New Pull Request".
   • Select the base repository and branch (e.g., main) and compare it with your branch.
   • Add a title and description, then click "Create Pull Request".

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Grading (100 Points Total)

Subtask	Points
File reading and memory allocation	20
Matrix multiplication correctness	30
Correct use of row-major indexing	15
File writing and deallocation	15
Code clarity and commenting	10
Successful test comparison with output.raw	10
Total	100

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Good luck, and have fun coding!