Programação Concorrente: Exercício de Multiplicar Matrizes com Threads

Summary

This video script guides viewers through the process of developing a matrix multiplication program. The task involves multiplying two matrices, 'A' and 'B', which are provided through files containing multiple cases for matrix multiplication. The script explains the need for an efficient implementation, with one version using a simple approach and another incorporating optimizations. Additionally, the program must calculate execution times and compare performance across different cases and system configurations. The goal is to analyze execution times and provide insights on optimizing matrix multiplication algorithms.

Takeaways

• 😀 The task is to develop a program to multiply two matrices, matrix A by matrix B.
• 😀 The matrices A and B are provided through a file available at a specific link.
• 😀 The file contains three test cases for matrix multiplication.
• 😀 The first two lines of each case specify the number of rows and columns for each matrix.
• 😀 You need to implement both a non-parallel and parallel version of the matrix multiplication to compare their performance.
• 😀 A credit-based approach will be used for implementing the parallel version of the program.
• 😀 The parallel implementation will determine which rows and columns to calculate based on the credit system.
• 😀 A spreadsheet will be used to capture execution times for each matrix multiplication test case.
• 😀 The spreadsheet should also include information about the machine being used and the execution times for different test cases.
• 😀 The execution times should be collected for various matrix sizes and the number of runs to calculate average performance.
• 😀 The parallel version will use multiple 'credits' to compute different rows of the resulting matrix concurrently.

Q & A

What is the main objective of the exercise described in the transcript?

The main objective is to develop a program to multiply two matrices, Matrix A and Matrix B, by implementing both a simple and a complex version of matrix multiplication to compare their performance.

Where are the matrices A and B provided from in this exercise?

The matrices A and B are provided from a file available at a specific link mentioned in the transcript.

What are the key components included in the file that contains the matrices?

The file contains three cases for matrix multiplication, with the first two lines of each case providing the dimensions of the matrices (number of rows and columns for each matrix).

What is the role of the 'credit' mentioned in the task?

The 'credit' refers to the approach for implementing parallel computation to process the rows of the matrix and calculate the resultant matrix efficiently. It is a mechanism for distributing the work among multiple threads or processors.

How should the program calculate the resulting matrix values?

To calculate the resulting matrix values, the program must identify the correct row from Matrix A and the correct column from Matrix B, multiply the corresponding elements, and then sum the products to form the values of the resulting matrix.

What is the purpose of creating a spreadsheet for the task?

The spreadsheet will capture the execution times for each of the three matrix multiplication cases. It will also display the machine's data, such as its specifications, to analyze the performance based on different execution times and matrix sizes.

How will the spreadsheet data help in evaluating performance?

The spreadsheet will help evaluate performance by comparing the execution times for different matrix sizes and by taking the average execution time over multiple runs, providing a clear picture of the efficiency of the implementation.

What does the term 'y credit' refer to in the context of the task?

'Y credit' refers to the number of rows in the resulting matrix. For instance, if the resulting matrix has 3 rows and 3 columns, there would be 3 separate computational tasks (threads or processes) to compute each row's result.

In the example of the 3x3 matrix, how will the program handle the computation of rows?

In the case of a 3x3 matrix, one thread or process will compute the result for the first row, another will compute for the second row, and the third will compute for the third row, ensuring parallel processing of the matrix multiplication.

What types of matrix multiplication implementations are expected in this task?

The task requires two types of implementations: one that uses a basic, single-threaded approach and another that utilizes parallel processing to optimize the computation.