Java Multithreading and Parallel Programming Masterclass - Parallel Matrix Multiplication - The Naive Way

This lecture covers the matrix multiplication algorithm and explores how to optimize it using multithreading. It explains the algorithm's workings, including the dot product and how elements are computed independently. The lecture then demonstrates both serial and parallel implementations, highlighting the use of a thread pool for parallel processing. Finally, it compares the performance of both implementations, showing that the parallel version is significantly faster.

What is the primary operation used to compute elements in the output matrix during matrix multiplication?

• Scalar multiplication

• Matrix inversion

• Dot product

• Cross product

How does the choice of column in the second input array affect the output matrix?

• It determines the row in the output matrix.

• It has no effect on the output matrix.

• It determines the column in the output matrix.

• It determines the diagonal in the output matrix.

Why is matrix multiplication suitable for parallelization?

• It requires a single thread for execution.

• Elements can be computed independently.

• It is a recursive algorithm.

• Each element computation depends on the previous one.

What is the first step in implementing the matrix multiplication algorithm in code?

• Initialize the matrices

• Print the output matrix

• Check the result

• Define the thread pool

In the serial implementation, what is the role of the 'K' variable?

• It tracks the number of threads.

• It initializes the matrices.

• It is used to sum the elements.

• It iterates over the elements for multiplication.

What is the purpose of using a thread pool in the parallel implementation?

• To limit the number of concurrent threads

• To increase the number of tasks

• To reduce memory usage

• To simplify the code

How is the parallel version of the algorithm different from the serial version?

• It requires fewer computations.

• It uses more memory.

• It creates tasks for each element to be computed in parallel.

• It computes elements sequentially.

What was the observed speedup when using the parallel version on a 2000x2000 matrix?

• 4x

• 3x

• 2x

• 1.5x

Why is it important to use the check result method after running the algorithm?

• To ensure the matrices are initialized

• To measure execution time

• To print the matrices

• To verify the correctness of the output

What is a recommended way to further analyze the performance of the parallel algorithm?

• Ignore the execution time

• Vary the number of threads and plot results on a graph

• Use a single thread

• Run the tests once