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import threading | |
import time | |
import random | |
def copy_row(row): | |
return [float(value+0.0) for value in row] | |
def copy_matrix(matrix): | |
return [copy_row(row) for row in matrix] | |
def multiply_row(A, B, row_index, result): | |
# Compute the row result | |
num_columns_B = len(B[0]) | |
num_columns_A = len(A[0]) | |
for j in range(num_columns_B): | |
sum = 0 | |
for k in range(num_columns_A): | |
sum += A[row_index][k] * B[k][j] | |
result.append(sum) | |
def parallel_matrix_multiplication(a, b, result, row_indices): | |
a = copy_matrix(a) | |
b = copy_matrix(b) | |
for row_index in row_indices: | |
tmp_result = [] | |
multiply_row(a, b, row_index, tmp_result) | |
result[row_index] = tmp_result | |
def multi_threaded_matrix_multiplication(a, b, num_threads): | |
num_rows = len(a) | |
result = [[0] * len(b[0]) for _ in range(num_rows)] | |
row_chunk = num_rows // num_threads | |
threads = [] | |
for i in range(num_threads): | |
start_row = i * row_chunk | |
end_row = (i + 1) * row_chunk if i != num_threads - 1 else num_rows | |
thread = threading.Thread(target=parallel_matrix_multiplication, args=(a, b, result, range(start_row, end_row))) | |
threads.append(thread) | |
thread.start() | |
for thread in threads: | |
thread.join() | |
return result | |
# Helper function to create a random matrix | |
def create_random_matrix(rows, cols): | |
return [[random.random() for _ in range(cols)] for _ in range(rows)] | |
def main(): | |
size = 500 # Define matrix size | |
a = create_random_matrix(size, size) | |
b = create_random_matrix(size, size) | |
num_threads = 8 # Define number of threads | |
start = time.perf_counter() | |
result = multi_threaded_matrix_multiplication(a, b, num_threads) | |
print("Matrix multiplication completed.", time.perf_counter() - start, "seconds.") | |
if __name__ == "__main__": | |
main() |
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