Multithreading increases the time in my matrix multiplication example?

Question

As the code shows, times increases by the increase of threads, and decrease by the decrease of threads, optimally at 1.

import java.util.concurrent.ExecutorService;
import java.util.concurrent.Executors;
import java.util.concurrent.TimeUnit;

public class MatrixMultiplication {
    public static class Matrix {
        int[][] container;
        int rows;
        int cols;

        public Matrix(int rows, int cols) {
            this.rows = rows;
            this.cols = cols;
            container = new int[rows][cols];
        }
    }

    public static class MultiplyThread implements Runnable {
        private int row;
        private int col;
        Matrix a;
        Matrix b;
        Matrix c;

        public MultiplyThread(Matrix a, Matrix b, Matrix c, int row, int col) {
            this.row = row;
            this.col = col;
            this.a = a;
            this.b = b;
            this.c = c;
        }

        @Override
        public void run() {
            int sum = 0;
            for (int i = 0; i < a.cols; i++) // or b.rows
                sum += a.container[row][i] * b.container[i][col];
            c.container[row][col] = sum;
        }
    }

    public static Matrix multiplyMatrices(Matrix a, Matrix b, int threads) throws InterruptedException {
        if (a.cols != b.rows)
            return null;
        int rows = a.rows;
        int cols = b.cols;
        Matrix ret = new Matrix(rows, cols);
        ExecutorService executor = Executors.newFixedThreadPool(threads);
        int i = 0, j = 0;
        for (i = 0; i < rows; i++)
            for (j = 0; j < cols; j++)
                executor.submit(new MultiplyThread(a, b, ret, i, j));
        executor.shutdown();
        executor.awaitTermination(Long.MAX_VALUE, TimeUnit.NANOSECONDS);
        return ret;
    }

    public static void main(String[] args) throws InterruptedException {
        int[][] mat = new int[][] { { 1, 1, 1, 1 }, { 1, 1, 1, 1 }, { 1, 1, 1, 1 }, { 1, 1, 1, 1 } };
        Matrix a = new Matrix(4, 4);
        a.container = mat;
        Matrix b = new Matrix(4, 4);
        b.container = mat;

        int numProcessors = Runtime.getRuntime().availableProcessors();
        System.out.println(numProcessors);
        for (int i = 10; i >= 1; i--) {
            long time = System.currentTimeMillis();
            for (int j = 0; j < 10000; j++)
                multiplyMatrices(a, b, i);
            time = System.currentTimeMillis() - time;
            System.out.println("number of threads: " + i + ", and the time: " + time);
        }
    }

}

As the code shows, times increases by the increase of threads, and decrease by the decrease of threads, optimally at 1.

a) http://stackoverflow.com/questions/504103/how-do-i-write-a-correct-micro-benchmark-in-java; b) these are tiny matrices - the overhead of starting extra threads will vastly outweigh any potential speed-up. — Andy Turner, Feb 01 '16 at 22:10
Check out my example of large matrix parallelization using Akka: https://github.com/GreaterMKEMeetup/akka-examples I used BigInteger to be able to multiply large matrices with large numbers. The speedup using a thread pool was significant. — aglassman, Feb 01 '16 at 23:04

score 1 · Answer 1 · answered Feb 01 '16 at 23:22

There is an issue of overhead for creating threads pointed out in the comments.

Additionally, when multiple CPUs attempt to modify memory that is represented on a cache line of each CPU, the first modification of that cache line causes the cache line in other CPUs to be marked as invalid. This creates a real performance issue in SMP systems. The issue is known as false sharing.

When you modify regions of memory that are close together in the address space from multiple CPUs, you are prone to false sharing. For an excellent write-up of the situation, see Dr. Dobbs.

Your code seems likely to be suffering from false sharing, as you meet the general criteria. You are modifying data, the data lies close together in memory, and you are doing this on multiple threads. If you also have more than one CPU core, there is almost certainly at least some degree of false sharing.

Multithreading increases the time in my matrix multiplication example?

1 Answers1