如何使用 Stream Parallel Java 执行矩阵计算? [英] How Do I Perform Matrix Calculation With Stream Parallel Java?

问题描述

我正在尝试使用多维数组 ([verybigrow][2]) 制作矩阵算术运算方法.我是新手，我只是找不到我做错了什么.如果您能告诉我它是什么，我真的很感激.

I'm trying to make a matrix arithmetic operation method using multidimensional arrays ([verybigrow][2]). I'm new at this, and I just can't find what I'm doing wrong. I'd really appreciate any help in telling me what it is.

    try {
        Stream<String> Matrix = Files.lines(Paths.get(file)).parallel();
        String[][] DataSet = Matrix.map(mapping -> mapping.split(",")).toArray(String[][]::new);
        Double[][] distanceTable = new Double[DataSet.length - 1][];

        /* START WANT TO REPLACE THIS MATRIX CALCULATION WITH PARALLEL STREAM RATHER THAN USE TRADITIONAL ARRAY ARITHMETICS START  */

        for (int i = 0; i < distanceTable.length - 1; ++i) {
            distanceTable[i] = new Double[i + 1];
            for (int j = 0; j <= i; ++j) {
                double distance = 0.0;
                for (int k = 0; k < DataSet[i + 1].length; ++k) {
                    double difference = Double.parseDouble(DataSet[j][k]) - Double.parseDouble(DataSet[i + 1][k]);
                    distance += difference * difference;
                }
                distanceTable[i][j] = distance;
            }
        }

        /* END WANT TO REPLACE THIS MATRIX CALCULATION WITH PARALLEL STREAM RATHER THAN USE TRADITIONAL ARRAY ARITHMETICS START  */

        } catch ( Exception except ){
            System.out.println ( except );
        }

我宁愿不使用库或类似的东西，我这样做主要是为了了解它是如何工作的.非常感谢你.如果你问数据看起来像:

I had rather not use libraries or anything like that, I'm mostly doing this to learn how it works. Thank you so much in advance. if you asking the data looks like :

4,53
5,63
10,59
9,77
13,49

数据处理的输出应该是这样的:

The Output of data processing should look like this :

[101] <- ((4-5)^2) + ((53-63)^2)
[72, 41] <- ( ((4-10)^2) + ((53-59)^2) ), ( ((5,10)^2) + ((63-59)^2))
[601.0, 212.0, 325.0]
[97.0, 260.0, 109.0, 800.0]
[337.0, 100.0, 109.0, 80.0, 400.0]

推荐答案

我尝试将 matrixDistance 更改为 distanceTable.尝试将此代码移动到不同的方法中，以便您可以并行运行它

I try to change matrixDistance with distanceTable. Try to move this code into different method so you can run it parallel

        for(int i = 0; i < matrixDistance.length - 1; ++i) {
            distanceTable[i] = new double[i + 1];
            for(int j = 0; j <= i; ++j) {
                double distance = 0.0;
                for(int k = 0; k < DataSet[i+1].length; ++k) {
                    double difference = Double.parseDouble(DataSet[j][k]) - Double.parseDouble(DataSet[i+1][k]);
                    distance += difference * difference;
                }
                distanceTable[i][j] = distance;
            }
        }

我根据您的问题创建了这个示例.

I've created this example based on your question.

    public void parallel(String file)
    ....
    // parsing from csv into matrix 2d Double[][]
    ....
        IntStream
            .range(1, data.length - 1)
            .parallel()
            .forEach(i -> {
                add(euclidian.euclidian(Arrays.copyOf(data, i+1)), i);
            });
}

这是您算法的迷你版.

    public Double[] euclidian(Double[][] data) {
        Double[] result = new Double[data.length - 1];
        for (int i = 0; i < result.length; i++) {
            result[i] =
                    Math.pow(data[i][0] - data[data.length - 1][0], 2) +
                            Math.pow(data[i][1] - data[data.length - 1][1], 2);
        }

        return result;
    }

并且由于并行执行，需要添加锁方法，用于将数据插入到distanceTable中.

And because of parallel execution, you need to add locking method for insert data into distanceTable.

    private final Object lock = new Object();
    Double[][] distanceTable;

    void add(Double[] data, int index){
        synchronized (lock) {
            distanceTable[index - 1] = data;
        }
    }

我已经在我的笔记本电脑上测试过，对于 csv 文件中的 74 行，比较是这样的(ORI 使用您的代码，PAR 使用我的方法):

I've tested it in my laptop, for 74 row in csv file the comparison is like this (ORI is using your code, PAR is using my approach):

java -jar target/stream-example-1.0-SNAPSHOT.jar test.csv 
#####################
ORI read: 59 ms
ORI  map: 71 ms
ORI time: 80 ms
#####################
PAR read: 0 ms
PAR  map: 6 ms
PAR time: 11 ms

希望有帮助.

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