在一些简短的测试中，我发现两者的结合在我所谓的合理密集数学中会更好:

类型:<双[]>列表

时间:00:00:05.1861300

类型：列表<列表<double>>

时间:00:00:05.7941351

类型:double[行*列]

时间:00:00:06.0547118

运行代码:

int rows = 10000;
int columns = 10000;

IMatrix Matrix = new IMatrix(rows, columns);

Stopwatch stopwatch = new Stopwatch();
stopwatch.Start();


for (int r = 0; r < Matrix.Rows; r++)
    for (int c = 0; c < Matrix.Columns; c++)
        Matrix[r, c] = Math.E;

for (int r = 0; r < Matrix.Rows; r++)
    for (int c = 0; c < Matrix.Columns; c++)
        Matrix[r, c] *= -Math.Log(Math.E);


stopwatch.Stop();
TimeSpan ts = stopwatch.Elapsed;

Console.WriteLine(ts.ToString());

我真希望我们有一些顶尖的硬件加速矩阵类，就像。net团队用system . numbers . vectors类做的那样!

c#可能是最好的ML语言，只要在这方面多做一些工作!

很容易测量…

在少量的紧循环处理代码中，我知道长度是固定的，我使用数组来进行额外的微小优化;如果你使用索引器/ for表单，数组可以稍微快一点——但是IIRC认为这取决于数组中数据的类型。但除非你需要进行微观优化，否则尽量保持简单，使用List<T>等。

当然，这只适用于读取所有数据的情况;对于基于键的查找，字典会更快。

下面是我使用“int”的结果(第二个数字是一个校验和，以验证它们都做了相同的工作):

(修改bug)

List/for: 1971ms (589725196)
Array/for: 1864ms (589725196)
List/foreach: 3054ms (589725196)
Array/foreach: 1860ms (589725196)

基于试验台:

using System;
using System.Collections.Generic;
using System.Diagnostics;
static class Program
{
    static void Main()
    {
        List<int> list = new List<int>(6000000);
        Random rand = new Random(12345);
        for (int i = 0; i < 6000000; i++)
        {
            list.Add(rand.Next(5000));
        }
        int[] arr = list.ToArray();

        int chk = 0;
        Stopwatch watch = Stopwatch.StartNew();
        for (int rpt = 0; rpt < 100; rpt++)
        {
            int len = list.Count;
            for (int i = 0; i < len; i++)
            {
                chk += list[i];
            }
        }
        watch.Stop();
        Console.WriteLine("List/for: {0}ms ({1})", watch.ElapsedMilliseconds, chk);

        chk = 0;
        watch = Stopwatch.StartNew();
        for (int rpt = 0; rpt < 100; rpt++)
        {
            for (int i = 0; i < arr.Length; i++)
            {
                chk += arr[i];
            }
        }
        watch.Stop();
        Console.WriteLine("Array/for: {0}ms ({1})", watch.ElapsedMilliseconds, chk);

        chk = 0;
        watch = Stopwatch.StartNew();
        for (int rpt = 0; rpt < 100; rpt++)
        {
            foreach (int i in list)
            {
                chk += i;
            }
        }
        watch.Stop();
        Console.WriteLine("List/foreach: {0}ms ({1})", watch.ElapsedMilliseconds, chk);

        chk = 0;
        watch = Stopwatch.StartNew();
        for (int rpt = 0; rpt < 100; rpt++)
        {
            foreach (int i in arr)
            {
                chk += i;
            }
        }
        watch.Stop();
        Console.WriteLine("Array/foreach: {0}ms ({1})", watch.ElapsedMilliseconds, chk);

        Console.ReadLine();
    }
}

简短的回答:

在。net List中<T>和Array<T>具有相同的速度/性能，因为在。net List中是Array的包装器。

再说一遍:List在里面是数组!在。net List中<T>是其他语言中的<T>数组列表。

详细说明在哪些情况下需要使用什么:

Array need to use: So often as possible. It's fast and takes smallest RAM range for same amount information. If you know exact count of cells needed If data saved in array < 85000 b (85000/32 = 2656 elements for integer data) If needed high Random Access speed List need to use: If needed to add cells to the end of list (often) If needed to add cells in the beginning/middle of the list (NOT OFTEN) If data saved in array < 85000 b (85000/32 = 2656 elements for integer data) If needed high Random Access speed LinkedList need to use: If needed to add cells in the beginning/middle/end of the list (often) If needed only sequential access (forward/backward) If you need to save LARGE items, but items count is low. Better do not use for large amount of items, as it's use additional memory for links. If you not sure that you need LinkedList -- YOU DON'T NEED IT. Just do not use it.

更多的细节:

更多细节:

https://stackoverflow.com/a/29263914/4423545

我担心在其他答案中发布的基准测试仍然会为编译器留下优化，消除或合并循环的空间，所以我写了一个:

使用不可预测的输入(随机) 运行计算结果并将结果打印到控制台 每次重复修改输入数据

结果是，直接数组的性能比访问封装在IList中的数组要好250%:

10亿次数组访问:4000毫秒 10亿次列表访问:10000毫秒 1亿个数组访问:350毫秒 1亿次列表访问:1000毫秒

代码如下:

static void Main(string[] args) {
  const int TestPointCount = 1000000;
  const int RepetitionCount = 1000;

  Stopwatch arrayTimer = new Stopwatch();
  Stopwatch listTimer = new Stopwatch();

  Point2[] points = new Point2[TestPointCount];
  var random = new Random();
  for (int index = 0; index < TestPointCount; ++index) {
    points[index].X = random.NextDouble();
    points[index].Y = random.NextDouble();
  }

  for (int repetition = 0; repetition <= RepetitionCount; ++repetition) {
    if (repetition > 0) { // first repetition is for cache warmup
      arrayTimer.Start();
    }
    doWorkOnArray(points);
    if (repetition > 0) { // first repetition is for cache warmup
      arrayTimer.Stop();
    }

    if (repetition > 0) { // first repetition is for cache warmup
      listTimer.Start();
    }
    doWorkOnList(points);
    if (repetition > 0) { // first repetition is for cache warmup
      listTimer.Stop();
    }
  }

  Console.WriteLine("Ignore this: " + points[0].X + points[0].Y);
  Console.WriteLine(
    string.Format(
      "{0} accesses on array took {1} ms",
      RepetitionCount * TestPointCount, arrayTimer.ElapsedMilliseconds
    )
  );
  Console.WriteLine(
    string.Format(
      "{0} accesses on list took {1} ms",
      RepetitionCount * TestPointCount, listTimer.ElapsedMilliseconds
    )
  );

}

private static void doWorkOnArray(Point2[] points) {
  var random = new Random();

  int pointCount = points.Length;

  Point2 accumulated = Point2.Zero;
  for (int index = 0; index < pointCount; ++index) {
    accumulated.X += points[index].X;
    accumulated.Y += points[index].Y;
  }

  accumulated /= pointCount;

  // make use of the result somewhere so the optimizer can't eliminate the loop
  // also modify the input collection so the optimizer can merge the repetition loop
  points[random.Next(0, pointCount)] = accumulated;
}

private static void doWorkOnList(IList<Point2> points) {
  var random = new Random();

  int pointCount = points.Count;

  Point2 accumulated = Point2.Zero;
  for (int index = 0; index < pointCount; ++index) {
    accumulated.X += points[index].X;
    accumulated.Y += points[index].Y;
  }

  accumulated /= pointCount;

  // make use of the result somewhere so the optimizer can't eliminate the loop
  // also modify the input collection so the optimizer can merge the repetition loop
  points[random.Next(0, pointCount)] = accumulated;
}

实际上，如果在循环中执行一些复杂的计算，那么数组索引器与列表索引器的性能可能会非常小，最终，这无关紧要。

对于@Marc Gravell的回答，我有两点需要澄清。

测试是在。net 6 x64版本中完成的。

测试代码结束。

数组和列表没有以相同的方式测试

为了在相同条件下测试array和List，还需要修改for。

for (int i = 0; i < arr.Length; i++)

新版本:

int len = arr.Length;
for (int i = 0; i < len; i++)

瓶颈列表/foreach:

List (List/foreach测试)的瓶颈是可以修复的。

改为:

list.ForEach(x => chk += x);

在Windows 10 pro 21H1 x64的笔记本电脑上测试运行，内核为i7-10510U

List/for Count out: 1495ms (589725196)
List/for Count in: 1706ms (589725196)
Array/for Count out: 945ms (589725196)
Array/for Count in: 1072ms (589725196)
List/foreach: 2114ms (589725196)
List/foreach fixed: 1210ms (589725196)
Array/foreach: 1179ms (589725196)

结果解释

数组/for比原始测试快。(减少12%)

List/foreach fixed比List/for快。

List/foreach fixed接近Array/foreach。

这个测试我已经运行了几次。结果改变了，但数量级保持不变。

这个测试的结果表明，您确实必须对性能有很大的需求才能强制使用Array。

根据用于操作List的方法，性能可以除以2。

这个测试是局部的。没有随机存取、直接存取、写存取测试等。

是我弄错了什么地方，还是你有其他提高性能的想法?

测试代码:

using System;
using System.Collections.Generic;
using System.Diagnostics;
static class Program
{
    static void Main()
    {        List<int> list = new List<int>(6000000);
        Random rand = new Random(12345);
        for (int i = 0; i < 6000000; i++)
        {
            list.Add(rand.Next(5000));
        }
        int[] arr = list.ToArray();

        int chk = 0;
        Stopwatch watch = Stopwatch.StartNew();
        for (int rpt = 0; rpt < 100; rpt++)
        {
            int len = list.Count;
            for (int i = 0; i < len; i++)
            {
                chk += list[i];
            }
        }
        watch.Stop();
        Console.WriteLine("List/for Count out: {0}ms ({1})", watch.ElapsedMilliseconds, chk);

        chk = 0;
        Stopwatch watch = Stopwatch.StartNew();
        for (int rpt = 0; rpt < 100; rpt++)
        {
            for (int i = 0; i < list.Count; i++)
            {
                chk += list[i];
            }
        }
        watch.Stop();
        Console.WriteLine("List/for Count in: {0}ms ({1})", watch.ElapsedMilliseconds, chk);

        chk = 0;
        watch = Stopwatch.StartNew();
        for (int rpt = 0; rpt < 100; rpt++)
        {
            int len = arr.Length;
            for (int i = 0; i < len; i++)
            {
                chk += arr[i];
            }
        }
        watch.Stop();
        Console.WriteLine("Array/for Count out: {0}ms ({1})", watch.ElapsedMilliseconds, chk);

        chk = 0;
        watch = Stopwatch.StartNew();
        for (int rpt = 0; rpt < 100; rpt++)
        {
            for (int i = 0; i < arr.Length; i++)
            {
                chk += arr[i];
            }
        }
        watch.Stop();
        Console.WriteLine("Array/for Count in: {0}ms ({1})", watch.ElapsedMilliseconds, chk);

        chk = 0;
        watch = Stopwatch.StartNew();
        for (int rpt = 0; rpt < 100; rpt++)
        {
            foreach (int i in list)
            {
                chk += i;
            }
        }
        watch.Stop();
        Console.WriteLine("List/foreach: {0}ms ({1})", watch.ElapsedMilliseconds, chk);

        chk = 0;
        watch = Stopwatch.StartNew();
        for (int rpt = 0; rpt < 100; rpt++)
        {
            list.ForEach(i => chk += i);
        }
        watch.Stop();
        Console.WriteLine("List/foreach fixed: {0}ms ({1})", watch.ElapsedMilliseconds, chk);

        chk = 0;
        watch = Stopwatch.StartNew();
        for (int rpt = 0; rpt < 100; rpt++)
        {
            foreach (int i in arr)
            {
                chk += i;
            }
        }
        watch.Stop();
        Console.WriteLine("Array/foreach: {0}ms ({1})", watch.ElapsedMilliseconds, chk);

        Console.ReadLine();
    }
}