当我遇到一个有趣的结果时，我正在做一些有趣的实验计算：
Completed 1024x1024 pixels with 700 points in...
For Loop (Inline): 19636ms
For Loop: 12612ms
Parallel.For Loop: 3835ms
这不是我所期望的。
系统：Windows 7 64、i3 2120【双核、4线程】、Visual Studio 2010。
构建：优化开启，发布模式 [无调试器]，32 位。
其次是令人失望的 64 位性能。虽然它在比率方面更符合我的预期，但它通过全面减慢来实现这一点。
Completed 1024x1024 pixels with 700 points in...
For Loop (Inline): 23409ms
For Loop: 24373ms
Parallel.For Loop: 6839ms
计算很简单：对于索引 x 和 y，找到最接近的 Vector3 并将其存储在二维数组中。
如果你敢的话，这个问题是试图解释为什么内联 for 循环如此缓慢。解释 64 位版本缺乏性能的奖励积分。
using System;
using System.Diagnostics;
using System.Threading.Tasks;
namespace TextureFromPoints
{
class Program
{
const int numPoints = 700;
const int textureSize = 1024;
static Random rnd = new Random();
static void Main(string[] args)
{
while (true)
{
Console.WriteLine("Starting");
Console.WriteLine();
var pointCloud = new Vector3[numPoints];
for (int i = 0; i < numPoints; i++)
pointCloud[i] = new Vector3(textureSize);
var result1 = new Vector3[textureSize, textureSize];
var result2 = new Vector3[textureSize, textureSize];
var result3 = new Vector3[textureSize, textureSize];
var sw1 = Stopwatch.StartNew();
for (int x = 0; x < textureSize; x++)
for (int y = 0; y < textureSize; y++)
{
var targetPos = new Vector3(x, y, 0);
var nearestV3 = pointCloud[0];
var nearestV3Distance = nearestV3.DistanceToPoint(targetPos);
for (int i = 1; i < numPoints; i++)
{
var currentV3 = pointCloud[i];
var currentV3Distance = currentV3.DistanceToPoint(targetPos);
if (currentV3Distance < nearestV3Distance)
{
nearestV3 = currentV3;
nearestV3Distance = currentV3Distance;
}
}
result1[x, y] = nearestV3;
}
sw1.Stop();
var sw2 = Stopwatch.StartNew();
for (int x = 0; x < textureSize; x++)
for (int y = 0; y < textureSize; y++)
Computation(pointCloud, result2, x, y);
sw2.Stop();
var sw3 = Stopwatch.StartNew();
Parallel.For(0, textureSize, x =>
{
for (int y = 0; y < textureSize; y++)
Computation(pointCloud, result3, x, y);
});
sw3.Stop();
Console.WriteLine("Completed {0}x{0} pixels with {1} points in...", textureSize, numPoints);
Console.WriteLine("{0}: {1}ms", "For Loop (Inline)", sw1.ElapsedMilliseconds);
Console.WriteLine("{0}: {1}ms", "For Loop", sw2.ElapsedMilliseconds);
Console.WriteLine("{0}: {1}ms", "Parallel.For Loop", sw3.ElapsedMilliseconds);
Console.WriteLine();
Console.Write("Verifying Data: ");
Console.WriteLine(CheckResults(result1, result2) && CheckResults(result1, result3) ? "Valid" : "Error");
Console.WriteLine(); Console.WriteLine();
Console.ReadLine();
}
}
private static bool CheckResults(Vector3[,] lhs, Vector3[,] rhs)
{
for (int x = 0; x < textureSize; x++)
for (int y = 0; y < textureSize; y++)
if (!lhs[x, y].Equals(rhs[x, y]))
return false;
return true;
}
private static void Computation(Vector3[] pointCloud, Vector3[,] result, int x, int y)
{
var targetPos = new Vector3(x, y, 0);
var nearestV3 = pointCloud[0];
var nearestV3Distance = nearestV3.DistanceToPoint(targetPos);
for (int i = 1; i < numPoints; i++)
{
var currentV3 = pointCloud[i];
var currentV3Distance = currentV3.DistanceToPoint(targetPos);
if (currentV3Distance < nearestV3Distance)
{
nearestV3 = currentV3;
nearestV3Distance = currentV3Distance;
}
}
result[x, y] = nearestV3;
}
struct Vector3
{
public float x;
public float y;
public float z;
public Vector3(float x, float y, float z)
{
this.x = x;
this.y = y;
this.z = z;
}
public Vector3(float randomDistance)
{
this.x = (float)rnd.NextDouble() * randomDistance;
this.y = (float)rnd.NextDouble() * randomDistance;
this.z = (float)rnd.NextDouble() * randomDistance;
}
public static Vector3 operator -(Vector3 a, Vector3 b)
{
return new Vector3(a.x - b.x, a.y - b.y, a.z - b.z);
}
public float sqrMagnitude()
{
return x * x + y * y + z * z;
}
public float DistanceToPoint(Vector3 point)
{
return (this - point).sqrMagnitude();
}
}
}
}
更新：感谢Drew Marsh的努力，我们现在有了这个超级优化的版本，它内联了所有 V3 操作。
using System;
using System.Diagnostics;
using System.Threading.Tasks;
namespace TextureFromPoints
{
class RevisedProgram
{
const int numPoints = 700;
const int textureSize = 1024;
static Random rnd = new Random();
static void Main(string[] args)
{
while (true)
{
Console.WriteLine("Starting REVISED");
Console.WriteLine();
var pointCloud = new Vector3[numPoints];
for (int i = 0; i < numPoints; i++)
pointCloud[i] = new Vector3(textureSize);
var result1 = new Vector3[textureSize, textureSize];
var result2 = new Vector3[textureSize, textureSize];
var result3 = new Vector3[textureSize, textureSize];
var sw1 = Inline(pointCloud, result1);
var sw2 = NotInline(pointCloud, result2);
var sw3 = Parallelized(pointCloud, result3);
Console.WriteLine("Completed {0}x{0} pixels with {1} points in...", textureSize, numPoints);
Console.WriteLine("{0}: {1}ms", "For Loop (Inline)", sw1.ElapsedMilliseconds);
Console.WriteLine("{0}: {1}ms", "For Loop", sw2.ElapsedMilliseconds);
Console.WriteLine("{0}: {1}ms", "Parallel.For Loop", sw3.ElapsedMilliseconds);
Console.WriteLine();
Console.Write("Verifying Data: ");
Console.WriteLine(CheckResults(result1, result2) && CheckResults(result1, result3) ? "Valid" : "Error");
Console.WriteLine();
Console.WriteLine();
Console.ReadLine();
}
}
private static Stopwatch Parallelized(Vector3[] pointCloud, Vector3[,] result3)
{
var sw3 = Stopwatch.StartNew();
Parallel.For(0, textureSize, x =>
{
for (int y = 0; y < textureSize; y++)
Computation(pointCloud, result3, x, y);
});
sw3.Stop();
return sw3;
}
private static Stopwatch NotInline(Vector3[] pointCloud, Vector3[,] result2)
{
var sw2 = Stopwatch.StartNew();
for (int x = 0; x < textureSize; x++)
for (int y = 0; y < textureSize; y++)
Computation(pointCloud, result2, x, y);
sw2.Stop();
return sw2;
}
private static Stopwatch Inline(Vector3[] pointCloud, Vector3[,] result1)
{
var sw1 = Stopwatch.StartNew();
for (int x = 0; x < textureSize; x++)
for (int y = 0; y < textureSize; y++)
{
var targetPos = new Vector3(x, y, 0);
var nearestV3 = pointCloud[0];
Vector3 temp1 = new Vector3(nearestV3.x - targetPos.x, nearestV3.y - targetPos.y, nearestV3.z - targetPos.z);
var nearestV3Distance = temp1.x * temp1.x + temp1.y * temp1.y + temp1.z * temp1.z;
for (int i = 1; i < numPoints; i++)
{
var currentV3 = pointCloud[i];
Vector3 temp2 = new Vector3(currentV3.x - targetPos.x, currentV3.y - targetPos.y, currentV3.z - targetPos.z);
var currentV3Distance = temp2.x * temp2.x + temp2.y * temp2.y + temp2.z * temp2.z;
if (currentV3Distance < nearestV3Distance)
{
nearestV3 = currentV3;
nearestV3Distance = currentV3Distance;
}
}
result1[x, y] = nearestV3;
}
sw1.Stop();
return sw1;
}
private static bool CheckResults(Vector3[,] lhs, Vector3[,] rhs)
{
for (int x = 0; x < textureSize; x++)
for (int y = 0; y < textureSize; y++)
if (!lhs[x, y].Equals(rhs[x, y]))
return false;
return true;
}
private static void Computation(Vector3[] pointCloud, Vector3[,] result, int x, int y)
{
var targetPos = new Vector3(x, y, 0);
var nearestV3 = pointCloud[0];
Vector3 temp1 = new Vector3(nearestV3.x - targetPos.x, nearestV3.y - targetPos.y, nearestV3.z - targetPos.z);
var nearestV3Distance = temp1.x * temp1.x + temp1.y * temp1.y + temp1.z * temp1.z;
for (int i = 1; i < numPoints; i++)
{
var currentV3 = pointCloud[i];
Vector3 temp2 = new Vector3(currentV3.x - targetPos.x, currentV3.y - targetPos.y, currentV3.z - targetPos.z);
var currentV3Distance = temp2.x * temp2.x + temp2.y * temp2.y + temp2.z * temp2.z;
if (currentV3Distance < nearestV3Distance)
{
nearestV3 = currentV3;
nearestV3Distance = currentV3Distance;
}
}
result[x, y] = nearestV3;
}
struct Vector3
{
public float x;
public float y;
public float z;
public Vector3(float x, float y, float z)
{
this.x = x;
this.y = y;
this.z = z;
}
public Vector3(float randomDistance)
{
this.x = (float)rnd.NextDouble() * randomDistance;
this.y = (float)rnd.NextDouble() * randomDistance;
this.z = (float)rnd.NextDouble() * randomDistance;
}
}
}
}
它给出了以下结果：
x86
Completed 1024x1024 pixels with 700 points in...
For Loop (Inline): 3820ms
For Loop: 3962ms
Parallel.For Loop: 1681ms
x64
Completed 1024x1024 pixels with 700 points in...
For Loop (Inline): 10978ms
For Loop: 10924ms
Parallel.For Loop: 3073ms
所以好消息是我们可以大大提高这段代码的性能——并且让单线程版本的运行速度与它的并行表亲保持一致。
坏消息是这意味着完全放弃 x64 并手动内联所有数学。
在这个阶段，我对编译器的性能感到非常失望——我希望它们会好得多。
结论
这是令人沮丧和悲伤的......虽然我们真的不知道为什么我们可以对它是由愚蠢的编译器引起的做出有根据的猜测。只需将编译器从 x64 更改为 x86 并进行一些手动内联，24 秒到 3.8 秒并不是我所期望的。然而，我已经完成了我正在编写的概念证明，并且由于一个简单的空间散列，我可以在 0.7 秒内计算一个 1024 x 1024 的图像，其中包含 70,000 个“点”——比我原来的 x64 场景快约 340000%，并且没有螺纹或内衬。因此，我已经接受了一个答案——迫切需要已经消失了，尽管我仍在研究这个问题。