Leo*_*zen 15 java math math.sqrt sqrt
我正在尝试在java上实现Fast Inverse Square Root,以加快向量规范化.但是,当我在Java中实现单精度版本时,我得到的速度与1F / (float)Math.sqrt()最初速度大致相同,然后迅速降低到速度的一半.这很有意思,因为虽然Math.sqrt使用(我推测)一个本机方法,但这涉及浮点除法,我听说它实在很慢.我计算数字的代码如下:
public static float fastInverseSquareRoot(float x){
float xHalf = 0.5F * x;
int temp = Float.floatToRawIntBits(x);
temp = 0x5F3759DF - (temp >> 1);
float newX = Float.intBitsToFloat(temp);
newX = newX * (1.5F - xHalf * newX * newX);
return newX;
}
使用一个简短的程序,我写了迭代每1600万次,然后汇总结果,重复,我得到这样的结果:
1F / Math.sqrt() took 65209490 nanoseconds.
Fast Inverse Square Root took 65456128 nanoseconds.
Fast Inverse Square Root was 0.378224 percent slower than 1F / Math.sqrt()
1F / Math.sqrt() took 64131293 nanoseconds.
Fast Inverse Square Root took 26214534 nanoseconds.
Fast Inverse Square Root was 59.123647 percent faster than 1F / Math.sqrt()
1F / Math.sqrt() took 27312205 nanoseconds.
Fast Inverse Square Root took 56234714 nanoseconds.
Fast Inverse Square Root was 105.895914 percent slower than 1F / Math.sqrt()
1F / Math.sqrt() took 26493281 nanoseconds.
Fast Inverse Square Root took 56004783 nanoseconds.
Fast Inverse Square Root was 111.392402 percent slower than 1F / Math.sqrt()
我总是得到两个速度大致相同的数字,然后是快速反向平方根节省大约60%所需时间的1F / Math.sqrt()迭代,然后进行几次迭代,快速反向平方根运行的时间大约是两倍.控制.我很困惑为什么FISR会从相同的速度 - > 60% - >慢100%,并且每次运行程序时都会发生这种情况.
编辑:上面的数据是我在eclipse中运行它.当我运行程序时,javac/java我得到完全不同的数据:
1F / Math.sqrt() took 57870498 nanoseconds.
Fast Inverse Square Root took 88206794 nanoseconds.
Fast Inverse Square Root was 52.421004 percent slower than 1F / Math.sqrt()
1F / Math.sqrt() took 54982400 nanoseconds.
Fast Inverse Square Root took 83777562 nanoseconds.
Fast Inverse Square Root was 52.371599 percent slower than 1F / Math.sqrt()
1F / Math.sqrt() took 21115822 nanoseconds.
Fast Inverse Square Root took 76705152 nanoseconds.
Fast Inverse Square Root was 263.259133 percent slower than 1F / Math.sqrt()
1F / Math.sqrt() took 20159210 nanoseconds.
Fast Inverse Square Root took 80745616 nanoseconds.
Fast Inverse Square Root was 300.539585 percent slower than 1F / Math.sqrt()
1F / Math.sqrt() took 21814675 nanoseconds.
Fast Inverse Square Root took 85261648 nanoseconds.
Fast Inverse Square Root was 290.845374 percent slower than 1F / Math.sqrt()
EDIT2:经过几次反应后,似乎速度在几次迭代后稳定下来,但它稳定的数字是高度不稳定的.任何人都知道为什么？
这是我的代码(不完全简洁,但这里是整个事情):
public class FastInverseSquareRootTest {
public static FastInverseSquareRootTest conductTest() {
float result = 0F;
long startTime, endTime, midTime;
startTime = System.nanoTime();
for (float x = 1F; x < 4_000_000F; x += 0.25F) {
result = 1F / (float) Math.sqrt(x);
}
midTime = System.nanoTime();
for (float x = 1F; x < 4_000_000F; x += 0.25F) {
result = fastInverseSquareRoot(x);
}
endTime = System.nanoTime();
return new FastInverseSquareRootTest(midTime - startTime, endTime
- midTime);
}
public static float fastInverseSquareRoot(float x) {
float xHalf = 0.5F * x;
int temp = Float.floatToRawIntBits(x);
temp = 0x5F3759DF - (temp >> 1);
float newX = Float.intBitsToFloat(temp);
newX = newX * (1.5F - xHalf * newX * newX);
return newX;
}
public static void main(String[] args) throws Exception {
for (int i = 0; i < 7; i++) {
System.out.println(conductTest().toString());
}
}
private long controlDiff;
private long experimentalDiff;
private double percentError;
public FastInverseSquareRootTest(long controlDiff, long experimentalDiff) {
this.experimentalDiff = experimentalDiff;
this.controlDiff = controlDiff;
this.percentError = 100D * (experimentalDiff - controlDiff)
/ controlDiff;
}
@Override
public String toString() {
StringBuilder sb = new StringBuilder();
sb.append(String.format("1F / Math.sqrt() took %d nanoseconds.%n",
controlDiff));
sb.append(String.format(
"Fast Inverse Square Root took %d nanoseconds.%n",
experimentalDiff));
sb.append(String
.format("Fast Inverse Square Root was %f percent %s than 1F / Math.sqrt()%n",
Math.abs(percentError), percentError > 0D ? "slower"
: "faster"));
return sb.toString();
}
}
Dan*_*her 11
JIT优化器似乎已经把这个电话扔掉了Math.sqrt.
使用未经修改的代码,我得到了
1F / Math.sqrt() took 65358495 nanoseconds.
Fast Inverse Square Root took 77152791 nanoseconds.
Fast Inverse Square Root was 18,045544 percent slower than 1F / Math.sqrt()
1F / Math.sqrt() took 52872498 nanoseconds.
Fast Inverse Square Root took 75242075 nanoseconds.
Fast Inverse Square Root was 42,308531 percent slower than 1F / Math.sqrt()
1F / Math.sqrt() took 23386359 nanoseconds.
Fast Inverse Square Root took 73532080 nanoseconds.
Fast Inverse Square Root was 214,422951 percent slower than 1F / Math.sqrt()
1F / Math.sqrt() took 23790209 nanoseconds.
Fast Inverse Square Root took 76254902 nanoseconds.
Fast Inverse Square Root was 220,530610 percent slower than 1F / Math.sqrt()
1F / Math.sqrt() took 23885467 nanoseconds.
Fast Inverse Square Root took 74869636 nanoseconds.
Fast Inverse Square Root was 213,452678 percent slower than 1F / Math.sqrt()
1F / Math.sqrt() took 23473514 nanoseconds.
Fast Inverse Square Root took 73063699 nanoseconds.
Fast Inverse Square Root was 211,260168 percent slower than 1F / Math.sqrt()
1F / Math.sqrt() took 23738564 nanoseconds.
Fast Inverse Square Root took 71917013 nanoseconds.
Fast Inverse Square Root was 202,954353 percent slower than 1F / Math.sqrt()
时间一直较慢fastInverseSquareRoot,而且时间都在同一个球场,同时Math.sqrt呼叫也大大增加.
更改代码以便Math.sqrt无法避免调用,
for (float x = 1F; x < 4_000_000F; x += 0.25F) {
result += 1F / (float) Math.sqrt(x);
}
midTime = System.nanoTime();
for (float x = 1F; x < 4_000_000F; x += 0.25F) {
result -= fastInverseSquareRoot(x);
}
endTime = System.nanoTime();
if (result == 0) System.out.println("Wow!");
我有
1F / Math.sqrt() took 184884684 nanoseconds.
Fast Inverse Square Root took 85298761 nanoseconds.
Fast Inverse Square Root was 53,863804 percent faster than 1F / Math.sqrt()
1F / Math.sqrt() took 182183542 nanoseconds.
Fast Inverse Square Root took 83040574 nanoseconds.
Fast Inverse Square Root was 54,419278 percent faster than 1F / Math.sqrt()
1F / Math.sqrt() took 165269658 nanoseconds.
Fast Inverse Square Root took 81922280 nanoseconds.
Fast Inverse Square Root was 50,431143 percent faster than 1F / Math.sqrt()
1F / Math.sqrt() took 163272877 nanoseconds.
Fast Inverse Square Root took 81906141 nanoseconds.
Fast Inverse Square Root was 49,834815 percent faster than 1F / Math.sqrt()
1F / Math.sqrt() took 165314846 nanoseconds.
Fast Inverse Square Root took 81124465 nanoseconds.
Fast Inverse Square Root was 50,927296 percent faster than 1F / Math.sqrt()
1F / Math.sqrt() took 164079534 nanoseconds.
Fast Inverse Square Root took 80453629 nanoseconds.
Fast Inverse Square Root was 50,966689 percent faster than 1F / Math.sqrt()
1F / Math.sqrt() took 162350821 nanoseconds.
Fast Inverse Square Root took 79854355 nanoseconds.
Fast Inverse Square Root was 50,813704 percent faster than 1F / Math.sqrt()
更慢的时间Math.sqrt,并且只有适度慢的时间fastInverseSqrt(现在它必须在每次迭代中进行减法).