So what did we do? We're using the fact that, you know, we have a gigaflop sitting here.

所以我们怎么做的呢？我们到了千兆次浮点运算。

Several methods have been developed to represent floating point numbers.

已经开发了几种方法来表示浮点数。

And I'm doing some floating point arithmetic, ultimately.

最，我在做一些浮点运算。

There was float, which is a real number with a decimal point.

有一个浮点数，是一个带小数点的实数。

Plus, rounding errors are inherent in floating point computations.

，舍入误差是浮点计算中固有的。

There's an infinite number of floating point real numbers in the world.

界上有无数的浮点实数。

I'll invoke instantiateEntity and then pass in a float 3, 4, 0, 4.

我将调 instantiateEntity，传入浮点数 3、4、0、4。

If it only has a finite number of bits, 32, via which to represent a float.

如果它只有有限个位， 32表示浮点数。

These are called " floating point" numbers, because the decimal point can float around in the middle of number.

这些被称为“浮点”数字，因为小数点可以在数字中间浮动。

Essentially value types like bytes, ints, floating points, simpler data types.

本质上是值类型，如字节、整数、浮点、更简单的数据类型。

Int dimension x, int dimension y, and float spacing.

整型尺寸 x、整型尺寸 y 和浮点间距。

Now inside of the curly braces, we'll set the value to a new float at coordinate 204.

现在，在大括号内，我们将把值设置为坐标 204 处的新浮点数。

Similarly, a double is like a floating point value.

类似地，double类似于浮点值。

This time I'm using floats, but let's go ahead and just run this.

这次我的是浮点数，但让我们继续运行这个。

Let's just store the float A in the first element of the native array.

我们将浮点数 A 存储在本机数组的第一个元素中。

Percent f is going to be for a floating point value.

百分比f是一个浮点值。

Or maybe floating point values, like 3.14159, or other such values.

或者是浮点值，比如3.14159，或者其他类似的值。

They are giant, inscrutable matrices of floating point numbers that we nudge in the direction of better performance until they inexplicably start working.

它们是无法参透的大型浮点数矩阵，我们在朝着更好的性能试探着前进，直到它们莫名其妙地运行起来。

It'll be a native array of floats.

它将是一个原生的浮点数组。

If we bypass their mainframe and direct route to the Homer cluster, we can clock this at around 600 teraflops.

如果我们绕过他们的主机直接进入分系统群，我们可以把运算速度保持在600浮点运算次数每秒。