如何获得 2^k 的数字的 lg2

发布于 2024-08-20 07:29:04 字数 963 浏览 5 评论 0原文

获取一个数字的以 2 为底的对数的最佳解决方案是什么,我知道这个数字是 2 的幂 (2^k)。 (当然,我只知道值 2^k 而不是 k 本身。)

我想到的一种方法是减去 1,然后进行位计数:

lg2(n) = bitcount( n - 1 ) = k, iff k is an integer
0b10000 - 1 = 0b01111, bitcount(0b01111) = 4

但是有没有更快的方法(没有缓存)?另外,不涉及位数快的东西也很高兴知道?

其中一个应用程序是:

suppose you have bitmask
0b0110111000

and value
0b0101010101

and you are interested of
(value & bitmask) >> number of zeros in front of bitmask
(0b0101010101 & 0b0110111000) >> 3 = 0b100010

this can be done with

using bitcount
value & bitmask >> bitcount((bitmask - 1) xor bitmask) - 1

or using lg2
value & bitmask >> lg2(((bitmask - 1) xor bitmask) + 1 ) - 2

它比 更快bitcount 没有缓存,它应该比 O(lg(k)) 更快,其中 k 是存储位数。

What is the best solution for getting the base 2 logarithm of a number that I know is a power of two (2^k). (Of course I know only the value 2^k not k itself.)

One way I thought of doing is by subtracting 1 and then doing a bitcount:

lg2(n) = bitcount( n - 1 ) = k, iff k is an integer
0b10000 - 1 = 0b01111, bitcount(0b01111) = 4

But is there a faster way of doing it (without caching)? Also something that doesn't involve bitcount about as fast would be nice to know?

One of the applications this is:

suppose you have bitmask
0b0110111000

and value
0b0101010101

and you are interested of
(value & bitmask) >> number of zeros in front of bitmask
(0b0101010101 & 0b0110111000) >> 3 = 0b100010

this can be done with

using bitcount
value & bitmask >> bitcount((bitmask - 1) xor bitmask) - 1

or using lg2
value & bitmask >> lg2(((bitmask - 1) xor bitmask) + 1 ) - 2

For it to be faster than bitcount without caching it should be faster than O(lg(k)) where k is the count of storage bits.

如果你对这篇内容有疑问,欢迎到本站社区发帖提问 参与讨论,获取更多帮助,或者扫码二维码加入 Web 技术交流群。

扫码二维码加入Web技术交流群

发布评论

需要 登录 才能够评论, 你可以免费 注册 一个本站的账号。

评论(4

探春 2024-08-27 07:29:04

是的。如果您知道所讨论的整数是 2 的幂,则可以在不使用 lg(n) 中的位数的情况下执行此操作。

unsigned int x = ...;
static const unsigned int arr[] = {
  // Each element in this array alternates a number of 1s equal to
  // consecutive powers of two with an equal number of 0s.
  0xAAAAAAAA, // 0b10101010..         // one 1, then one 0, ...
  0xCCCCCCCC, // 0b11001100..         // two 1s, then two 0s, ...
  0xF0F0F0F0, // 0b11110000..         // four 1s, then four 0s, ...
  0xFF00FF00, // 0b1111111100000000.. // [The sequence continues.]
  0xFFFF0000
}

register unsigned int reg = (x & arr[0]) != 0;
reg |= ((x & arr[4]) != 0) << 4;
reg |= ((x & arr[3]) != 0) << 3;
reg |= ((x & arr[2]) != 0) << 2;
reg |= ((x & arr[1]) != 0) << 1;

// reg now has the value of lg(x).

在每个 reg |= 步骤中,我们连续测试x的任何位是否与arr中的交替位掩码共享。如果是,则意味着 lg(x) 具有该位掩码中的位,并且我们有效地将 2^k 添加到 reg,其中 k 是交替位掩码长度的对数。例如,0xFF00FF00 是 8 个 1 和 0 的交替序列,因此此位掩码的 k 为 3(或 lg(8))。

本质上,每个 reg |= ((x & arr[k]) ... 步骤(以及初始赋值)测试 lg(x) 是否具有位

如果是这样 太神奇了,让我们尝试一个例子。假设我们想知道 2,048 是 2 的幂:

// x = 2048
//   = 1000 0000 0000

register unsigned int reg = (x & arr[0]) != 0;
// reg =       1000 0000 0000
         & ... 1010 1010 1010
       =       1000 0000 0000 != 0
// reg = 0x1 (1)        // <-- Matched! Add 2^0 to reg.

reg |= ((x & arr[4]) != 0) << 4;
// reg =     0x .. 0800
           & 0x .. 0000
       =              0 != 0
// reg = reg | (0 << 4) // <--- No match.
// reg = 0x1 | 0
// reg remains 0x1.

reg |= ((x & arr[3]) != 0) << 3;
// reg =     0x .. 0800
           & 0x .. FF00
       =            800 != 0
// reg = reg | (1 << 3) // <--- Matched! Add 2^3 to reg.
// reg = 0x1 | 0x8
// reg is now 0x9.         

reg |= ((x & arr[2]) != 0) << 2;
// reg =     0x .. 0800
           & 0x .. F0F0
       =              0 != 0
// reg = reg | (0 << 2) // <--- No match.
// reg = 0x9 | 0
// reg remains 0x9.        

reg |= ((x & arr[1]) != 0) << 1;
// reg =     0x .. 0800
           & 0x .. CCCC
       =            800 != 0
// reg = reg | (1 << 1) // <--- Matched! Add 2^1 to reg.
// reg = 0x9 | 0x2
// reg is now 0xb (11).

我们看到 reg 的最终值为 2^0 + 2^1 + 2^3,这确实是 11。

Yes. Here's a way to do it without the bitcount in lg(n), if you know the integer in question is a power of 2.

unsigned int x = ...;
static const unsigned int arr[] = {
  // Each element in this array alternates a number of 1s equal to
  // consecutive powers of two with an equal number of 0s.
  0xAAAAAAAA, // 0b10101010..         // one 1, then one 0, ...
  0xCCCCCCCC, // 0b11001100..         // two 1s, then two 0s, ...
  0xF0F0F0F0, // 0b11110000..         // four 1s, then four 0s, ...
  0xFF00FF00, // 0b1111111100000000.. // [The sequence continues.]
  0xFFFF0000
}

register unsigned int reg = (x & arr[0]) != 0;
reg |= ((x & arr[4]) != 0) << 4;
reg |= ((x & arr[3]) != 0) << 3;
reg |= ((x & arr[2]) != 0) << 2;
reg |= ((x & arr[1]) != 0) << 1;

// reg now has the value of lg(x).

In each of the reg |= steps, we successively test to see if any of the bits of x are shared with alternating bitmasks in arr. If they are, that means that lg(x) has bits which are in that bitmask, and we effectively add 2^k to reg, where k is the log of the length of the alternating bitmask. For example, 0xFF00FF00 is an alternating sequence of 8 ones and zeroes, so k is 3 (or lg(8)) for this bitmask.

Essentially, each reg |= ((x & arr[k]) ... step (and the initial assignment) tests whether lg(x) has bit k set. If so, we add it to reg; the sum of all those bits will be lg(x).

That looks like a lot of magic, so let's try an example. Suppose we want to know what power of 2 the value 2,048 is:

// x = 2048
//   = 1000 0000 0000

register unsigned int reg = (x & arr[0]) != 0;
// reg =       1000 0000 0000
         & ... 1010 1010 1010
       =       1000 0000 0000 != 0
// reg = 0x1 (1)        // <-- Matched! Add 2^0 to reg.

reg |= ((x & arr[4]) != 0) << 4;
// reg =     0x .. 0800
           & 0x .. 0000
       =              0 != 0
// reg = reg | (0 << 4) // <--- No match.
// reg = 0x1 | 0
// reg remains 0x1.

reg |= ((x & arr[3]) != 0) << 3;
// reg =     0x .. 0800
           & 0x .. FF00
       =            800 != 0
// reg = reg | (1 << 3) // <--- Matched! Add 2^3 to reg.
// reg = 0x1 | 0x8
// reg is now 0x9.         

reg |= ((x & arr[2]) != 0) << 2;
// reg =     0x .. 0800
           & 0x .. F0F0
       =              0 != 0
// reg = reg | (0 << 2) // <--- No match.
// reg = 0x9 | 0
// reg remains 0x9.        

reg |= ((x & arr[1]) != 0) << 1;
// reg =     0x .. 0800
           & 0x .. CCCC
       =            800 != 0
// reg = reg | (1 << 1) // <--- Matched! Add 2^1 to reg.
// reg = 0x9 | 0x2
// reg is now 0xb (11).

We see that the final value of reg is 2^0 + 2^1 + 2^3, which is indeed 11.

‘画卷フ 2024-08-27 07:29:04

如果您知道该数字是 2 的幂,则可以将其右移 (>>),直到它等于 0。右移的次数(减 1)就是您的 k

编辑:比这更快的是查找表方法(虽然你牺牲了一些空间,但不是很多)。请参阅http:// doctorinterview.com/index.html/algorithmscoding/find-the-integer-log-base-2-of-an-integer/

If you know the number is a power of 2, you could just shift it right (>>) until it equals 0. The amount of times you shifted right (minus 1) is your k.

Edit: faster than this is the lookup table method (though you sacrifice some space, but not a ton). See http://doctorinterview.com/index.html/algorithmscoding/find-the-integer-log-base-2-of-an-integer/.

揪着可爱 2024-08-27 07:29:04

许多架构都有“查找第一个”指令(bsr、clz、bfffo、cntlzw 等),这比位计数方法快得多。

Many architectures have a "find first one" instruction (bsr, clz, bfffo, cntlzw, etc.) which will be much faster than bit-counting approaches.

寒尘 2024-08-27 07:29:04

如果您不介意处理浮点数,可以使用 log(x) / log(2)。

If you don't mind dealing with floats you can use log(x) / log(2).

~没有更多了~
我们使用 Cookies 和其他技术来定制您的体验包括您的登录状态等。通过阅读我们的 隐私政策 了解更多相关信息。 单击 接受 或继续使用网站,即表示您同意使用 Cookies 和您的相关数据。
原文