Merge #10577: Add an explanation of quickly hashing onto a non-power of two range.
dd869c6 Add an explanation of quickly hashing onto a non-power of two range. (Gregory Maxwell) Tree-SHA512: 8b362e396206a4ee2e825908dcff6fe4525c12b9c85a6e6ed809d75f03d42edcfba5e460a002e5d17cc70c103792f84d99693563b638057e4e97946dd1d800b2
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@ -206,6 +206,37 @@ private:
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/** compute_hashes is convenience for not having to write out this
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* expression everywhere we use the hash values of an Element.
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*
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* We need to map the 32-bit input hash onto a hash bucket in a range [0, size) in a
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* manner which preserves as much of the hash's uniformity as possible. Ideally
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* this would be done by bitmasking but the size is usually not a power of two.
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*
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* The naive approach would be to use a mod -- which isn't perfectly uniform but so
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* long as the hash is much larger than size it is not that bad. Unfortunately,
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* mod/division is fairly slow on ordinary microprocessors (e.g. 90-ish cycles on
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* haswell, ARM doesn't even have an instruction for it.); when the divisor is a
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* constant the compiler will do clever tricks to turn it into a multiply+add+shift,
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* but size is a run-time value so the compiler can't do that here.
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*
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* One option would be to implement the same trick the compiler uses and compute the
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* constants for exact division based on the size, as described in "{N}-bit Unsigned
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* Division via {N}-bit Multiply-Add" by Arch D. Robison in 2005. But that code is
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* somewhat complicated and the result is still slower than other options:
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*
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* Instead we treat the 32-bit random number as a Q32 fixed-point number in the range
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* [0,1) and simply multiply it by the size. Then we just shift the result down by
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* 32-bits to get our bucket number. The results has non-uniformity the same as a
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* mod, but it is much faster to compute. More about this technique can be found at
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* http://lemire.me/blog/2016/06/27/a-fast-alternative-to-the-modulo-reduction/
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*
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* The resulting non-uniformity is also more equally distributed which would be
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* advantageous for something like linear probing, though it shouldn't matter
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* one way or the other for a cuckoo table.
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*
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* The primary disadvantage of this approach is increased intermediate precision is
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* required but for a 32-bit random number we only need the high 32 bits of a
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* 32*32->64 multiply, which means the operation is reasonably fast even on a
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* typical 32-bit processor.
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*
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* @param e the element whose hashes will be returned
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* @returns std::array<uint32_t, 8> of deterministic hashes derived from e
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*/
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