[LLVMdev] We need better hashing

[Chandler Carruth]

Jeffrey and I are working on future standard library functionality for
hashing user defined types:

http://www.open-std.org/jtc1/sc22/wg21/docs/papers/2012/n3333.html

I would much rather have an interface that is close to or mirrors this one.
We already have some field experience with it, and using it in LLVM and
Clang would provide more. Also, it would be possible to essentially share
code between such an implementation and libc++.

We looked closely at 'hasher' objects and using add methods on them and
they tended to have some serious drawbacks:

1) they require some amount of "incrementality", limiting the quality and
performance of the hashing algorithm
2) they require more boiler plate
3) they compose recursively less cleanly

Even given your interface, there is no actual requirement for an
incremental hash. Simply store intermediate state in the object, and
provide a 'finalize' step that produces the final hash code.

On Sun, Feb 12, 2012 at 4:59 PM, Talin <viridia at gmail.com> wrote:

> Here's my latest version of Hashing.h, which I propose to add to llvm/ADT.
> Comments welcome and encouraged.
>
>
> On Thu, Feb 9, 2012 at 11:23 AM, Talin <viridia at gmail.com> wrote:
>
>> By the way, the reason I'm bringing this up is that a number of folks are
>> currently working on optimizing the use of hash tables within LLVM's code
>> base, and unless we can come up with a common hashing facility, there will
>> be an increasing proliferation of cut & paste copies of hash functions. So
>> feedback would be nice.
>>
>>
>> On Tue, Feb 7, 2012 at 10:58 PM, Talin <viridia at gmail.com> wrote:
>>
>>> LLVM currently has a bunch of different hashing algorithms scattered
>>> throughout the code base.
>>>
>>> There's also a number of places in the code where a FoldingSetNodeID is
>>> created for the purpose of calculating a hash, and then discarded. From an
>>> efficiency standpoint, this isn't all that bad unless the number of
>>> individual items being hashed > 32, at which point the SmallVector
>>> overflows and memory is allocated.
>>>
>>> I personally want to see a better approach to hashing because of the
>>> cleanup work I've been doing - I've been replacing std::map and FoldingSet
>>> with DenseMap in a number of places, and plan to do more of this. The thing
>>> is, for complex key types, you really need to have a custom DenseMapInfo,
>>> and that's where having a good hash function comes in.
>>>
>>> There are a bunch of hash functions out there (FNV1, SuperFastHash, and
>>> many others). The best overall hash function that I am currently aware of
>>> is Austin Appleby's MurmurHash3 (http://code.google.com/p/smhasher/).
>>>
>>> For LLVM's use, we want a hash function that can handle mixed data -
>>> that is, pointers, ints, strings, and so on. Most of the high-performance
>>> hash functions will work well on mixed data types, but you have to put
>>> everything into a flat buffer - that is, an array of machine words whose
>>> starting address is aligned on a machine-word boundary. The inner loops of
>>> the hash functions are designed to take advantage of parallelism of the
>>> instruction pipeline, and if you try feeding in values one at a time it's
>>> possible that you can lose a lot of speed. (Although I am not an expert in
>>> this area, so feel free to correct me on this point.) On the other hand, if
>>> your input values aren't already arranged into a flat buffer, the cost of
>>> writing them to memory has to be taken into account.
>>>
>>> Also, most of the maps in LLVM are fairly small (<1000 entries), so the
>>> speed of the hash function itself is probably more important than getting
>>> the best possible mixing of bits.
>>>
>>> It seems that for LLVM's purposes, something that has an interface
>>> similar to FoldingSetNodeID would make for an easy transition. One approach
>>> would be to start with something very much like FoldingSetNodeID, except
>>> with a fixed-length buffer instead of a SmallVector - the idea is that when
>>> you are about to overflow, instead of allocating more space, you would
>>> compute an intermediate hash value and then start over at the beginning of
>>> the buffer.
>>>
>>> Another question is whether or not you would want to replace the hash
>>> functions in DenseMapInfo, which are designed to be efficient for very
>>> small keys - most of the high-performance hash functions have a fairly
>>> substantial fixed overhead (usually in the form of a final mixing step) and
>>> thus only make sense for larger key sizes.
>>>
>>> --
>>> -- Talin
>>>
>>
>>
>>
>> --
>> -- Talin
>>
>
>
>
> --
> -- Talin
>
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