The patch implements the current state of PEP-456 plus a configure option to select the hash algorithm. I have tested it only on 64bit Linux so far.

Here is a simple benchmark (Linux, gcc 4.7.3):

$ ./python -m timeit -s "words=[w for line in open('LICENSE') for w in line.split()]; import collections" "c = collections.Counter(words); c.most_common(10)"

• 64-bit build, before: 313 usec per loop

• 64-bit build, after: 298 usec per loop

• 32-bit build, before: 328 usec per loop

• 32-bit build, before: 329 usec per loop

• x32 build, before: 291 usec per loop

• x32 build, after: 284 usec per loop

Microbenchmarking hash computation (Linux, gcc 4.7.3):

• Short strings:
  python -m timeit -s "b=b'x'*20" "hash(memoryview(b))"

• 64-bit build, before: 0.263 usec per loop

• 64-bit build, after: 0.263 usec per loop

• 32-bit build, before: 0.303 usec per loop

• 32-bit build, after: 0.358 usec per loop

• Long strings:
  python -m timeit -s "b=b'x'*1000" "hash(memoryview(b))"

• 64-bit build, before: 1.56 usec per loop

• 64-bit build, after: 1.03 usec per loop

• 32-bit build, before: 1.61 usec per loop

• 32-bit build, after: 2.46 usec per loop

Overall, performance looks fine to me.

Your benchmark is a bit unrealistic because it times the hash cache most of the time. Here is a better benchmark (but bytes-only):

$ ./python -m timeit -s "words=[w.encode('utf-8') for line in open('../LICENSE') for w in line.split()]; import collections" -- "c = collections.Counter(memoryview(w) for w in words); c.most_common(10)"
1000 loops, best of 3: 1.63 msec per loop

This increases the number of hash calculations from about 28k to over 8.4 mio.

I also added a little statistic function to see how large typical string are. The artificial benchmark:

hash 1: 115185
hash 2: 1440956
hash 3: 1679976
hash 4: 873769
hash 5: 948124
hash 6: 651799
hash 7: 676707
hash 8: 545459
hash 9: 523615
hash 10: 421232
hash 11: 161641
hash 12: 140797
hash 13: 86826
hash 14: 41702
hash 15: 41570
hash 16: 332
hash 17: 211
hash 18: 4275
hash 19: 205
hash 20: 131
hash 21: 4197
hash 22: 70
hash 23: 35
hash 24: 44
hash 25: 4145
hash 26: 4137
hash 27: 4137
hash 28: 21
hash 29: 4124
hash 30: 8
hash 31: 5
hash 32: 1
hash other: 28866
hash total: 8404302

And here is the statistic of a full test run.

hash 1: 18935
hash 2: 596761
hash 3: 643973
hash 4: 645399
hash 5: 576231
hash 6: 742531
hash 7: 497214
hash 8: 330890
hash 9: 291301
hash 10: 93206
hash 11: 1417900
hash 12: 160802
hash 13: 58675
hash 14: 49324
hash 15: 48068
hash 16: 90634
hash 17: 24163
hash 18: 66079
hash 19: 23408
hash 20: 20695
hash 21: 16424
hash 22: 17236
hash 23: 59135
hash 24: 10368
hash 25: 6047
hash 26: 6784
hash 27: 5565
hash 28: 5931
hash 29: 3469
hash 30: 4220
hash 31: 2652
hash 32: 2911
hash other: 72042
hash total: 6608973

About 50% of the hashed elements are between 1 and 5 characters long. A realistic hash collision attack on 32bit needs at least 7, 8 chars. I see the chance of a micro-optimization! :)

Your benchmark is a bit unrealistic because it times the hash cache
most of the time. Here is a better benchmark (but bytes-only):

$ ./python -m timeit -s "words=[w.encode('utf-8') for line in
open('../LICENSE') for w in line.split()]; import collections" -- "c
= collections.Counter(memoryview(w) for w in words);
c.most_common(10)"
1000 loops, best of 3: 1.63 msec per loop

Good point. Can you also post all benchmark results?

unmodified Python:

1000 loops, best of 3: 307 usec per loop (unicode)
1000 loops, best of 3: 930 usec per loop (memoryview)

SipHash:

1000 loops, best of 3: 300 usec per loop (unicode)
1000 loops, best of 3: 906 usec per loop (memoryview)

New changeset c960bed22bf6 by Christian Heimes in branch 'default':
Make Nick BDFG delegate
http://hg.python.org/peps/rev/c960bed22bf6

I propose extract all hash related stuff from Include/object.h in separated file Include/pyhash.h. And perhaps move Objects/hash.c to Python/pyhash.c.

Since hash algorithm determined at compile time, the _Py_HashSecret_t structure and the _Py_HashSecret function are redundant. We need define only the _Py_HashBytes function.

Currently SipHash algorithm doesn't work with unaligned data.

Sure it does. The test for unaligned hashing passes without an error or a segfault.

And note that the quality of the FNV hash function is reduced (msg186403). We need "shuffle" result's bits.

The test for unaligned hashing passes without an error or a segfault.

On some platforms it can work without a segfault.

Added some structural comments to the patch. I'll defer to Serhiy when it comes to assessing the algorithm details :)

I have created a clone for PEP-456 and applied your suggestions. I'm still looking for a nice API to handle the hash definition. Do you have some suggestions?

Nick, can you do another review? All tests should pass on common boxes.

The latest code hides the struct with the hash function. I have added a configure block that detects platforms that don't support unaligned memory access. It works correctly on the SPARC Solaris 10 box.

I'm still looking for a 64bit big endian box and a 32bit big endian box that support unaligned memory.

I'm still looking for a 64bit big endian box

Have you tried the PPC64 PowerLinux box? It's in the stable buildbots for a reason :-)

I can no longer find the configuration for custom path. It's still documented but there is no field for "repo path".

http://buildbot.python.org/all/buildslaves/edelsohn-powerlinux-ppc64

I can no longer find the configuration for custom path. It's still documented but there is no field for "repo path".

http://buildbot.python.org/all/builders/PPC64%20PowerLinux%20custom

(usually, just replace "3.x" with "custom" in the URL)

Nick, please review the latest patch. I have addressed Antoine's review in 257597d20fa8.diff. I'll update the PEP as soon as you are happy with the patch.

I suggest move to Include/pyhash.h and Python/pyhash.c only _Py_HashBytes() and string hash algorithm related constants, and don't touch PyObject_Hash(), _Py_HashDouble(), etc. So if anybody want change string hashing algorithm, it need only replace these two minimal files.

PyObject_Hash() and PyObject_HashNotImplemented() should not have been moved to pyhash.h. But the other internal helper methods should be kept together.

On 27 Oct 2013 23:46, "Christian Heimes" <report@bugs.python.org> wrote:

Christian Heimes added the comment:

Nick, please review the latest patch. I have addressed Antoine's review
in 257597d20fa8.diff. I'll update the PEP as soon as you are happy with the
patch.

Comments from the others sound promising, I should be able to take a look
myself tomorrow night.

Christian, why PY_HASH_EXTERNAL is here? Do you plan use it any official build? I think that in custom build of Python whole files pyhash.c and pyhash.h can be replaced.

When you will get rid from PY_HASH_EXTERNAL, then you could get rid from PyHash_FuncDef, PyHash_Func, etc.

Why _Py_HashDouble() and _Py_HashPointer() are moved to pyhash.c? They are hash algorithm agnostic, and it is unlikely they will be redefined in custom build.

You not need the HAVE_ALIGNED_REQUIRED macros if use PY_UHASH_CPY (or something like for exact 64 bit) in siphash24. On platforms where aligned access is required you will use per-bytes copy, otherwise you will use fast 64-bit copy.

Am 28.10.2013 16:18, schrieb Serhiy Storchaka:

Christian, why PY_HASH_EXTERNAL is here? Do you plan use it any official build? I think that in custom build of Python whole files pyhash.c and pyhash.h can be replaced.

Because you can't simple replace the files. It also contains
_Py_HashBytes() and _PyHash_Fini(). With PY_HASH_EXTERNAL embedders can
simply define PY_HASH_ALGORITHM PY_HASH_EXTERNAL and provide the extern
struct inside a separate object file.

When you will get rid from PY_HASH_EXTERNAL, then you could get rid from PyHash_FuncDef, PyHash_Func, etc.

I don't understand why you want me to get rid of the struct. What's your
argument against the struct? I like the PyHash_FuncDef because it groups
all information (func ptr, name, hash metadata) in a single structure.

Why _Py_HashDouble() and _Py_HashPointer() are moved to pyhash.c? They are hash algorithm agnostic, and it is unlikely they will be redefined in custom build.

I have moved the functions to pyhash.c in order to keep all related
internal function in one file. They do not belong in Objects/object.c.

You not need the HAVE_ALIGNED_REQUIRED macros if use PY_UHASH_CPY (or something like for exact 64 bit) in siphash24. On platforms where aligned access is required you will use per-bytes copy, otherwise you will use fast 64-bit copy.

I'm not going to make siphash24 compatible with platforms that require
aligned memory for integers. It's an unnecessary complication and
slow-down for all common platforms. The feature will simply not be
available on archaic architectures.

Seriously, nobody gives a ... about SPARC and MIPS. :) It's nice that
Python still works on these CPU architectures. But I neither want to
deviate from the SipHash24 implementation nor make the code slower on
all relevant platforms such as X86 and X86_64.

Serhiy, I would like to land my patch before beta 1 hits the fan. We can always improve the code during beta. Right now I don't want to mess around with SipHash24 code. That includes non-64bit platforms as well as architectures that enforce aligned memory for integers.

Seriously, nobody gives a ... about SPARC and MIPS. :) It's nice that
Python still works on these CPU architectures. But I neither want to
deviate from the SipHash24 implementation nor make the code slower on
all relevant platforms such as X86 and X86_64.

Well, unaligned memory access is usually slower on all architectures :-)
Also, I think some ARM architectures don't support unaligned access, so
it's not really a thing of the past...

Am 28.10.2013 16:59, schrieb Charles-François Natali:

Well, unaligned memory access is usually slower on all architectures :-)
Also, I think some ARM architectures don't support unaligned access, so
it's not really a thing of the past...

On modern computers it's either not slower or just a tiny bit slower.
http://lemire.me/blog/archives/2012/05/31/data-alignment-for-speed-myth-or-reality/

Python's str and bytes datatype are always aligned properly. The
majority of bytearray and memoryview instances are aligned, too.
Unaligned memory access is rare case for most applications. About 50% of
strings have less than 8 bytes (!), 90% have less than 16 bytes. For the
Python's test suite the numbers are even smaller: ~45% <=5 bytes, ~90%
<=12 bytes.

You might see a 10% slowdown for very long and unaligned byte arrays on
some older CPUs. I think we can safely ignore the special case. Any
special case for unaligned memory will introduce additional overhead
that *will* slow down the common path.

Oh, and ARM has unaligned memory access:
http://infocenter.arm.com/help/index.jsp?topic=/com.arm.doc.ddi0360f/CDFEJCBH.html

To support Windows 32 bit, the following code in PC/pyconfig.h can be modified to use __int64 or _W64: see ssize_t definition below in the same file.

#ifndef PY_UINT64_T
#if SIZEOF_LONG_LONG == 8
#define HAVE_UINT64_T 1
#define PY_UINT64_T unsigned PY_LONG_LONG
#endif
#endif

Victor:
I have added the licence to Doc/licence.rst and created a new ticket for PY_UINT64_T on Windows bpo-19433.

Nick:
The memory layout of the hash secret is now documented. I have renamed the members to reflect their purpose, too. http://hg.python.org/features/pep-456/file/f0a7e606c2d0/Include/pyhash.h#l32

I'll update my PEP shortly and address the memory layout of _Py_HashSecret_t, the small string hashing optimization and performance/memory alignment.

About memcpy(). Here is sample file. Compile it to assembler:

gcc -O2 -S -masm=intel fnv.c

With memcpy() main loop is compiled to:

.L3:
mov esi, DWORD PTR [ebx]
imul eax, eax, 1000003
add ebx, 4
xor eax, esi
sub ecx, 1
mov DWORD PTR [esp+24], esi
jne .L3

With per-byte copy it is compiled to:

.L3:
mov dl, BYTE PTR [ecx]
imul eax, eax, 1000003
sub ebp, 1
movzx ebx, BYTE PTR [ecx+1]
movzx edi, BYTE PTR [ecx+2]
movzx esi, BYTE PTR [ecx+3]
add ecx, 4
mov dh, bl
sal edi, 16
movzx edx, dx
sal esi, 24
or edx, edi
or edx, esi
xor eax, edx
cmp ebp, -1
jne .L3

I had to add the conversion from LE to host endianess. The missing conversion was affecting and degrading hash value dispersion.

Hi Nick,

I have updated the patch and the PEP text. The new version has small string hash optimization disabled. The other changes are mostly cleanup, reformatting and simplifications.

Can you please do a review so I can get the patch into 3.4 before beta1 is released?

I reviewed the latest PEP text at http://www.python.org/dev/peps/pep-0456/

I'm almost prepared to accept the current version of the implementation, but there's one technical decision to be clarified and a few placeholders in the PEP that need to be cleaned up prior to formal acceptance:

• The rationale for turning off the small string optimisation by default rather than setting the cutoff to 7 bytes isn't at all clear to me. A consistent 3-5% speed difference on the benchmark suite isn't trivial, and if we have the small string optimization off by default, why aren't we just deleting that code instead?

• A link to the benchmark suite at http://hg.python.org/benchmarks should be included at the appropriate places in the PEP

• The "Further things to consider" section needs to be moved to a paragraph under "Discussion" describing the current implementation (i.e. the hash equivalence is tolerated for simplicity and consistency)

• The "TBD" in the performance section needs to go. Reference should be made to the numbers in the small string optimisation section.

• The performance numbers need to be clear on what version of the feature branch was used to obtain them (preferably the one you plan to commit!).

Here are benchmarks on two Linux machine. It looks like SipHash24 takes advantage of newer CPUs. I'm a bit puzzled about the results. Or maybe my super simple and naive analyzer doesn't give sensible results...

https://bitbucket.org/tiran/pep-456-benchmarks/

Thanks - are those numbers with the current feature branch, and hence no small string optimization?

To be completely clear, I'm happy to accept a performance penalty to fix the hash algorithm. I'd just like to know exactly how big a penalty I'm accepting, and whether taking advantage of the small string optimization makes it measurably smaller.

For the record, it's better to use a geometric mean when agregating benchmark results into a single score.

So, amusingly, Christian's patch seems to be 4-5% faster than vanilla on many benchmarks here (Sandy Bridge Core i5, 64-bit, gcc 4.8.1). A couple of benchmarks are a couple % slower, but nothing severe.

This without the small strings optimization.

On top of that, the small strings optimization seems to have varying effects, some positive, some negative, so it doesn't seem to be desirable (on this platform anyway).

Benchmark report (without the small strings optimization):

http://bpaste.net/show/UohtA8dmSREbrtsJYfTI/

The numbers are between cpython default tip and my feature branch. I have pulled and merged all upstream changes into my feature branch yesterday. The results with "sso" in the file name are with small string optimization.

Performance greatly depends on compiler and CPU. In general SipHash24 is faster on modern compilers and modern CPUs. MSVC generates slower code for SipHash24 but I haven't optimized the code for MSVC yet. Perhaps Serhiy is able to do his magic. :)

PS: I have uploaded more benchmarks. The directories contain the raw output and CSV files.

The PEP should be ready now. I have addressed your input in http://hg.python.org/peps/rev/fbe779221a7a

New changeset adb471b9cba1 by Christian Heimes in branch 'default':
ssue bpo-19183: Implement PEP-456 'secure and interchangeable hash algorithm'.
http://hg.python.org/cpython/rev/adb471b9cba1

New changeset 422ed27b62ce by Christian Heimes in branch 'default':
Issue bpo-19183: test_gdb's test_dict was failing on some machines as the order or dict keys has changed again.
http://hg.python.org/cpython/rev/422ed27b62ce

New changeset 11cb1c8faf11 by Victor Stinner in branch 'default':
Issue bpo-19183: Fix repr() tests of test_gdb, hash() is now platform dependent
http://hg.python.org/cpython/rev/11cb1c8faf11

Not only test_gdb relies on repr() exact value, there is also test_functools:

http://buildbot.python.org/all/builders/AMD64%20OpenIndiana%203.x/builds/6875/steps/test/logs/stdio

======================================================================
FAIL: test_repr (test.test_functools.TestPartialC)
----------------------------------------------------------------------

Traceback (most recent call last):
  File "/export/home/buildbot/64bits/3.x.cea-indiana-amd64/build/Lib/test/test_functools.py", line 174, in test_repr
    repr(f))
AssertionError: 'func[51 chars]88>, b=<object object at 0xffffdd7fff790440>, [36 chars]00>)' != 'func[51 chars]88>, a=<object object at 0xffffdd7fff790400>, [36 chars]40>)'
- functools.partial(<function capture at 0xffffdd7ffe64d788>, b=<object object at 0xffffdd7fff790440>, a=<object object at 0xffffdd7fff790400>)
+ functools.partial(<function capture at 0xffffdd7ffe64d788>, a=<object object at 0xffffdd7fff790400>, b=<object object at 0xffffdd7fff790440>)

======================================================================
FAIL: test_repr (test.test_functools.TestPartialCSubclass)
----------------------------------------------------------------------

Traceback (most recent call last):
  File "/export/home/buildbot/64bits/3.x.cea-indiana-amd64/build/Lib/test/test_functools.py", line 174, in test_repr
    repr(f))
AssertionError: 'Part[49 chars]88>, b=<object object at 0xffffdd7fff790540>, [36 chars]00>)' != 'Part[49 chars]88>, a=<object object at 0xffffdd7fff790500>, [36 chars]40>)'
- PartialSubclass(<function capture at 0xffffdd7ffe64d788>, b=<object object at 0xffffdd7fff790540>, a=<object object at 0xffffdd7fff790500>)
+ PartialSubclass(<function capture at 0xffffdd7ffe64d788>, a=<object object at 0xffffdd7fff790500>, b=<object object at 0xffffdd7fff790540>)

New changeset 961d832d8734 by Christian Heimes in branch 'default':
Issue bpo-19183: too many tests depend on the sort order of repr().
http://hg.python.org/cpython/rev/961d832d8734

The problems have been resolved.

New changeset eec4758e3a45 by Victor Stinner in branch 'default':
Issue bpo-19183: Simplify test_gdb
http://hg.python.org/cpython/rev/eec4758e3a45