Raymond Hettinger added the comment:
I verified that Clang and GCC both give the expected disassembly with Serhiy's
patch. We ought to restrict the #if to just the compilers that are known to
optimize away the memcpy.
Clang (for 'BUILD_LIST_UNPACK')
-------------------------------
.loc 10 2525 9 ## Python/ceval.c:2525:9
movzwl (%r13), %r9d
addq $2, %r13
Ltmp2042:
##DEBUG_VALUE: PyEval_EvalFrameEx:next_instr <- R13
GCC (for 'BUILD_LIST_UNPACK')
-----------------------------
LM1275:
movzwl (%rdx), %r8d
LVL1147:
leaq 2(%rdx), %rbp
[Mark]
> Benchmarks showing dramatic real-world speed improvements ...
Much of the doubling of speed for core Python that has occurred over the last
ten decade has occurred one little step at a time, none of the them being
individually "dramatic". In general, if we have a chance to reduce the work
load in the ceval inner-loop, we should take it.
A simple benchmark on clang shows a roughly 10+% speedup in code exercising
simple and common opcodes that that have a oparg (there is no point of
benchmarking the effect on opcodes like IMPORT_NAME where the total eval-loop
overhead is already an insignificant proportion of the total work).
Baseline version with CLANG Apple LLVM version 7.0.2 (clang-700.1.81)
$ ./python.exe exercise_oparg.py
0.22484053499647416
$ ./python.exe exercise_oparg.py
0.22687773499637842
$ ./python.exe exercise_oparg.py
0.22026274001109414
Patched version with CLANG Apple LLVM version 7.0.2 (clang-700.1.81)
$ ./python.exe exercise_oparg.py
0.19516360601119231
$ ./python.exe exercise_oparg.py
0.20087355599389412
$ ./python.exe exercise_oparg.py
0.1980393300036667
To better isolate the effect, I suppose you could enable the READ_TIMESTAMP
macros to precisely measure the effect of converting five sequentially
dependent instructions with two independent instructions, but likely all it
would show you is that the two are cheaper than the five.
----------
Added file: http://bugs.python.org/file41289/exercise_oparg.py
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