Zach Byrne added the comment:
Hi, I'm new. I wrote a test for nested directories under cgi-bin and got that
to pass without failing the test added for 19435 by undoing most of the changes
to run_cgi() but building path from the values in self.cgi_info. Thoughts?
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keywords: +patch
Zach Byrne added the comment:
Done and done.
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Zach Byrne added the comment:
So I'm trying something pretty similar to Victor's pseudo-code and just using
timeit to look for speedups
timeit('x+x', 'x=10', number=1000)
before:
1.193423141393
1.1988609210002323
1.1998214110003573
1.206968028999654
1.2065417159997196
after
Zach Byrne added the comment:
Well, dont' I feel silly. I confirmed both my regression and the inline speedup
using the benchmark Victor added. I wonder if I got my binaries backwards in my
first test...
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Zach Byrne added the comment:
I did something similar to BINARY_SUBSCR after looking at the 2.7 source as
Raymond suggested. Hopefully I got my binaries straight this time :) The new
patch includes Victor's inlining and my new subscript changes.
Platform of campaign orig:
Python version
Zach Byrne added the comment:
I ran the whole benchmark suite. There are a few that are slower:
call_method_slots, float, pickle_dict, and unpack_sequence.
Report on Linux zach-vbox 3.2.0-24-generic-pae #39-Ubuntu SMP Mon May 21
18:54:21 UTC 2012 i686 i686
Total CPU cores: 1
### 2to3
Zach Byrne added the comment:
I haven't looked at it since I posted the benchmark results for 21955_2.patch.
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Zach Byrne added the comment:
Anybody still looking at this? I can take another stab at it if it's still in
scope.
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Zach Byrne added the comment:
> Can you figure why unpack_sequence and other benchmarks were slower?
I didn't look really closely, A few of the slower ones were floating point
heavy, which would incur the slow path penalty, but I can dig into
unpack_seque
Zach Byrne added the comment:
I'm attaching output from a selection of the benchmarks, I'm counting
non-builtins and slices, but for everything, not just lists and tuples.
Quick observation: math workloads seem list heavy, text workloads seem dict
heavy, and tuples are usually somewhere
Zach Byrne added the comment:
Ok, I've started on the instrumenting, thanks for that head start, that would
have taken me a while to figure out where to call the stats dump function from.
Fun fact: BINARY_SUBSCR is called 717 starting python
Zach Byrne added the comment:
I'll put together something comprehensive in a bit, but here's a quick preview:
$ ./python
Python 3.6.0a0 (default, Feb 4 2016, 20:08:03)
[GCC 4.6.3] on linux
Type "help", "copyright", "credits" or "license" for more inf
Zach Byrne added the comment:
One thing I forgot to do was count slices.
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Zach Byrne added the comment:
I took another look at this, and tried applying it to 3.6 and running the
latest benchmarks. It applied cleanly, and the benchmark results were similar,
this time unpack_sequence and spectral_norm were slower. Spectral norm makes
sense, it's doing lots of FP
Zach Byrne added the comment:
Yury,
Are you going to tackle this one, or would you like me to?
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Zach Byrne added the comment:
> Could you please take a look at the updated patch?
Looks ok to me, for whatever that's worth.
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Zach Byrne added the comment:
> I don't understand what this table means (why 4 columns?). Can you explain
> what you did?
Yury suggested running perf.py twice with the binaries swapped
So "faster" and "slower" underneath "Baseline Reference" are runs
Zach Byrne added the comment:
I ran 6 benchmarks on my work machine(not the same one as the last set)
overnight.
Two with just the BINARY_ADD change, two with the BINARY_SUBSCR change, and two
with both.
I'm attaching the output from all my benchmark runs, but here are the highlights
Zach Byrne added the comment:
The new patch "subscr2" removes the tuple block, and addresses Victor's
comments. This one looks a little faster, down to 0.0215 usec for the same test.
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Added file: http://bugs.python.org/file42049/sub
Zach Byrne added the comment:
Here's a patch that looks likes Victor's from the duplicate, but with tuples
covered as well. I ran some straight forward micro benchmarks but haven't
bothered running the benchmark suite yet. Unsurprisingly, optimized paths are
faster, and the others take
Zach Byrne added the comment:
Is it worth handling the exception, or just let it take the slow path and get
caught by PyObject_GetItem()? We're still making sure the index is in bounds.
Also, where would be an appropriate place to put a macro for adjusting negative
indices
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