I recently downloaded Ronald Doussoren's universal binary installer
for MacPython. This installed fine on my Macbook Pro, and the
resulting python version was able to build most of my favorite
extensions (except PyOpenGL, which I can't get to build on any Mac
recently - I'll post a separate issue about that.).
I also compiled the source from the universal svn tree (revision 41).
This required that I edit
Lib/distutils/unixccompiler.py as follows:
--- python/Lib/distutils/unixccompiler.py (revision 41)
+++ python/Lib/distutils/unixccompiler.py (working copy)
@@ -42,8 +42,11 @@
# should just happily stuff them into the preprocessor/compiler/
linker
# options and carry on.
+
def _darwin_compiler(compiler_so, cc_args):
compiler_so = list(compiler_so)
+ stripArch=0
+ stripSysroot=0
if os.uname()[2] < '8.':
stripArch = stripSysroot = 1
This just clears up a bug where some variables can end up undefined
if an if condition comes up false. After this modification, the
source complied fine with --enable-framework. I didn't use --enable-
universal-sdk, so I guess I compiled an Intel-only version of the
framework. I then built some extensions for this version also.
My reason to comment here has to do with the relevant performance of
the Universal vs locally compiled pythons. In particular, I make
heavy use of numarray, so I have a standard benchmark that tests many
of the most computation intensive numarray routines with a variety of
different data types. According to this benchmark, I'm getting much
(>3X) better performance out of the local version than out of the
Universal one.
For both python frameworks, I built numarray 1.5.1 using the basic
"python setup.py install" (starting with clean source). The
benchmarks I got were (in seconds to completion) about 24 seconds for
the Universal, and only 7.2 seconds for the locally compiled python.
For comparison, the older PPC only MacPython 2.4.1, with numarray
installed via the included package manager took 32.6 seconds.
The native code on the MacBook compares very well to other machines.
Native code on my dual G5 takes 8.4 seconds on this task. The only
machine I've seen that's as fast as this MacBook was an SGI Altix 330
(Itanium 2), and even it wasn't any faster. This makes me pretty
happy about the Intel Core Duo, but somewhat worried about Universal
binaries (in general, but for python in particular) since the binary
seems closer in performance to rosetta than to native code.
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