Update of /cvsroot/fink/dists/10.4/unstable/main/finkinfo/libs/pythonmods
In directory sc8-pr-cvs17:/tmp/cvs-serv15748/main/finkinfo/libs/pythonmods
Modified Files:
Tag: pangocairo-branch
pycurl-py.info
Added Files:
Tag: pangocairo-branch
parallel-py.info
Log Message:
missing stuff from HEAD
Index: pycurl-py.info
===================================================================
RCS file:
/cvsroot/fink/dists/10.4/unstable/main/finkinfo/libs/pythonmods/pycurl-py.info,v
retrieving revision 1.1.2.5
retrieving revision 1.1.2.6
diff -u -d -r1.1.2.5 -r1.1.2.6
--- pycurl-py.info 15 Jul 2007 15:10:39 -0000 1.1.2.5
+++ pycurl-py.info 19 Jul 2007 22:23:31 -0000 1.1.2.6
@@ -1,7 +1,7 @@
Info2: <<
Package: pycurl-py%type_pkg[python]
Version: 7.16.4
-Revision: 2
+Revision: 1
Distribution: (%type_pkg[python] = 23) 10.3, (%type_pkg[python] = 23) 10.4
Type: python (2.3 2.4 2.5)
Description: Python bindings for libcurl
--- NEW FILE: parallel-py.info ---
Info2: <<
Package: parallel-py%type_pkg[python]
Version: 1.4.3
Revision: 1
Type: python (2.3 2.4 2.5)
Source: http://www.parallelpython.com/downloads/pp/pp-%v.tar.bz2
Source-MD5: b1c714500ce6f2a3929d5719be71b2fe
Depends: python%type_pkg[python]
CompileScript: <<
python%type_raw[python] setup.py build
<<
InstallScript: <<
python%type_raw[python] setup.py install --prefix %p --root %d
<<
DocFiles: README PKG-INFO
Description: Simple parallel processing for Python
DescDetail: <<
parallel-python is a python module which provides mechanism for parallel
execution of python code on SMP (systems with multiple processors or cores)
and clusters (computers connected via network). It is light, easy to install
and integrate with other python software.
parallel-python is an open source and cross-platform module written in pure
python.
Features:
* Parallel execution of python code on SMP and clusters
* Easy to understand and implement job-based parallelization technique (easy
to convert serial application in parallel)
* Automatic detection of the optimal configuration (by default the number of
worker processes is set to the number of the effective processors in the
system)
* Dynamic processors allocation (number of worker processes can be changed
at runtime)
* Low overhead for subsequent jobs with the same function (transparent
caching is implemented to decrease the overhead)
* Dynamic load balancing (jobs are distributed between processors at runtime)
* SHA based authentication for network connections
* Cross-platform portability (Windows, Linux, Unix)
<<
License: BSD
Homepage: http://www.parallelpython.com/
Maintainer: Pepe Barbe <[EMAIL PROTECTED]>
<<
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