worker: change partition strategy to every Nth element

The only consumer of the worker pool code today is `hg update`.

Previously, the algorithm to partition work to each worker process
preserved input list ordering. We'd take the first N elements, then
the next N elements, etc. Measurements on mozilla-central demonstrate
this isn't an optimal partitioning strategy.

I added debug code to print when workers were exiting. When performing
a working copy update on a previously empty working copy of
mozilla-central, I noticed that process lifetimes were all over the
map. One worker would complete after 7s. Many would complete after
12s. And another worker would often take >16s. This behavior occurred
for many worker process counts and was more pronounced on some than
others.

What I suspect is happening is some workers end up with lots of
small files and others with large files. This is because the update
code passes in actions according to sorted filenames. And, directories
under tend to accumulate similar files. For example, test directories
often consist of many small test files and media directories contain
binary (often larger) media files.

This patch changes the partitioning algorithm to select every Nth
element from the input list. Each worker thus has a similar composition
of files to operate on.

The result of this change is that worker processes now all tend to exit
around the same time. The possibility of a long pole due to being
unlucky and receiving all the large files has been mitigated. Overall
execution time seems to drop, but not by a statistically significant
amount on mozilla-central. However, repositories with directories
containing many large files will likely show a drop.

There shouldn't be any regressions due to partial manifest decoding
because the update code already iterates the manifest to determine
what files to operate on, so the manifest should already be decoded.

mercurial/worker.py

@@ -152,14 +152,11 if os.name != 'nt':
     _exitstatus = _posixexitstatus
 
 def partition(lst, nslices):
-    '''partition a list into N slices of equal size'''
-    n = len(lst)
-    chunk, slop = n / nslices, n % nslices
-    end = 0
-    for i in xrange(nslices):
-        start = end
-        end = start + chunk
-        if slop:
-            end += 1
-            slop -= 1
-        yield lst[start:end]
+    '''partition a list into N slices of roughly equal size
+
+    The current strategy takes every Nth element from the input. If
+    we ever write workers that need to preserve grouping in input
+    we should consider allowing callers to specify a partition strategy.
+    '''
+    for i in range(nslices):
+        yield lst[i::nslices]