Fine tune the speed/space trade-off for overallocating small lists.

The Py2.3 approach overallocated small lists by up to 8 elements.
The last checkin would limited this to one but slowed down (by 20 to 30%)
the creation of small lists between 3 to 8 elements.

This tune-up balances the two, limiting overallocation to 3 elements
(significantly reducing space consumption from Py2.3) and running faster
than the previous checkin.

The first part of the growth pattern (0, 4, 8, 16) neatly meshes with
allocators that trigger data movement only when crossing a power of two
boundary.  Also, then even numbers mesh well with common data alignments.

1 files changed, 2 insertions, 2 deletions

diff --git a/Doc/whatsnew/whatsnew24.tex b/Doc/whatsnew/whatsnew24.tex
index 1fbc298..4720eea 100644
--- a/Doc/whatsnew/whatsnew24.tex
+++ b/Doc/whatsnew/whatsnew24.tex
@@ -260,8 +260,8 @@ yellow 5
 \begin{itemize}
 
 \item The machinery for growing and shrinking lists was optimized
- for speed and for space efficiency.  Small lists (under six elements)
- never over-allocate by more than one element.  Large lists do not
+ for speed and for space efficiency.  Small lists (under eight elements)
+ never over-allocate by more than three elements.  Large lists do not
  over-allocate by more than 1/8th.  Appending and popping from lists
  now runs faster due to more efficient code paths and less frequent
  use of the underlying system realloc().  List comprehensions also