Fairly significant changes to enums.c and the way it is generated. enums.c
now contains 3 static tables. The first table is a single, large string of
all the enum names. The second table is an array, sorted by enum name, of
indexes to the string table and the matching enum value. The extra string
table is used to eliminate relocs (and save space) in the compiled file.
The third table is an array, sorted by enum value, of indexes into the
second table.
The [name, enum] table contains all of the enums, but the table sorted by
enum-value does not. This table contains one entry per enum value. For
enum values that have multiple names (e.g., 0x84C0 has GL_TEXTURE0_ARB and
GL_TEXTURE0), only an index to the "best" name will appear in the table.
gl_enums.py gives precedence to "core" GL versions of names, followed by ARB
versions, followed by EXT versions, followed, finally, by vendor versions
(i.e., anything that doesn't fall into one of the previous categories). By
filtering the unneeded elements from this table, not only can we guarantee
determinism in the generated tables, but we save 364 elements in the table.
The optimizations outlined above reduced the size of the stripped enums.o
(on x86) from ~80KB to ~53KB.
The internal organization of gl_enums.py was also heavily modified.
Previously enums were stored in an unsorted list as [value, name] tuples
(basically). This list was then sorted, using a user-specified compare
function (i.e., VERY slow in most Python implementations) to generate a
table sorted by enum value. It was then sorted again, using another
user-specified compare function, to generate a table sorted by name.
Enums are now stored in a dictionary, called enum_table, with the enum value
as the key. Each dictionary element is a list of [name, priority] pairs.
The priority is determined as described above. The table sorted by enum
value is generated by sorting the keys of enum_table (i.e., very fast). The
tables sorted by name are generated by creating a list, called name_table,
of [name, enum value] pairs. This table can then be sorted by doing
name_table.sort() (i.e., very fast).
The result is a fair amount more Python code, but execution time was reduced
from ~14 seconds to ~2 seconds.
3 files changed