llvm/tools/opt-viewer/optpmap.py
Adam Nemet a5a5f8ef6f [opt-viewer] Move under tools, install it
We weren't installing opt-viewer and co before, this fixes the omission.  I am
also moving the tools from utils/ to tools/.  I believe that this is more
appropriate since these tools have matured greatly in the past year through
contributions by multiple people (thanks!) so they are ready to become
external tools.

The tools are installed under <install>/share/opt-viewer/.

I am *not* adding the llvm- prefix.  If people feel strongly about adding
that, this is probably a good time since the new location will require some
mental adjustment anyway.

Fixes PR33521

Differential Revision: https://reviews.llvm.org/D35048

git-svn-id: https://llvm.org/svn/llvm-project/llvm/trunk@307285 91177308-0d34-0410-b5e6-96231b3b80d8
2017-07-06 17:51:15 +00:00

54 lines
1.6 KiB
Python

import sys
import multiprocessing
_current = None
_total = None
def _init(current, total):
global _current
global _total
_current = current
_total = total
def _wrapped_func(func_and_args):
func, argument, should_print_progress = func_and_args
if should_print_progress:
with _current.get_lock():
_current.value += 1
sys.stdout.write('\r\t{} of {}'.format(_current.value, _total.value))
return func(argument)
def pmap(func, iterable, processes, should_print_progress, *args, **kwargs):
"""
A parallel map function that reports on its progress.
Applies `func` to every item of `iterable` and return a list of the
results. If `processes` is greater than one, a process pool is used to run
the functions in parallel. `should_print_progress` is a boolean value that
indicates whether a string 'N of M' should be printed to indicate how many
of the functions have finished being run.
"""
global _current
global _total
_current = multiprocessing.Value('i', 0)
_total = multiprocessing.Value('i', len(iterable))
func_and_args = [(func, arg, should_print_progress,) for arg in iterable]
if processes <= 1:
result = map(_wrapped_func, func_and_args, *args, **kwargs)
else:
pool = multiprocessing.Pool(initializer=_init,
initargs=(_current, _total,),
processes=processes)
result = pool.map(_wrapped_func, func_and_args, *args, **kwargs)
if should_print_progress:
sys.stdout.write('\r')
return result