#!/usr/bin/env python
#
# mcmandelbrot
#
# An example package for AsynQueue:
# Asynchronous task queueing based on the Twisted framework, with task
# prioritization and a powerful worker interface.
#
# Copyright (C) 2015 by Edwin A. Suominen,
# http://edsuom.com/AsynQueue
#
# See edsuom.com for API documentation as well as information about
# Ed's background and other projects, software and otherwise.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the
# License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing,
# software distributed under the License is distributed on an "AS
# IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either
# express or implied. See the License for the specific language
# governing permissions and limitations under the License.
"""
Runner for Mandelbrot set computation processes.
"""
import sys, time, array
import png
import numpy as np
from zope.interface import implements
from twisted.internet import defer, reactor
from twisted.internet.interfaces import IPushProducer
import asynqueue
from asynqueue.threads import OrderedItemProducer
from asynqueue.process import ProcessQueue
from mcmandelbrot.valuer import MandelbrotValuer
class Runner(object):
"""
I run a multi-process Mandelbrot Set computation operation.
@cvar N_processes: The number of processes to use, disregarded if
I{useThread} is set C{True} in my constructor.
"""
N_minProcesses = 2
N_maxProcesses = 6
msgProto = "{} ({:+16.13f} +/-{:10E}, {:+16.13f} +/-{:10E}) "+\
"{:d} pixels in {:4.2f} sec"
def __init__(self, N_values, stats=False, verbose=False):
self.q = ProcessQueue(self.N_processes, callStats=stats)
self.mv = MandelbrotValuer(N_values)
self.verbose = verbose
def shutdown(self):
return self.q.shutdown()
@property
def N_processes(self):
maxValue = min([
self.N_maxProcesses, ProcessQueue.cores()])
return max([self.N_minProcesses, maxValue])
def log(self, *args):
if self.verbose:
print self.msgProto.format(*args)
def run(self, fh, Nx, cr, ci, crPM, ciPM, dCancel=None, requester=None):
"""
Runs my L{compute} method to generate a PNG image of the
Mandelbrot Set and write it in chunks to the file handle or
write-capable object I{fh}.
The image is centered at location I{cr, ci} in the complex
plane, plus or minus I{crPM} on the real axis and I{ciPM} on
the imaginary axis.
@return: A C{Deferred} that fires with the total elasped time
for the computation and the number of pixels computed.
"""
def done(N):
timeSpent = time.time() - t0
if requester:
self.log(
requester, cr, crPM, ci, ciPM, N, timeSpent)
return timeSpent, N
t0 = time.time()
xySpans = []
for center, plusMinus in ((cr, crPM), (ci, ciPM)):
xySpans.append([center - plusMinus, center + plusMinus])
xySpans[0].append(Nx)
diffs = []
for k in (0, 1):
diff = xySpans[k][1] - xySpans[k][0]
if diff <= 5E-16:
return defer.succeed((0, 0))
diffs.append(diff)
xySpans[1].append(int(Nx * diffs[1] / diffs[0]))
return self.compute(
fh, xySpans[0], xySpans[1], dCancel).addCallback(done)
@defer.inlineCallbacks
def compute(self, fh, xSpan, ySpan, dCancel=None):
"""
Computes the Mandelbrot Set under C{Twisted} and generates a
pretty image, written as a PNG image to the supplied file
handle I{fh} one row at a time.
@return: A C{Deferred} that fires when the image is completely
written and you can close the file handle, with the number
of pixels computed (may be a lower number than expected if
the connection terminated early).
"""
def f(rows):
try:
writer = png.Writer(Nx, Ny, bitdepth=8, compression=9)
writer.write(fh, rows)
except Exception as e:
# Trap ValueError caused by mid-stream cancellation
if not isinstance(e, StopIteration):
if "rows" not in e.message and "height" not in e.message:
print "Error generating PNG: {}".format(e.message)
crMin, crMax, Nx = xSpan
ciMin, ciMax, Ny = ySpan
# We have at most 5 calls in the process queue for each worker
# servicing them, to allow midstream canceling and interleave
# parallel computation requests.
ds = defer.DeferredSemaphore(5*self.N_processes)
p = OrderedItemProducer()
yield p.start(f)
# "The pickle module keeps track of the objects it has already
# serialized, so that later references to the same object won't be
# serialized again." --Python docs
for k, ci in enumerate(np.linspace(ciMax, ciMin, Ny)):
# "Wait" for the number of pending calls to fall back to
# the limit
yield ds.acquire()
# Make sure the render hasn't been canceled
if getattr(dCancel, 'called', False):
break
# Call one of my processes to get each row of values,
# starting from the top
d = p.produceItem(
self.q.call, self.mv, crMin, crMax, Nx, ci,
series='process')
d.addCallback(lambda _: ds.release())
yield p.stop()
defer.returnValue(Nx*(k+1))
def showStats(self, callInfo):
"""
Displays stats about the run on stdout
"""
def gotStats(stats):
x = np.asarray(stats)
workerTime, processTime = [np.sum(x[:,k]) for k in (0,1)]
print "Run stats, with {:d} parallel ".format(self.N_processes) +\
"processes running {:d} calls\n{}".format(len(stats), "-"*70)
print "Process:\t{:7.2f} seconds, {:0.1f}% of main".format(
processTime, 100*processTime/totalTime)
print "Worker:\t\t{:7.2f} seconds, {:0.1f}% of main".format(
workerTime, 100*workerTime/totalTime)
print "Total on main:\t{:7.2f} seconds".format(totalTime)
diffs = 1000*(x[:,0] - x[:,1])
mean = np.mean(diffs)
print "Mean worker-to-process overhead (ms/call): {:0.7f}".format(
mean)
totalTime = callInfo[0]
print "Computed {:d} pixels in {:1.1f} seconds.".format(
callInfo[1], totalTime)
return self.q.stats().addCallback(gotStats)
