def main():
parser = OptionParser()
parser.set_defaults(n=100)
parser.set_defaults(tmin=1e-3)
parser.set_defaults(tmax=1)
parser.set_defaults(profile='default')
parser.add_option("-n",
type='int',
dest='n',
help='the number of tasks to run')
parser.add_option("-t",
type='float',
dest='tmin',
help='the minimum task length in seconds')
parser.add_option("-T",
type='float',
dest='tmax',
help='the maximum task length in seconds')
parser.add_option("-p",
'--profile',
type='str',
dest='profile',
help="the cluster profile [default: 'default']")
(opts, args) = parser.parse_args()
assert opts.tmax >= opts.tmin, "tmax must not be smaller than tmin"
rc = Client()
view = rc.load_balanced_view()
print view
rc.block = True
nengines = len(rc.ids)
with rc[:].sync_imports():
from IPython.utils.timing import time
# the jobs should take a random time within a range
times = [
random.random() * (opts.tmax - opts.tmin) + opts.tmin
for i in range(opts.n)
]
stime = sum(times)
print "executing %i tasks, totalling %.1f secs on %i engines" % (
opts.n, stime, nengines)
time.sleep(1)
start = time.time()
amr = view.map(time.sleep, times)
amr.get()
stop = time.time()
ptime = stop - start
scale = stime / ptime
print "executed %.1f secs in %.1f secs" % (stime, ptime)
print "%.3fx parallel performance on %i engines" % (scale, nengines)
print "%.1f%% of theoretical max" % (100 * scale / nengines)
def execute(self):
from IPython.utils.timing import time
points = []
while len(points) == 0:
hs = self.heuristics
self.current = (self.current + 1) % len(hs)
points.extend(hs[self.current].get_points(self.size))
time.sleep(1e-3)
return points
def task():
while True:
gtk.threads_enter()
try:
[c.draw_idle() for c in self._canvases if c._need_redraw]
finally:
gtk.threads_leave()
from IPython.utils.timing import time
time.sleep(self.config.ui_redraw_delay)
def main():
parser = OptionParser()
parser.set_defaults(n=100)
parser.set_defaults(tmin=1)
parser.set_defaults(tmax=60)
parser.set_defaults(controller='localhost')
parser.set_defaults(meport=10105)
parser.set_defaults(tport=10113)
parser.add_option("-n", type='int', dest='n',
help='the number of tasks to run')
parser.add_option("-t", type='float', dest='tmin',
help='the minimum task length in seconds')
parser.add_option("-T", type='float', dest='tmax',
help='the maximum task length in seconds')
parser.add_option("-c", type='string', dest='controller',
help='the address of the controller')
parser.add_option("-p", type='int', dest='meport',
help="the port on which the controller listens for the MultiEngine/RemoteController client")
parser.add_option("-P", type='int', dest='tport',
help="the port on which the controller listens for the TaskClient client")
(opts, args) = parser.parse_args()
assert opts.tmax >= opts.tmin, "tmax must not be smaller than tmin"
rc = client.MultiEngineClient()
tc = client.TaskClient()
print(tc.task_controller)
rc.block=True
nengines = len(rc.get_ids())
rc.execute('from IPython.utils.timing import time')
# the jobs should take a random time within a range
times = [random.random()*(opts.tmax-opts.tmin)+opts.tmin for i in range(opts.n)]
tasks = [client.StringTask("time.sleep(%f)"%t) for t in times]
stime = sum(times)
print("executing %i tasks, totalling %.1f secs on %i engines"%(opts.n, stime, nengines))
time.sleep(1)
start = time.time()
taskids = [tc.run(t) for t in tasks]
tc.barrier(taskids)
stop = time.time()
ptime = stop-start
scale = stime/ptime
print("executed %.1f secs in %.1f secs"%(stime, ptime))
print("%.3fx parallel performance on %i engines"%(scale, nengines))
print("%.1f%% of theoretical max"%(100*scale/nengines))
def main():
parser = OptionParser()
parser.set_defaults(n=100)
parser.set_defaults(tmin=1e-3)
parser.set_defaults(tmax=1)
parser.set_defaults(profile='default')
parser.add_option("-n", type='int', dest='n',
help='the number of tasks to run')
parser.add_option("-t", type='float', dest='tmin',
help='the minimum task length in seconds')
parser.add_option("-T", type='float', dest='tmax',
help='the maximum task length in seconds')
parser.add_option("-p", '--profile', type='str', dest='profile',
help="the cluster profile [default: 'default']")
(opts, args) = parser.parse_args()
assert opts.tmax >= opts.tmin, "tmax must not be smaller than tmin"
rc = Client()
view = rc.load_balanced_view()
print(view)
rc.block = True
nengines = len(rc.ids)
with rc[:].sync_imports():
from IPython.utils.timing import time
# the jobs should take a random time within a range
times = [
random.random() * (opts.tmax - opts.tmin) + opts.tmin for i in range(opts.n)]
stime = sum(times)
print("executing %i tasks, totalling %.1f secs on %i engines" %
(opts.n, stime, nengines))
time.sleep(1)
start = time.time()
amr = view.map(time.sleep, times)
amr.get()
stop = time.time()
ptime = stop - start
scale = stime / ptime
print("executed %.1f secs in %.1f secs" % (stime, ptime))
print("%.3fx parallel performance on %i engines" % (scale, nengines))
print("%.1f%% of theoretical max" % (100 * scale / nengines))
def main():
parser = OptionParser()
parser.set_defaults(n=100)
parser.set_defaults(tmin=1)
parser.set_defaults(tmax=60)
parser.set_defaults(controller='localhost')
parser.set_defaults(meport=10105)
parser.set_defaults(tport=10113)
parser.add_option("-n",
type='int',
dest='n',
help='the number of tasks to run')
parser.add_option("-t",
type='float',
dest='tmin',
help='the minimum task length in seconds')
parser.add_option("-T",
type='float',
dest='tmax',
help='the maximum task length in seconds')
parser.add_option("-c",
type='string',
dest='controller',
help='the address of the controller')
parser.add_option(
"-p",
type='int',
dest='meport',
help=
"the port on which the controller listens for the MultiEngine/RemoteController client"
)
parser.add_option(
"-P",
type='int',
dest='tport',
help=
"the port on which the controller listens for the TaskClient client")
(opts, args) = parser.parse_args()
assert opts.tmax >= opts.tmin, "tmax must not be smaller than tmin"
rc = client.MultiEngineClient()
tc = client.TaskClient()
print tc.task_controller
rc.block = True
nengines = len(rc.get_ids())
rc.execute('from IPython.utils.timing import time')
# the jobs should take a random time within a range
times = [
random.random() * (opts.tmax - opts.tmin) + opts.tmin
for i in range(opts.n)
]
tasks = [client.StringTask("time.sleep(%f)" % t) for t in times]
stime = sum(times)
print "executing %i tasks, totalling %.1f secs on %i engines" % (
opts.n, stime, nengines)
time.sleep(1)
start = time.time()
taskids = [tc.run(t) for t in tasks]
tc.barrier(taskids)
stop = time.time()
ptime = stop - start
scale = stime / ptime
print "executed %.1f secs in %.1f secs" % (stime, ptime)
print "%.3fx parallel performance on %i engines" % (scale, nengines)
print "%.1f%% of theoretical max" % (100 * scale / nengines)
def func_sum(tid, data):
"x is either a number or a list/vector of numbers"
time.sleep(math.log(1 + random()))
return tid, numpy.sum(data)
