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"""Early Recomputation Measurements
"""
import functools as ft
import multiprocessing.pool
import shutil
import threading
import time
import diskcache as dc
def make_timer(times):
"""Make a decorator which accumulates (start, end) in `times` for function
calls.
"""
lock = threading.Lock()
def timer(func):
@ft.wraps(func)
def wrapper(*args, **kwargs):
start = time.time()
func(*args, **kwargs)
pair = start, time.time()
with lock:
times.append(pair)
return wrapper
return timer
def make_worker(times, delay=0.2):
"""Make a worker which accumulates (start, end) in `times` and sleeps for
`delay` seconds.
"""
@make_timer(times)
def worker():
time.sleep(delay)
return worker
def make_repeater(func, total=10, delay=0.01):
"""Make a repeater which calls `func` and sleeps for `delay` seconds
repeatedly until `total` seconds have elapsed.
"""
def repeat(num):
start = time.time()
while time.time() - start < total:
func()
time.sleep(delay)
return repeat
def frange(start, stop, step=1e-3):
"""Generator for floating point values from `start` to `stop` by `step`."""
while start < stop:
yield start
start += step
def plot(option, filename, cache_times, worker_times):
"""Plot concurrent workers and latency."""
import matplotlib.pyplot as plt
fig, (workers, latency) = plt.subplots(2, sharex=True)
fig.suptitle(option)
changes = [(start, 1) for start, _ in worker_times]
changes.extend((stop, -1) for _, stop in worker_times)
changes.sort()
start = (changes[0][0] - 1e-6, 0)
counts = [start]
for mark, diff in changes:
# Re-sample between previous and current data point for a nicer-looking
# line plot.
for step in frange(counts[-1][0], mark):
pair = (step, counts[-1][1])
counts.append(pair)
pair = (mark, counts[-1][1] + diff)
counts.append(pair)
min_x = min(start for start, _ in cache_times)
max_x = max(start for start, _ in cache_times)
for step in frange(counts[-1][0], max_x):
pair = (step, counts[-1][1])
counts.append(pair)
x_counts = [x - min_x for x, y in counts]
y_counts = [y for x, y in counts]
workers.set_title('Concurrency')
workers.set_ylabel('Workers')
workers.set_ylim(0, 11)
workers.plot(x_counts, y_counts)
latency.set_title('Latency')
latency.set_ylabel('Seconds')
latency.set_ylim(0, 0.5)
latency.set_xlabel('Time')
x_latency = [start - min_x for start, _ in cache_times]
y_latency = [stop - start for start, stop in cache_times]
latency.scatter(x_latency, y_latency)
plt.savefig(filename)
def main():
shutil.rmtree('/tmp/cache')
cache = dc.Cache('/tmp/cache')
count = 10
cache_times = []
timer = make_timer(cache_times)
options = {
('No Caching', 'no-caching.png'): [
timer,
],
('Traditional Caching', 'traditional-caching.png'): [
timer,
cache.memoize(expire=1),
],
('Synchronized Locking', 'synchronized-locking.png'): [
timer,
cache.memoize(expire=0),
dc.barrier(cache, dc.Lock),
cache.memoize(expire=1),
],
('Early Recomputation', 'early-recomputation.png'): [
timer,
dc.memoize_stampede(cache, expire=1),
],
('Early Recomputation (beta=0.5)', 'early-recomputation-05.png'): [
timer,
dc.memoize_stampede(cache, expire=1, beta=0.5),
],
('Early Recomputation (beta=0.3)', 'early-recomputation-03.png'): [
timer,
dc.memoize_stampede(cache, expire=1, beta=0.3),
],
}
for (option, filename), decorators in options.items():
print('Simulating:', option)
worker_times = []
worker = make_worker(worker_times)
for decorator in reversed(decorators):
worker = decorator(worker)
worker()
repeater = make_repeater(worker)
with multiprocessing.pool.ThreadPool(count) as pool:
pool.map(repeater, [worker] * count)
plot(option, filename, cache_times, worker_times)
cache.clear()
cache_times.clear()
if __name__ == '__main__':
main()
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