from async_runners.AsyncPool import AsyncPool
from work.web.async_web import do_job as async_web
from work.web.async_web_requests import do_job as async_web_requests
from work.web.threads_web import do_job as threads_web
from work.web.process_web import do_job as process_web
from benchmark import Benchmark
logging.basicConfig(level=logging.DEBUG)
benchmarker = Benchmark(3)
funcs = [
(async_web_requests, 'async_web_requests'),
(async_web, 'async_web'),
(threads_web, 'threads_web'),
(process_web, 'process_web')
]
for func, name in funcs:
print(name)
result = benchmarker.benchmark(
func,
13,
[1, 50, 200, 1000],
[1, 4, 10, 30, 50, 100, 1000])
result = result.pivot(
index='job_count', columns='worker_count', values='execution_time')
result.to_csv(f'data/{name}.csv')
import matplotlib.pyplot as plt
import seaborn as sns
from bokeh.io import output_file, show
from bokeh.plotting import figure
from bokeh.transform import linear_cmap
from bokeh.util.hex import hexbin
from async_runners.AsyncPool import AsyncPool
from work.compute_prime.async_compute import do_job as async_prime
from work.compute_prime.threads_compute import do_job as threads_prime
from work.compute_prime.process_compute import do_job as process_prime
from benchmark import Benchmark
benchmarker = Benchmark(3)
funcs = [(async_prime, 'async_prime'),
(threads_prime, 'threads_prime'),
(process_prime, 'process_prime')]
for func, name in funcs:
print(name)
result = benchmarker.benchmark(
func,
1000,
[1, 100, 1000],
[1, 4, 50, 100, 1000])
result = result.pivot(
index='job_count', columns='worker_count', values='execution_time')
result.to_csv(f'data/{name}.csv')
