<p>以下是允许使用多个进程的新脚本(但没有性能改进):</p>
<pre><code>from MMTK import *
from MMTK.Trajectory import Trajectory, TrajectoryOutput, SnapshotGenerator
from MMTK.Proteins import Protein, PeptideChain
import numpy as np
import time
filename = 'traj_prot_nojump.nc'
trajectory = Trajectory(None, filename)
universe = trajectory.universe
proteins = universe.objectList(Protein)
chain = proteins[0][0]
def calpha_2dmap_mult(trajectory = trajectory, t = range(0,len(trajectory))):
dist = []
universe = trajectory.universe
proteins = universe.objectList(Protein)
chain = proteins[0][0]
traj = trajectory[t]
dt = 1000 # calculate distance every 1000 steps
for n, step in enumerate(traj):
if n % dt == 0:
universe.setConfiguration(step['configuration'])
for i in np.arange(len(chain)-1):
for j in np.arange(len(chain)-1):
dist.append(universe.distance(chain[i].peptide.C_alpha,
chain[j].peptide.C_alpha))
return(dist)
c0 = time.time()
dist1 = calpha_2dmap_mult(trajectory, range(0,11001))
#dist1 = calpha_2dmap_mult(trajectory, range(0,11001))
c1 = time.time() - c0
print(c1)
# Multiprocessing
from multiprocessing import Pool, cpu_count
pool = Pool(processes=4)
c0 = time.time()
dist_pool = [pool.apply(calpha_2dmap_mult, args=(trajectory, t,)) for t in
[range(0,2001), range(3000,5001), range(6000,8001),
range(9000,11001)]]
c1 = time.time() - c0
print(c1)
dist1 = np.array(dist1)
dist_pool = np.array(dist_pool)
dist_pool = dist_pool.flatten()
print(np.all((dist_pool == dist1)))
</code></pre>
<p>在没有(70.1s)或多处理(70.2s)的情况下,计算距离所花费的时间是“相同的”!我也许不期望因子4有所改善,但我至少期待一些改进!在</p>