update
This commit is contained in:
29
language_learning/2021.05.10_parallel_calculations_using_sh_files/a.py
Executable file
29
language_learning/2021.05.10_parallel_calculations_using_sh_files/a.py
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import numpy as np
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# 设置
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cpus = 7 # 使用的CPU个数（等于提交任务的个数）
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parameter_array_all = np.arange(0, 10, 0.1) # 需要计算的参数
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# 通过.sh脚本文件修改的任务指标。job_index从0开始，最大值为cpus-1
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job_index = -1
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# 预处理
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len_of_parameter_all = len(parameter_array_all) # 需要计算参数的个数
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if len_of_parameter_all%cpus == 0:
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len_parameter = int(len_of_parameter_all/cpus) # 一个CPU/任务需要计算参数的个数
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parameter_array = parameter_array_all[job_index*len_parameter:(job_index+1)*len_parameter]
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else:
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len_parameter = int(len_of_parameter_all/(cpus-1)) # 一个CPU/任务需要计算参数的个数
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if job_index != cpus-1:
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parameter_array = parameter_array_all[job_index*len_parameter:(job_index+1)*len_parameter]
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else:
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parameter_array = parameter_array_all[job_index*len_parameter:len_of_parameter_all]
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# 任务
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with open('a'+str(job_index)+'.txt', 'w') as f:
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for parameter in parameter_array:
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result = parameter**2
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f.write(str(parameter)+' '+str(result)+'\n')
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5
language_learning/2021.05.10_parallel_calculations_using_sh_files/a.sh
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language_learning/2021.05.10_parallel_calculations_using_sh_files/a.sh
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#!/bin/sh
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#PBS -N task
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#PBS -l nodes=1:ppn=1
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export OMP_NUM_THREADS=1
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python a.py
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f = open('combine.txt', 'w')
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for job_index in range(7):
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with open('a'+str(job_index)+'.txt', 'r') as f0:
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text = f0.read()
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f.write(text)
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f.close()
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0.0 0.0
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0.1 0.010000000000000002
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0.2 0.04000000000000001
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0.30000000000000004 0.09000000000000002
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0.4 0.16000000000000003
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0.5 0.25
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0.6000000000000001 0.3600000000000001
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0.7000000000000001 0.4900000000000001
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0.8 0.6400000000000001
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0.9 0.81
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1.0 1.0
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1.1 1.2100000000000002
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1.2000000000000002 1.4400000000000004
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1.3 1.6900000000000002
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1.4000000000000001 1.9600000000000004
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1.5 2.25
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1.6 2.5600000000000005
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1.7000000000000002 2.8900000000000006
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1.8 3.24
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1.9000000000000001 3.6100000000000003
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2.0 4.0
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2.1 4.41
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2.2 4.840000000000001
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2.3000000000000003 5.290000000000001
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2.4000000000000004 5.760000000000002
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2.5 6.25
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2.6 6.760000000000001
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2.7 7.290000000000001
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2.8000000000000003 7.840000000000002
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2.9000000000000004 8.410000000000002
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3.0 9.0
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3.1 9.610000000000001
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3.2 10.240000000000002
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3.3000000000000003 10.890000000000002
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3.4000000000000004 11.560000000000002
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3.5 12.25
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3.6 12.96
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3.7 13.690000000000001
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3.8000000000000003 14.440000000000001
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3.9000000000000004 15.210000000000003
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4.0 16.0
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4.1000000000000005 16.810000000000006
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4.2 17.64
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4.3 18.49
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4.4 19.360000000000003
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4.5 20.25
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4.6000000000000005 21.160000000000004
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4.7 22.090000000000003
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4.800000000000001 23.040000000000006
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4.9 24.010000000000005
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5.0 25.0
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5.1000000000000005 26.010000000000005
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5.2 27.040000000000003
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5.300000000000001 28.090000000000007
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5.4 29.160000000000004
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5.5 30.25
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5.6000000000000005 31.360000000000007
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5.7 32.49
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5.800000000000001 33.64000000000001
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5.9 34.81
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6.0 36.0
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6.1000000000000005 37.21000000000001
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6.2 38.440000000000005
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6.300000000000001 39.69000000000001
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6.4 40.96000000000001
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6.5 42.25
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6.6000000000000005 43.56000000000001
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6.7 44.89
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6.800000000000001 46.24000000000001
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6.9 47.61000000000001
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7.0 49.0
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7.1000000000000005 50.41000000000001
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7.2 51.84
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7.300000000000001 53.29000000000001
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7.4 54.760000000000005
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7.5 56.25
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7.6000000000000005 57.760000000000005
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7.7 59.290000000000006
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7.800000000000001 60.84000000000001
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7.9 62.410000000000004
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8.0 64.0
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8.1 65.61
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8.200000000000001 67.24000000000002
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8.3 68.89000000000001
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8.4 70.56
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8.5 72.25
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8.6 73.96
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8.700000000000001 75.69000000000001
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8.8 77.44000000000001
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8.9 79.21000000000001
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9.0 81.0
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9.1 82.80999999999999
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9.200000000000001 84.64000000000001
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9.3 86.49000000000001
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9.4 88.36000000000001
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9.5 90.25
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9.600000000000001 92.16000000000003
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9.700000000000001 94.09000000000002
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9.8 96.04000000000002
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9.9 98.01
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0.0 0.0
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0.1 0.010000000000000002
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0.2 0.04000000000000001
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0.30000000000000004 0.09000000000000002
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0.4 0.16000000000000003
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0.5 0.25
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0.6000000000000001 0.3600000000000001
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0.7000000000000001 0.4900000000000001
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0.8 0.6400000000000001
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0.9 0.81
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1.0 1.0
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1.1 1.2100000000000002
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1.2000000000000002 1.4400000000000004
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1.3 1.6900000000000002
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1.4000000000000001 1.9600000000000004
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1.5 2.25
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1.6 2.5600000000000005
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1.7000000000000002 2.8900000000000006
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1.8 3.24
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1.9000000000000001 3.6100000000000003
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2.0 4.0
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2.1 4.41
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2.2 4.840000000000001
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2.3000000000000003 5.290000000000001
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2.4000000000000004 5.760000000000002
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2.5 6.25
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2.6 6.760000000000001
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2.7 7.290000000000001
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2.8000000000000003 7.840000000000002
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2.9000000000000004 8.410000000000002
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3.0 9.0
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3.1 9.610000000000001
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3.2 10.240000000000002
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3.3000000000000003 10.890000000000002
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3.4000000000000004 11.560000000000002
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3.5 12.25
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3.6 12.96
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3.7 13.690000000000001
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3.8000000000000003 14.440000000000001
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3.9000000000000004 15.210000000000003
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4.0 16.0
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4.1000000000000005 16.810000000000006
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4.2 17.64
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4.3 18.49
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4.4 19.360000000000003
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4.5 20.25
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4.6000000000000005 21.160000000000004
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4.7 22.090000000000003
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4.800000000000001 23.040000000000006
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4.9 24.010000000000005
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5.0 25.0
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5.1000000000000005 26.010000000000005
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5.2 27.040000000000003
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5.300000000000001 28.090000000000007
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5.4 29.160000000000004
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5.5 30.25
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5.6000000000000005 31.360000000000007
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5.7 32.49
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5.800000000000001 33.64000000000001
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5.9 34.81
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6.0 36.0
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6.1000000000000005 37.21000000000001
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6.2 38.440000000000005
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6.300000000000001 39.69000000000001
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6.4 40.96000000000001
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6.5 42.25
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6.6000000000000005 43.56000000000001
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6.7 44.89
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6.800000000000001 46.24000000000001
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6.9 47.61000000000001
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7.0 49.0
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7.1000000000000005 50.41000000000001
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7.2 51.84
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7.300000000000001 53.29000000000001
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7.4 54.760000000000005
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7.5 56.25
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7.6000000000000005 57.760000000000005
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7.7 59.290000000000006
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7.800000000000001 60.84000000000001
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7.9 62.410000000000004
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8.0 64.0
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8.1 65.61
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8.200000000000001 67.24000000000002
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8.3 68.89000000000001
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8.4 70.56
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8.5 72.25
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8.6 73.96
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8.700000000000001 75.69000000000001
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8.8 77.44000000000001
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8.9 79.21000000000001
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9.0 81.0
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9.1 82.80999999999999
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9.200000000000001 84.64000000000001
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9.3 86.49000000000001
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9.4 88.36000000000001
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9.5 90.25
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9.600000000000001 92.16000000000003
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9.700000000000001 94.09000000000002
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9.8 96.04000000000002
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9.9 98.01
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14
language_learning/2021.05.10_parallel_calculations_using_sh_files/task.sh
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language_learning/2021.05.10_parallel_calculations_using_sh_files/task.sh
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#!/bin/sh
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for job_index in 0 1 2 3 4 5 6
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do
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cp a.py a${job_index}.py
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sed -i "s/job_index = -1/job_index = ${job_index}/" a${job_index}.py
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cp a.sh a${job_index}.sh
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sed -i "s/python a.py/python a${job_index}.py/" a${job_index}.sh
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qsub a${job_index}.sh
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done
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