This script use the brian module (neuroscience), to benchmark cpu(s) from a computer.

bench.py

'''

Model fitting example using several machines.

Before running this example, you must start the Playdoh server on the remote machines.

'''

from brian import loadtxt, ms, Equations

from brian.library.modelfitting import *

import time 

import datetime





numberpop = 100000

nbr_cpu = 1









def bench(nbr_cpu,numberpop):

    # List of machines IP addresses

    now = datetime.datetime.now().strftime("%s")

    machines = ['bobs-machine.university.com',

                'jims-machine.university.com']

    

    equations = Equations('''

        dV/dt=(R*I-V)/tau : 1

        I : 1

        R : 1

        tau : second

    ''')

    input = loadtxt('current.txt')

    spikes = loadtxt('spikes.txt')

    results = modelfitting( model = equations,

                            reset = 0,

                            threshold = 1,

                            data = spikes,

                            input = input,

                            dt = .1*ms,

                            popsize = numberpop,

                            maxiter = 3,

                            delta = 4*ms,

                            unit_type = 'CPU',

                            #machines = machines,

                            R = [1.0e9, 9.0e9],

                            tau = [10*ms, 40*ms],

                            refractory = [0*ms, 10*ms],

                            cpu=nbr_cpu)

    #print_table(results)

    cal_time = int(datetime.datetime.now().strftime("%s"))-int(now)

    return cal_time





if __name__ == '__main__':

    numberpop = 10000

    liste_cpu_perf=[]

    while numberpop < 100000 :       

        nbr_cpu = 2	

        liste_cpu_perf.append(bench(nbr_cpu,numberpop))

        numberpop = numberpop+10000

    print liste_cpu_perf