## Main.ParallelComputing History

June 13, 2022, at 12:44 PM by 172.58.61.177 -
Changed line 126 from:
        s.m = GEKKO()

to:
        s.m = GEKKO(remote=False)

May 14, 2021, at 03:28 AM by 136.36.4.38 -

(:sourceend:)

May 14, 2021, at 03:28 AM by 136.36.4.38 -
Changed lines 13-14 from:

In Python, parallelization is accomplished with multithreading. The following example shows how to create and run a program with 10 threads that each print a message.

to:

In Python, parallelization is accomplished with multithreading. The following example shows an example of how to create and run a program with 10 threads that each print a message.

Changed lines 55-56 from:

(:toggle hide gekko button show="Show GEKKO Python Solution":) (:div id=gekko:)

to:

(:toggle hide single_gekko button show="Show Single Threaded GEKKO Python":) (:div id=single_gekko:) (:source lang=python:) import numpy as np from gekko import GEKKO

1. Optimize at mesh points

x = np.arange(20.0, 30.0, 2.0) y = np.arange(30.0, 50.0, 4.0) amg, bmg = np.meshgrid(x, y)

1. Initialize results array

obj = np.empty_like(amg)

m = GEKKO(remote=False) objective = float('NaN')

a,b = m.Array(m.FV,2)

1. model variables, equations, objective

x1 = m.Var(1,lb=1,ub=5) x2 = m.Var(5,lb=1,ub=5) x3 = m.Var(5,lb=1,ub=5) x4 = m.Var(1,lb=1,ub=5) m.Equation(x1*x2*x3*x4>=a) m.Equation(x1**2+x2**2+x3**2+x4**2==b) m.Minimize(x1*x4*(x1+x2+x3)+x3) m.options.SOLVER = 1 # APOPT solver

1. Calculate obj at all meshgrid points

for i in range(amg.shape[0]):

    for j in range(bmg.shape[1]):
a.MEAS = amg[i,j]
b.MEAS = bmg[i,j]

m.solve(disp=False)

obj[i,j] = m.options.OBJFCNVAL
print(amg[i,j],bmg[i,j],obj[i,j])

1. plot 3D figure of results

from mpl_toolkits.mplot3d import Axes3D import matplotlib.pyplot as plt from matplotlib import cm import numpy as np

fig = plt.figure() ax = fig.gca(projection='3d') surf = ax.plot_surface(amg, bmg, obj, rstride=1, cstride=1, cmap=cm.coolwarm, vmin = 10, vmax = 25, linewidth=0, antialiased=False) ax.set_xlabel('a') ax.set_ylabel('b') ax.set_zlabel('obj') plt.show() (:divend:)

(:toggle hide gekko button show="Show Multithreaded GEKKO Python":)

Changed line 84 from:
        s.m.Obj(s.m.x1*s.m.x4*(s.m.x1+s.m.x2+s.m.x3)+s.m.x3)

to:
        s.m.Minimize(s.m.x1*s.m.x4*(s.m.x1+s.m.x2+s.m.x3)+s.m.x3)

June 21, 2020, at 04:45 AM by 136.36.211.159 -
Deleted lines 212-230:

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<script type="text/javascript">
/* * * CONFIGURATION VARIABLES: EDIT BEFORE PASTING INTO YOUR WEBPAGE * * */
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January 16, 2020, at 02:32 AM by 147.46.252.163 -

(:html:) <iframe width="560" height="315" src="https://www.youtube.com/embed/z2gNLjJs6EM" frameborder="0" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe> (:htmlend:)

January 16, 2020, at 02:22 AM by 147.46.252.163 -
Changed line 5 from:

Programs can run on heterogeneous networks and platforms. In this example application, we solve a series of optimization problems using Linux and Windows servers. The optimization problems are transferred to the servers in parallel, computed in parallel, and returned asynchronously to the MATLAB or Python script.

to:

Programs can run on multiple CPU cores or on heterogeneous networks and platforms with parallelization. In this example application, we solve a series of optimization problems using Linux and Windows servers using Python multi-threading. The optimization problems are initialized sequentially, computed in parallel, and returned asynchronously to the MATLAB or Python script.

January 16, 2020, at 02:20 AM by 147.46.252.163 -
Changed line 5 from:

APM is configured to run on heterogeneous networks and platforms. In this example application, we solve a series of optimization problems using Linux and Windows servers. The optimization problems are transferred to the servers in parallel, computed in parallel, and returned asynchronously to the MATLAB or Python script.

to:

Programs can run on heterogeneous networks and platforms. In this example application, we solve a series of optimization problems using Linux and Windows servers. The optimization problems are transferred to the servers in parallel, computed in parallel, and returned asynchronously to the MATLAB or Python script.

July 16, 2019, at 02:03 PM by 174.148.8.17 -
        self.m.cleanup()

Deleted lines 50-51:

Deleted lines 3-4:

#### Parallel Computing

(:divend:)

(:toggle hide gekko button show="Show GEKKO Python Solution":) (:div id=gekko:) (:source lang=python:) import numpy as np import threading import time, random from gekko import GEKKO

    def __init__(self, id, server, ai, bi):
s = self
s.id = id
s.server = server
s.m = GEKKO()
s.a = ai
s.b = bi
s.objective = float('NaN')

# initialize variables
s.m.x1 = s.m.Var(1,lb=1,ub=5)
s.m.x2 = s.m.Var(5,lb=1,ub=5)
s.m.x3 = s.m.Var(5,lb=1,ub=5)
s.m.x4 = s.m.Var(1,lb=1,ub=5)

# Equations
s.m.Equation(s.m.x1*s.m.x2*s.m.x3*s.m.x4>=s.a)
s.m.Equation(s.m.x1**2+s.m.x2**2+s.m.x3**2+s.m.x4**2==s.b)

# Objective
s.m.Obj(s.m.x1*s.m.x4*(s.m.x1+s.m.x2+s.m.x3)+s.m.x3)

# Set global options
s.m.options.IMODE = 3 # steady state optimization
s.m.options.SOLVER = 1 # APOPT solver

def run(self):

# Don't overload server by executing all scripts at once
sleep_time = random.random()
time.sleep(sleep_time)

print('Running application ' + str(self.id) + '\n')

# Solve
self.m.solve(disp=False)

# Results
#print('')
#print('Results')
#print('x1: ' + str(self.m.x1.value))
#print('x2: ' + str(self.m.x2.value))
#print('x3: ' + str(self.m.x3.value))
#print('x4: ' + str(self.m.x4.value))

# Retrieve objective if successful
if (self.m.options.APPSTATUS==1):
self.objective = self.m.options.objfcnval
else:
self.objective = float('NaN')

1. Select server

server = 'https://byu.apmonitor.com'

1. Optimize at mesh points

x = np.arange(20.0, 30.0, 2.0) y = np.arange(30.0, 50.0, 2.0) a, b = np.meshgrid(x, y)

1. Calculate objective at all meshgrid points

id = 0 for i in range(a.shape[0]):

    for j in range(b.shape[1]):
# Increment ID
id += 1

1. Run applications simultaneously as multiple threads
2. Max number of threads to run at once

    while (threading.activeCount()>max_threads):
time.sleep(0.01)
t.start()

1. Check for completion

mt = 3.0 # max time it = 0.0 # incrementing time st = 1.0 # sleep time while (threading.activeCount()>=1):

    time.sleep(st)
it = it + st
# Terminate after max time
if (it>=mt):
break

1. Wait for all threads to complete
3. t.join()
5. Initialize array for objective

obj = np.empty_like(a)

1. Retrieve objective results

id = 0 for i in range(a.shape[0]):

    for j in range(b.shape[1]):
id += 1

1. plot 3D figure of results

from mpl_toolkits.mplot3d import Axes3D import matplotlib.pyplot as plt from matplotlib import cm import numpy as np

fig = plt.figure() ax = fig.gca(projection='3d') surf = ax.plot_surface(a, b, obj, rstride=1, cstride=1, cmap=cm.coolwarm, vmin = 12, vmax = 22, linewidth=0, antialiased=False) ax.set_xlabel('a') ax.set_ylabel('b') ax.set_zlabel('obj') ax.set_title('Multi-Threaded GEKKO') plt.show() (:sourceend:) (:divend:)

Changed lines 7-8 from:

APM is configured to run on heterogeneous networks and platforms. In this example application, we solve a series of optimization problems using Linux and Windows servers. The optimization problems are transferred to the servers in parallel, computed in parallel, and returned asynchronously to the MATLAB or Python script. In Python, parallelization is accomplished with multi-threading.

to:

APM is configured to run on heterogeneous networks and platforms. In this example application, we solve a series of optimization problems using Linux and Windows servers. The optimization problems are transferred to the servers in parallel, computed in parallel, and returned asynchronously to the MATLAB or Python script.

In Python, parallelization is accomplished with multithreading. The following example shows how to create and run a program with 10 threads that each print a message.

Changed lines 26-28 from:
        print "ID => s completes at %s\n" %               (self.id, self.getName(), now)

to:
        print("ID => s completes at %s\n" %               (self.id, self.getName(), now))

Changed lines 34-35 from:
    print 'Active threads: ' + str(threading.activeCount())

to:
    print('Active threads: ' + str(threading.activeCount()))

Changed lines 37-39 from:

to:

Changed line 43 from:

to:

Changed lines 7-42 from:

APM is configured to run on heterogeneous networks and platforms. In this example application, we solve a series of optimization problems using Linux and Windows servers. The optimization problems are transferred to the servers in parallel, computed in parallel, and returned asynchronously to the MATLAB or Python script. The tutorial begins with a simple Nonlinear Programming problem. The tutorial examples are available for download below:

to:

APM is configured to run on heterogeneous networks and platforms. In this example application, we solve a series of optimization problems using Linux and Windows servers. The optimization problems are transferred to the servers in parallel, computed in parallel, and returned asynchronously to the MATLAB or Python script. In Python, parallelization is accomplished with multi-threading.

(:source lang=python:) import threading import datetime import time, random

    def __init__(self, id):
self.id = id
self.delay = random.random()
def run(self):
time.sleep(self.delay)
now = datetime.datetime.now()
print "ID => s completes at %s\n" %               (self.id, self.getName(), now)


threads = [] for i in range(10):

    threads.append(MyThread(i))


1. Wait for all threads to complete

    t.join()


The next step is to embed a simple Nonlinear Programming (NLP) problem into the multi-threaded application. The tutorial examples are available for download below:

April 07, 2016, at 11:11 PM by 10.5.113.123 -
Changed lines 12-13 from:
to:
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January 21, 2013, at 07:40 AM by 69.169.188.188 -
Changed line 12 from:
to:
January 21, 2013, at 07:39 AM by 69.169.188.188 -
Changed line 7 from:

APM is configured to run on heterogeneous networks and platforms. In this example application, we solve a series of optimization problems using Linux and Windows servers. The optimization problems are transferred to the servers in parallel, computed in parallel, and returned asynchronously to the MATLAB interface. The tutorial begins with a simple Nonlinear Programming problem. The tutorial examples are available for download below:

to:

APM is configured to run on heterogeneous networks and platforms. In this example application, we solve a series of optimization problems using Linux and Windows servers. The optimization problems are transferred to the servers in parallel, computed in parallel, and returned asynchronously to the MATLAB or Python script. The tutorial begins with a simple Nonlinear Programming problem. The tutorial examples are available for download below:

January 21, 2013, at 07:35 AM by 69.169.188.188 -
Changed lines 7-11 from:

APM is configured to run on heterogeneous networks and platforms. In this example application, we solve a series of optimization problems using Linux and Windows servers. The optimization problems are transferred to the servers in parallel, computed in parallel, and returned asynchronously to the MATLAB interface. The tutorial begins with a simple Nonlinear Programming problem that is formulated in two different ways. The tutorial example and other files are available for download below:

to:

APM is configured to run on heterogeneous networks and platforms. In this example application, we solve a series of optimization problems using Linux and Windows servers. The optimization problems are transferred to the servers in parallel, computed in parallel, and returned asynchronously to the MATLAB interface. The tutorial begins with a simple Nonlinear Programming problem. The tutorial examples are available for download below:

January 19, 2013, at 06:32 AM by 69.169.188.188 -
Changed line 14 from:

<iframe width="560" height="315" src="https://www.youtube.com/embed/GB0NYz-k8ZM?rel=0" frameborder="0" allowfullscreen></iframe>

to:

<iframe width="560" height="315" src="https://www.youtube.com/embed/Hr-d_yHKPn4?rel=0" frameborder="0" allowfullscreen></iframe>

January 19, 2013, at 12:43 AM by 128.187.97.21 -
Changed lines 13-16 from:
to:

(:html:) <iframe width="560" height="315" src="https://www.youtube.com/embed/GB0NYz-k8ZM?rel=0" frameborder="0" allowfullscreen></iframe> (:htmlend:)

January 19, 2013, at 12:40 AM by 128.187.97.21 -

(:title Parallel Computing in Optimization:) (:keywords parallel computing, mathematical modeling, nonlinear, optimization, engineering optimization, interior point, active set, differential, algebraic, modeling language, university course:) (:description Tutorial on using MATLAB to solve parallel computing optimization applications.:)

#### Parallel Computing

APM is configured to run on heterogeneous networks and platforms. In this example application, we solve a series of optimization problems using Linux and Windows servers. The optimization problems are transferred to the servers in parallel, computed in parallel, and returned asynchronously to the MATLAB interface. The tutorial begins with a simple Nonlinear Programming problem that is formulated in two different ways. The tutorial example and other files are available for download below:

(:html:)

 <div id="disqus_thread"></div>
<script type="text/javascript">
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/* * * DON'T EDIT BELOW THIS LINE * * */
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dsq.src = 'https://' + disqus_shortname + '.disqus.com/embed.js';