In [1]:

    

import numpy as np
import matplotlib.pyplot as plt
%matplotlib inline
from collections import defaultdict
from math import log2, floor, ceil
from statistics import mean, median


    

In [2]:

    

def load_file(filename):
    a = np.loadtxt(filename, dtype='str', comments='#')
    rsp = [round(float(x),6) for x in  a[:,0]]
    latencies = a[:,1]
    times = a[:,2]
    processors = a[:,3]
    work = a[:,4]
    i_steals = a[:,16]
    e_steals = a[:,17]
    return rsp, latencies, times, processors, work#, i_steals, i_steals

directory = "/home/khatiri/these/projet/ws-simulator/Simulation/strategy_proba_memory/proba_memory_steal_50p/"
directory_70p = "/home/khatiri/these/projet/ws-simulator/Simulation/strategy_proba_memory/proba_memory_steal_70p/"

#directory_80p = "/home/khatiri/these/projet/ws-simulator/Simulation/strategy_proba/proba_steal_80p/"
#directory_90p = "/home/khatiri/these/projet/ws-simulator/Simulation/strategy_proba/proba_steal_90p/"


    

In [3]:

    

def compute_average(values, latence):
    average = defaultdict(int)
    run_number = defaultdict(int)
    
    for i in range(len(rsp)):
        if int(latencies[i]) == latence:
            run_number[float(rsp[i])] += 1
            average[float(rsp[i])] += int(values[i])
            
    for cle in average:
        average[cle] /= run_number[cle]
    return average

def compute_overhead_for_latence(data, latence):
    rsp, latencies, times, processors, work = data
    all_average = defaultdict(list)
    average = defaultdict(int)
    run_number = defaultdict(int)
    
    for i in range(len(rsp)):
        if int(latencies[i]) == latence:
            #run_number[float(rsp[i])] += 1
            all_average[float(rsp[i])].append(float(int(times[i]) - int(work[i])/int(processors[i])))
    for cle in sorted(all_average):
        size = len(all_average[cle])
        #average[cle] = mean(all_average[cle][ceil(0.25*size):ceil(0.75*size)])
        average[cle] = mean(all_average[cle])
        #print(mean(all_average[cle]), mean(all_average[cle][ceil(0.25*size):ceil(0.75*size)]))
  #      average[cle] = median(all_average[cle])

    return all_average, average, min(average.keys(), key=lambda x : average[x])


def compute_overhead(data, latence, variable):
    rsp, latencies, times, processors, work = data
    average = defaultdict(int)
    run_number = defaultdict(int)
    average = 0
    run_number = 0
    
    for i in range(len(rsp)):
        if float(rsp[i]) == variable and float(latencies[i]) == latence:
            run_number += 1
            average += float(int(times[i]) - int(work[i])/int(processors[i]))
            
    return average/run_number


    

In [4]:

    

def plot_for_best(filename):
    best = dict()
    base_line = dict()
    data = load_file(filename)
    latencies = data[1]
    for latence in sorted(set(latencies), key=lambda x: int(x)):
        all_average, avg_overhead, minimum  = compute_overhead_for_latence(data, int(latence))
        best_avg_overhead = compute_overhead(data, int(latence), minimum)
        best[latence] = best_avg_overhead
        if minimum < 1:
            base_line[latence] = compute_overhead(data, int(latence), 0.5)
            #print(latence, minimum)
    
    return best, base_line


    

In [5]:

    

def latence_for_best_param(filename):
    data = load_file(filename)
    latencies = data[1]
    best = dict()

    for latence in sorted(set(latencies), key=lambda x: int(x)):
        all_average, overhead, minimum = compute_overhead_for_latence(data, int(latence))
        #plt.subplot(223)
        plt.plot(overhead.keys(), overhead.values())
        best[latence] = minimum
    return best


    

In [6]:

    

def best_overhead_dict(directory):
    best_value_p = defaultdict(dict)
    best_value_w = defaultdict(dict)
    best_value_l = defaultdict(dict)

    for w in (10000000,50000000,100000000,500000000):
        for p in (16,32):
            filename = directory + "vss_proba_memory_" + str(p) + "_" + str(w)
            best, _ = plot_for_best(filename)
            for latence in best.keys():
                best_value_w[(p, int(latence))][w]=best[latence]
                best_value_p[(w, int(latence))][p]=best[latence]
                best_value_l[(w, p)][int(latence)]=best[latence]

    return best_value_w, best_value_p, best_value_l


    

In [7]:

    

def best_value_dict(directory, seuil):
    best_value_p = defaultdict(dict)
    best_value_w = defaultdict(dict)
    best_value_l = defaultdict(dict)

    for w in (10000000,50000000,100000000,500000000):
        for p in (16,32,64):
            filename = directory + "vss_proba_memory_" + str(p) + "_" + str(w)
            data = load_file(filename)
            for latence in (128,256,512,1024):
                _, overhead, minimum = compute_overhead_for_latence(data, latence)
                
                overhead_min = overhead[minimum]
                interval_max = overhead[minimum]  + 2*latence/p #+ overhead[minimum]*seuil/100

                #print(minimum, overhead[minimum], interval_max)
                
                
                overhead_plage = list(filter(lambda x : overhead_min <= overhead[x] <= interval_max, overhead))
                
                #print([(x, overhead[x]) for x in overhead_plage])
                
                best_value_w[(p, int(latence))][w]= overhead_plage
                best_value_p[(w, int(latence))][p]= overhead_plage
                best_value_l[(w, p)][int(latence)]= overhead_plage

    return best_value_w, best_value_p, best_value_l


    

In [8]:

    

fig = plt.figure()
fig.set_size_inches(18.5, 12.5, forward=True)

plt.subplot(331)
#plt.xlim(0.001, 0.5)
position = 330
for (l,lim) in sorted({(128,16000),(256,8000),(512,15000)}):
    for p,c in sorted({(16,"(a)"),(32,"(b)"),(64,"(c)")}):
        #position
        position += 1
        plt.subplot(position)
        
        #labels
        if l == 512:
            plt.xlabel("remote steal probability (rsp)")
        if l == 128:
            plt.title(c+" "+str(p)+" processors")
        if p == 16:
            plt.ylabel("overhead ($\lambda = "+str(l)+"$)")
        
        #limits
        plt.xlim(0.001, 0.1)
        plt.ylim(0, lim)
        
        #plots
        for w1, w2, s in sorted({(10000000,"1.$10^7$", "x--"),(50000000,"5.$10^7$", "o--"), (100000000,"1.$10^8$", "x-"), (500000000,"5.$10^8$", "o-")}):
            data = load_file(directory + "vss_proba_memory_"+str(p)+"_"+str(w1))
            #plt.title("l="+str(l)+" p="+str(p)+" w="+str(w1))
            all_average, overhead, minimum = compute_overhead_for_latence(data, l)
            plt.plot(overhead.keys(), overhead.values(), s, label=w2)
        
        #legend
        if p == 32 and l == 256: 
            plt.legend()
        plt.savefig('/tmp/overhead_according_rsp_l_2.pdf',bbox_inches='tight')


    

    




/home/khatiri/.local/lib/python3.6/site-packages/matplotlib/figure.py:98: MatplotlibDeprecationWarning: 
Adding an axes using the same arguments as a previous axes currently reuses the earlier instance.  In a future version, a new instance will always be created and returned.  Meanwhile, this warning can be suppressed, and the future behavior ensured, by passing a unique label to each axes instance.
  "Adding an axes using the same arguments as a previous axes "






    

In [22]:

    

fig = plt.figure()
fig.set_size_inches(18.5, 12.5, forward=True)

position = 0
for w1, w2 in sorted({(10000000,"1.$10^7$"),(50000000,"5.$10^7$"), (100000000,"1.$10^8$"), (500000000,"5.$10^8$")}):
    for p,c in sorted({(16,"(a)"),(32,"(b)"),(64,"(c)")}):
        
        
    
        #position
        position += 1
        plt.subplot(4, 3, position)
        plt.ylim(0,30000)

        #plots
        data = load_file(directory + "vss_proba_memory_"+str(p)+"_"+str(w1))
        
        for l,s in sorted({(64, "x--"), (128, "o--"), (256, "x-")}):
            if w1 == 10000000:
                plt.title(c+" "+str(p)+" processors")
            if w1 == 500000000:
                plt.xlabel("remote steal probability (rsp)")
            if p == 16:
                plt.ylabel("overhead (W = "+w2+")")
            
            plt.xlim(0.001, 0.1)
            all_average, overhead, minimum = compute_overhead_for_latence(data, l)
            plt.plot(overhead.keys(), overhead.values(), s, label="$\lambda=$"+str(l))
        
        #legend
        if p == 32 and w1 == 10000000: 
            plt.legend()
        plt.savefig('/tmp/overhead_according_rsp_l_2.pdf',bbox_inches='tight')


    

In [13]:

    

def boxdata(p, w, l, rspmin, rspmax):
    data = load_file(directory + "vss_proba_memory_{}_{}".format(p,w))
    all_average, overhead, minimum = compute_overhead_for_latence(data, l)
    #print(all_average)
    return [all_average[x] for x in all_average.keys() if rspmin <= x and x <= rspmax], \
[round(x,6) for x in all_average.keys() if rspmin <= x and x <= rspmax]


    

In [14]:

    

best_w, best_p, best_l = best_value_dict(directory, 2)


    

In [15]:

    

fig = plt.figure()
fig.set_size_inches(12.5, 6.5, forward=True)
list_avrg, list_keys = boxdata(32, 100000000, 64, 0, 0.5)
maxim = max(best_w[(32,256)][100000000])
#plt.subplot(111)

plt.xlabel("remote steal probability (rsp)")
plt.ylabel("overhead")

data = load_file(directory + "vss_proba_memory_32_100000000")

plt.ylim(0, 50000)
plt.xticks(rotation=90)
w = [0.1]*len(list_keys)
keys_positions = [x*100 for x in list_keys]
plt.boxplot(list_avrg, positions=keys_positions, labels = list_keys) 
_, overhead, minimum = compute_overhead_for_latence(data, 64)
plt.plot([x*100 for x in overhead.keys()], overhead.values(), "o--", label="latence = 256")


    

    
Out[15]:





[<matplotlib.lines.Line2D at 0x7fcc566d4710>]






    

In [19]:

    

fig = plt.figure()
fig.set_size_inches(12.5, 6.5, forward=True)
list_avrg, list_keys = boxdata(16, 100000000, 256, 0, 0.5)
maxim = max(best_w[(16,256)][100000000])
#plt.subplot(111)

plt.xlabel("remote steal probability (rsp)")
plt.ylabel("overhead")

data = load_file(directory + "vss_proba_memory_16_100000000")

plt.ylim(0, 30000)
plt.xticks(rotation=90)
w = [0.1]*len(list_keys)
keys_positions = [x*100 for x in list_keys]
plt.boxplot(list_avrg, positions=keys_positions, labels = list_keys) 
_, overhead, minimum = compute_overhead_for_latence(data, 256)
plt.plot([x*100 for x in overhead.keys()], overhead.values(), "o--", label="latence = 256")


    

    
Out[19]:





[<matplotlib.lines.Line2D at 0x7fcc572ef8d0>]






    

In [18]:

    

fig = plt.figure()
fig.set_size_inches(18.5, 12.5, forward=True)

plt.subplot(331)
#plt.xlim(0.001, 0.5)
position = 330
for (l,lim) in sorted({(128,6000),(256,8000),(512,15000)}):
    for p,c in sorted({(16,"(a)"),(32,"(b)"),(64,"(c)")}):
        #position
        position += 1
        plt.subplot(position)
        
        #labels
        if l == 512:
            plt.xlabel("remote steal probability (rsp)")
        if l == 128:
            plt.title(c+" "+str(p)+" processors")
        if p == 16:
            plt.ylabel("overhead ($\lambda = "+str(l)+"$)")
        
        #limits
        plt.xlim(0.001, 0.5)
        plt.ylim(0, lim)
        
        #plots
        for w1, w2, s in {(100000000,"1.$10^8$", "x--")}:
            filename = directory  + "vss_proba_"+str(p)+"_"+str(w1)
            data = load_file(filename)
            _, overhead, minimum = compute_overhead_for_latence(data, l)
            plt.plot(overhead.keys(), overhead.values(), "o--", label="steal 50%")

            filename = directory_70p + "vss_proba_"+str(p)+"_"+str(w1)
            data = load_file(filename)
            _, overhead, minimum = compute_overhead_for_latence(data, l)
            plt.plot(overhead.keys(), overhead.values(), "o-", label="steal 70%")

            filename = directory_80p + "vss_proba_"+str(p)+"_"+str(w1)
            data = load_file(filename)
            _, overhead, minimum = compute_overhead_for_latence(data, l)
            plt.plot(overhead.keys(), overhead.values(), "x-", label="steal 80%")

            filename = directory_90p + "vss_proba_"+str(p)+"_"+str(w1)
            data = load_file(filename)
            _, overhead, minimum = compute_overhead_for_latence(data, l)
            plt.plot(overhead.keys(), overhead.values(), "x--", label="steal 90%")
                    
                                        
        #legend
        if p == 32 and l == 256: 
            plt.legend()
        plt.savefig('/tmp/overhead_according_rsp_l_2.pdf',bbox_inches='tight')


    

    




---------------------------------------------------------------------------
OSError                                   Traceback (most recent call last)
<ipython-input-18-496ab1756600> in <module>
     26         for w1, w2, s in {(100000000,"1.$10^8$", "x--")}:
     27             filename = directory  + "vss_proba_"+str(p)+"_"+str(w1)
---> 28             data = load_file(filename)
     29             _, overhead, minimum = compute_overhead_for_latence(data, l)
     30             plt.plot(overhead.keys(), overhead.values(), "o--", label="steal 50%")

<ipython-input-3-51cf681f0517> in load_file(filename)
      1 def load_file(filename):
----> 2     a = np.loadtxt(filename, dtype='str', comments='#')
      3     rsp = [round(float(x),6) for x in  a[:,0]]
      4     latencies = a[:,1]
      5     times = a[:,2]

~/.local/lib/python3.6/site-packages/numpy/lib/npyio.py in loadtxt(fname, dtype, comments, delimiter, converters, skiprows, usecols, unpack, ndmin, encoding, max_rows)
    953             fname = os_fspath(fname)
    954         if _is_string_like(fname):
--> 955             fh = np.lib._datasource.open(fname, 'rt', encoding=encoding)
    956             fencoding = getattr(fh, 'encoding', 'latin1')
    957             fh = iter(fh)

~/.local/lib/python3.6/site-packages/numpy/lib/_datasource.py in open(path, mode, destpath, encoding, newline)
    264 
    265     ds = DataSource(destpath)
--> 266     return ds.open(path, mode, encoding=encoding, newline=newline)
    267 
    268 

~/.local/lib/python3.6/site-packages/numpy/lib/_datasource.py in open(self, path, mode, encoding, newline)
    622                                       encoding=encoding, newline=newline)
    623         else:
--> 624             raise IOError("%s not found." % path)
    625 
    626 

OSError: /home/khatiri/these/projet/ws-simulator/Simulation/strategy_proba_memory/proba_memory_steal_50p/vss_proba_16_100000000 not found.





    

In [44]:

    

fig = plt.figure()
fig.set_size_inches(18.5, 10.5, forward=True)

#filename = "/home/khatiri/these/projet/ws-simulator/Simulation/vss_proba_32_100000000"
print()
filename = directory  + "vss_proba_16_100000000"
best_proba_50, base_line = plot_for_best(filename)

filename = directory_70p + "vss_proba_16_100000000"
best_proba_70, _ = plot_for_best(filename)


filename = directory_90p + "vss_proba_16_100000000"
best_proba_90, _ = plot_for_best(filename)

filename = directory_80p + "vss_proba_16_100000000"
best_proba_80, _ = plot_for_best(filename)

#plt.plot(base_line.keys(), base_line.values(), 'o-', label="best_proba")
plt.plot(best_proba_50.keys(), best_proba_50.values(), 'o-', label="best_proba 50%")
plt.plot(best_proba_70.keys(), best_proba_70.values(), 'o--', label="best_proba 70%")
plt.plot(best_proba_80.keys(), best_proba_80.values(), 'x-', label="best_proba 80%")
plt.plot(best_proba_90.keys(), best_proba_90.values(), 'x--', label="best_proba 90%")
plt.xlabel("latence")
plt.legend()

fig = plt.figure()
fig.set_size_inches(18.5, 10.5, forward=True)
plt.ylim(1,2)
plt.plot(best_proba_50.keys(), [bl/b for (b, bl) in zip(best_proba_70.values(), best_proba_50.values())], 'o-', label="proba/base_line")


    

    










    




------------------------------------------------------------
OSError                    Traceback (most recent call last)
<ipython-input-44-aee2e4d3a1c2> in <module>
      5 print()
      6 filename = directory  + "vss_proba_16_100000000"
----> 7 best_proba_50, base_line = plot_for_best(filename)
      8 
      9 filename = directory_70p + "vss_proba_16_100000000"

<ipython-input-30-366f756f2156> in plot_for_best(filename)
      2     best = dict()
      3     base_line = dict()
----> 4     data = load_file(filename)
      5     latencies = data[1]
      6     for latence in sorted(set(latencies), key=lambda x: int(x)):

<ipython-input-17-bffe730b0bb8> in load_file(filename)
      1 def load_file(filename):
----> 2     a = np.loadtxt(filename, dtype='str', comments='#')
      3     rsp = [round(float(x),6) for x in  a[:,0]]
      4     latencies = a[:,1]
      5     times = a[:,2]

~/.local/lib/python3.6/site-packages/numpy/lib/npyio.py in loadtxt(fname, dtype, comments, delimiter, converters, skiprows, usecols, unpack, ndmin, encoding, max_rows)
    953             fname = os_fspath(fname)
    954         if _is_string_like(fname):
--> 955             fh = np.lib._datasource.open(fname, 'rt', encoding=encoding)
    956             fencoding = getattr(fh, 'encoding', 'latin1')
    957             fh = iter(fh)

~/.local/lib/python3.6/site-packages/numpy/lib/_datasource.py in open(path, mode, destpath, encoding, newline)
    264 
    265     ds = DataSource(destpath)
--> 266     return ds.open(path, mode, encoding=encoding, newline=newline)
    267 
    268 

~/.local/lib/python3.6/site-packages/numpy/lib/_datasource.py in open(self, path, mode, encoding, newline)
    622                                       encoding=encoding, newline=newline)
    623         else:
--> 624             raise IOError("%s not found." % path)
    625 
    626 

OSError: /home/khatiri/these/projet/ws-simulator/Simulation/strategy_proba_memory/proba_memory_steal_50p/vss_proba_16_100000000 not found.





    






<Figure size 1332x756 with 0 Axes>

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