#defines the class of the polar plot
[docs]class polar_axis():
step=5
def __init__(self,fig,ax,data):
self.fig = fig #pylab.figure()
self.ax = ax #pylab.axes([0.025,0.025,0.95,0.95], polar=True)
self.data=data
self.reset=True
self.ax.cla()
datatmp=self.data
self.subdata=self.dump_timing_level(datatmp)
self.draw_polarplot()
self.fig.canvas.mpl_connect('motion_notify_event',self._info_callback)
#self.fig.canvas.mpl_connect('pick_event',self._new_level)
self.fig.canvas.mpl_connect('button_press_event',self._new_level)
[docs] def draw_polarplot(self):
import pylab
import numpy as np
#self.subdata=data
self.tot=self.subdata["time"][0]
self.times=self.subdata["time"]
self.N=len(self.times)
self.width=np.array(self.subdata["time"])/self.tot*2*np.pi
self.theta=np.array(self.subdata["theta"])/self.tot*2*np.pi
self.bot=np.array(self.subdata["level"])
self.names=self.subdata["names"]
self.radii = np.array(self.N*[self.step])
self.bars = self.ax.bar(self.theta,self.radii,
width=self.width,
bottom=self.step*self.bot,picker=True)
ilev=0
#maxlev=max(self.bot)
for r,bar,ilev in zip(self.radii, self.bars,self.theta):
#print ilev,'hello',float(ilev)/float(N),maxlev
#bar.set_facecolor( pylab.cm.jet(float(ilev)/maxlev))
bar.set_facecolor(pylab.cm.jet(float(ilev)/(2.0*np.pi)))
bar.set_alpha(0.5)
ilev+=1
self.ax.set_xticklabels([])
self.ax.set_yticklabels([])
self._wrotesome=False
self.info=self.ax.text(0,0,'',fontsize=150)
## to be used what it is the moment to show the plot(s)
#def show(self):
# import pylab
# try:
# pylab.show()
# except KeyboardInterrupt:
# raise
def _find_name(self,th,level):
import numpy as np
levth=[]
ipiv=[]
for i in range(self.N):
if self.bot[i]==level:
levth.append(self.theta[i])
ipiv.append(i)
if len(levth)==0: return None
to_find_zero=np.array(levth)-th
to_find_zero[to_find_zero > 0]=-4*np.pi
routine=np.argmin(-to_find_zero)
if routine == len(to_find_zero)-1 and abs(routine)> 0.5:
return None
else:
return (self.theta[ipiv[routine]],self.names[level][routine],self.times[ipiv[routine]])
#if min(abs(to_find_zero))==0.0:
# routine=np.argmin(abs(to_find_zero))
# return (th,self.names[level][routine],self.times[ipiv[routine]])
#else:
# to_find_zero[to_find_zero > 0]=-4*np.pi
# routine=np.argmin(-to_find_zero)
# return (self.theta[ipiv[routine]],self.names[level][routine],self.times[ipiv[routine]])
def _info_string(self,xdata,level):
#create string to be plotted in plot
(tt,name,time)=self._find_name(xdata,level)
info=name+':\n'+str(time)+" s ("
info+= " %6.2f %s" % (time/self.tot*100.0,"% ) \n")
#find lower level
it=range(level)
it.reverse()
parents=''
for i in it:
(tt,name,time)=self._find_name(tt,i)
parents+="-"+name+"("+str(time)+"s,"
parents+= " %6.2f %s" % (time/self.tot*100.0,"% ) \n")
if len(parents) > 0:
info+="Calling path:\n"+parents
return info
def _new_level(self,event):
#click=event.mouseevent.button
click=event.button
datatmp=self.data
if click==3:
if not self.reset:
self.ax.texts.remove(self.comebackinfo)
self.reset=True
nameandlev=None
elif click==1:
#thisline = event.artist
#xdata, ydata = thisline.get_xy()
xdata, ydata=event.xdata,event.ydata
level = int(ydata)/self.step
goodpoint=self._find_name(xdata,level)
if goodpoint is None: return
(tt,name,time)=goodpoint
if not self.reset: return #no double level
self.reset=False
nameandlev=(name,level)
#extract the data to be plotted for the internal level
else:
return
self.subdata=self.dump_timing_level(datatmp,starting_point=nameandlev)
self.ax.cla()
self.draw_polarplot()
if not self.reset:
self.comebackinfo=self.ax.text(0.02,0.95,
'Subroutines of '+name+' (Right click to reset)',
fontsize=15,transform = self.ax.transAxes)
self.fig.canvas.draw()
def _info_callback(self,event):
#print dir(event)
#thisline = event.artist
#xdata, ydata = thisline.get_xy()
xdata=event.xdata#data
ydata=event.ydata#data
#print 'test',xdata,ydata,event.x,event.y
if xdata is None or ydata is None: return
level = int(ydata)/self.step
goodpoint=self._find_name(xdata,level)
#then plot the string
if self._wrotesome:
self.ax.texts.remove(self.info)
self.info=self.ax.text(0,0,'',fontsize=150)
self._wrotesome=False
if goodpoint is None: return
offset = 0.02
#print 'nowinfo',info
self.info= self.ax.text( offset, offset, self._info_string(xdata,level),
fontsize = 15,transform = self.ax.transAxes )
self._wrotesome=True
self.fig.canvas.draw()
[docs] def dump_timing_level(self,level,starting_point=None,ilev=0,theta=0,data=None): ##data={"time":[],"level":[],"theta":[],"names":[[]]},reset=False):
"""Inspect the first level of the given dictionary and dump the profile subroutines at this level"""
import pylab
if data is None: data={"time":[],"level":[],"theta":[],"names":[[]]}
subs=data["time"]
tht=data["theta"]
lev=data["level"]
nms=data["names"]
if ilev == len(nms):
nms.append([])
#tel=0
tel=theta #entry point of the routine
t0=0
start=starting_point
import copy
leveltmp=copy.deepcopy(level)
brk=False
for routine in leveltmp:
#first eliminate the subroutines from the level
try:
sublevel=routine.pop("Subroutines")
except:
sublevel=None
for name in routine:
if starting_point is not None:
if (name,ilev) != starting_point:
continue
else:
brk=True
start=None #from now on take the data
ilev=0
t0=routine[name][0] #take the time
subs.append(t0) #take the time
tht.append(tel)
lev.append(ilev)
nms[ilev].append(name)
if sublevel is not None:
jlev=ilev+1
self.dump_timing_level(sublevel,starting_point=start,ilev=jlev,theta=tel,data=data)
tel=tel+t0
if brk: break
return data
[docs]class TimeData:
barwidth=0.9#0.35
ignored_counters=['CPU parallelism','Routines timing and number of calls','SUMMARY','Report timestamp','Hostnames']
def __init__(self,*filenames,**kwargs):
"""
Class to analyze the timing from a Futile run
------
Arguments
filenames: list of the files which have to be treated
plottype: Decide the default yscale for the plotting
May be "Percent" or "Seconds"
static: Show the plot statically for screenshot use
fontsize: Determine fontsize of the bar chart plot
nokey: Remove the visualization of the key from the main plot
counter: retrieve as reference value the counter provided
only_last: retrieve only the last document in the time.yaml
"""
#here a try-catch section should be added for multiple documents
#if (len(filename) > 1
import Figures as Fig
only_last = kwargs.get('only_last',False)
self.log=[]
for filename in filenames:
import YamlIO as Yaml
tmplogs=Yaml.load(filename,doc_lists=True)
if only_last:
self.log+=[[ a for a in tmplogs][-1]]
else:
self.log+=tmplogs
#try:
# self.log+=[yaml.load(open(filename, "r").read(), Loader = yaml.CLoader)]
#except:
# self.log+=yaml.load_all(open(filename, "r").read(), Loader = yaml.CLoader)
#create the figure environemnt
self.figures=Fig.FigureSet(title='Profiling',QuitButton=True,twinaxes=True)
#self.barfig = None
#self.axbars = None
#self.newfigs =[]
self.radio = None
self.toggle_unbalancing = False
self.quitButton = None
self.lined=[]
self.plot_start=kwargs.get('plottype','Seconds')
self.static = kwargs.get('static',False)
self.fontsize=kwargs.get('fontsize',15)
self.nokey=kwargs.get('nokey',False)
self.strong_scaling=kwargs.get('strong_scaling',True)
counter=kwargs.get('counter','WFN_OPT') #the default value, to be customized
if counter in self.counters():
self.inspect_counter(counter)
else:
print "Warning: counter not initialized, check the available counters"
[docs] def counters(self):
"Inspect the available counters"
cnts=[]
for doc in self.log:
if doc is None: continue
for internal_cnts in doc.keys():
if internal_cnts not in cnts and internal_cnts not in self.ignored_counters: cnts.append(internal_cnts)
return cnts
def _refill_classes(self,main):
self.classes=[]
self.totals=[]
for doc in self.log:
if doc is None: continue
scf=doc.get(main)
if scf is None: continue
self.scf.append(scf)
self.routines.append(doc.get("Routines timing and number of calls"))
self.hostnames.append(doc.get("Hostnames"))
self.unbalancings.append(True)
loclass=scf["Classes"].keys()
if 'Total' in loclass:
self.totals.append(scf["Classes"]["Total"][1])
for cs in loclass:
if cs not in self.classes and cs != "Total": self.classes.append(cs)
if len(scf["Classes"][cs]) == 2: self.unbalancings[-1]=False
mpit=doc.get("CPU parallelism")
title=doc.get("Run name","Unknown")
if mpit is not None:
mpi=mpit.get('MPI tasks')
omp=mpit.get('OMP threads')
if title is "Unknown":
title=str(mpi) if omp is None else str(mpi)+'-'+str(omp)
ncores=mpi
if omp is not None: ncores*=omp
else:
ncores=0.0
self.ids.append(title)
self.ncores.append(ncores)
self.classes.sort()
self.classes.append("Unknown") #an evergreen
[docs] def inspect_counter(self,counter,unit=None):
self.routines=[]
self.hostnames=[]
self.unbalancings=[]
self.scf=[]
self.ids=[]
self.ncores=[] #number of cores for each of the valid run
self.vals=self.plot_start if unit is None else unit
self._refill_classes(counter)
#here we might add the policy to add new figures or delete previous ones
data=self.collect_categories(self.scf,self.vals)
fig,axis=self.figures[0]
axis.cla()
self.draw_barfigure(fig,axis,data,title="Counter "+counter)
[docs] def inspect_category(self,cat):
data=self.find_items(cat,self.scf)
if self.figures.exists(cat):
newfig,newax=self.figures[cat]
#erase the plot in case it is already available
newax.cla()
else:
newfig,newax=self.figures.add(title=cat,twinaxes=True)
self.draw_barfigure(newfig,newax,data,title=cat)
newfig.show()
[docs] def show(self):
self.figures.show()
def _strong_scaling_lines(self,data,axis):
totals=data[1]
ref=totals[0] # the reference value is assumed to be the first
coreref=float(self.ncores[0])
#print self.totals
if self.vals=='Seconds':
label='Speedup'
dataline=[ref/b for b in totals]
else:
label='Parallel Efficiency'
dataline=[ (ref/b)/(c/coreref) for b,c in zip(totals,self.ncores)]
self.draw_lineplot(axis.twin,dataline,label)
[docs] def routines_plot(self,index,event=None):
"Draw the plot of the routines level for the run identified by index"
toplt=self.routines[index]
title='Routines '+str(self.ids[index])+'-'+str(index)
if self.figures.exists(title): return
#data=dump_timing_level(toplt)#,starting_point='cluster')
fig,ax=self.figures.add(title=title,
axes={'rect':[0.025,0.025,0.95,0.95],
'polar':True})
if not hasattr(self,'routine_plots'): self.routine_plots=[]
plt=polar_axis(fig,ax,toplt)
self.routine_plots.append(plt)
fig.show()
[docs] def workload_plot(self,index,event=None):
"Draw the plot of the workload of different classes for the run identified by index"
from functools import partial
title='Unbalancing '+str(self.ids[index])+'-'+str(index)
if self.figures.exists(title): return
fig,ax=self.figures.add(title=title)
leg,plts,show=self.load_unbalancing(ax,self.scf[index]["Classes"],self.hostnames[index])
idx=len(self.lined)
self.lined.append({})
for legline, origline,yes in zip(leg.get_patches(), plts, show):
legline.set_picker(5) # 5 pts tolerance
self.lined[idx][legline] = origline
if not yes: self._toggle_visible(origline,legline)
fig.canvas.mpl_connect('pick_event', partial(self._onpick_curve,idx,fig))
fig.show()
[docs] def find_items(self,category,dict_list):
"""For a given category find the items which have them"""
import numpy as np
items={}
for idoc in range( len(dict_list) ):
for cat in dict_list[idoc]["Categories"]:
dicat=dict_list[idoc]["Categories"][cat]
if dicat["Class"] == category:
if cat not in items:
items[cat]=np.zeros(len(dict_list))
items[cat][idoc]=dicat["Data"][self.iprc]
return [ (cat,items[cat]) for cat in items],[ doc["Classes"][category][1] for doc in dict_list]
[docs] def collect_categories(self,dict_list,vals):
""" Collect all the categories which belong to all the dictionaries """
import numpy as np
if vals == 'Percent':
self.iprc=0
elif vals == 'Seconds':
self.iprc=1
catsdats=[]
self.values_legend=[]
totals=[]
for cat in self.classes:
try:
if cat == "Unknown": #this is always done
data_unk=[]
for doc in dict_list:
totals.append(doc["Classes"]["Total"][1])
percent_unk=100.0-doc["Classes"]["Total"][0]
if self.iprc==0:
data_unk.append(percent_unk)
elif self.iprc==1:
time_unk=(doc["Classes"]["Total"][1])*percent_unk/100.0
data_unk.append(time_unk)
dat=np.array(data_unk)
else:
dat=np.array([doc["Classes"][cat][self.iprc] for doc in dict_list])
catsdats.append((cat,dat))
self.values_legend.append(cat)
except Exception,e:
print 'EXCEPTION FOUND',e
print "category",cat,"not present everywhere"
return catsdats,totals
def _onclick_ev(self,fig,axis,event):
import matplotlib.pyplot as plt
#thisline = event.artist
#xdata, ydata = thisline.get_xy()
#print dir(event)
#print event.inaxes
xdata,ydata=event.x,event.y #event.xdata,event.ydata
if xdata is None or ydata is None: return
#print 'picked',xdata,ydata
xdata,ydata=axis.transData.inverted().transform((xdata,ydata))
#print 'transformed',xdata,ydata
#data_from_data(fig,dst=axis,src=axis.twin,data=(int(xdata),ydata))
#print 'converted',xdata,ydata
if ydata < 0.0 or xdata < 0.0 or xdata >= len(self.scf): return
xdata=int(xdata)
#find the category which has been identified
y0data=0.0
category=None
for cat in self.values_legend:
if cat == 'Unknown': continue
y0data+=self.scf[xdata]["Classes"][cat][self.iprc]
#print 'cat,y0data',cat,y0data,ydata
if y0data > ydata:
category=cat
break
#print 'category',category
if category is not None and category != "Unknown": self.inspect_category(category)
def _barplot_gnuplot(self,data,ids,lookup=None,select_category=None):
import Gnuplot
import numpy as np
gpdata=[]
for cat,dat in data:
if select_category is not None and cat not in select_category: continue
#gpdata.append(Gnuplot.Data(np.arange(len(dat)),dat,title=cat))
lup=np.arange(len(dat)) if lookup is None else lookup
toplt=[dat[0]]+dat[lup].tolist()
gpdata.append(Gnuplot.Data(toplt,title=cat))
names=[ids[i] for i in lup]
return gpdata,names
def _aggregate_names(self,data,list_agg):
"""
Aggregate the names of the plot in some different categories
the structure of dict_agg should ba a list of tuples
(newkey ,[list of oldkeys])
between the original keys and the desired keys
"""
newdata={newkey: 0.0 for newkey,oldkeys in list_agg}
for cat,dat in data:
found=False
for newkey,oldkeys in list_agg:
if cat in oldkeys:
newdata[newkey]+=dat
found=True
break
if not found: newdata[cat]=dat
result=[]
for newkeys,oldkeys in list_agg:
result.append((newkeys,newdata.pop(newkeys)))
for newkeys in newdata:
result.append((newkeys,newdata[newkeys]))
return result
def _draw_barplot(self,axbars,data,vals,title='Time bar chart',static=False,nokey=False):
import numpy as np
from pylab import cm as cm
ndata=len(data[0][1])
ind=np.arange(ndata)
bot=np.array(ndata*[0.0])
width=self.barwidth
icol=1.0
for cat,dat in data:
#print 'cat',cat,dat
#for i in range(len(self.ids)):
# print self.ids[i],dat[i]
plt=axbars.bar(ind,dat,width,bottom=bot,color=cm.jet(icol/len(data)),picker=True,label=cat)
#self.plts.append(plt)
bot+=dat
icol+=1.0
#drawn_classes=np.array(self.values_legend)
axbars.set_title(title,fontsize=self.fontsize)
axbars.set_ylabel(vals,fontsize=self.fontsize)
axbars.set_xticks(ind+width/2.)
axbars.set_xticklabels(np.array(self.ids),size=self.fontsize)
if not nokey:
#self.leg = axbars.legend(loc='upper right',fontsize=self.fontsize)
self.leg = axbars.legend(loc='best',fontsize=self.fontsize)
self.leg.get_frame().set_alpha(0.4)
#treat the totals differently
return bot
[docs] def draw_lineplot(self,ax,data,label):
#ax=axbars.twinx()
import numpy as np
ind=np.arange(len(data))+self.barwidth/2.
ax.plot(ind,data,'o-',color='red',label=label)
ax.set_ylabel(label,fontsize=self.fontsize)
[docs] def replot(self,label):
self.vals=label
#print self.vals
for i,fig in enumerate(self.figures):
fx,ax=fig
cat=ax.get_title()
ax.cla()
if i==0:
data=self.collect_categories(self.scf,label)
else:
data=self.find_items(cat,self.scf)
if data != []: self.draw_barfigure(fx,ax,data,cat)
[docs] def unbalanced(self,val):
"""Criterion for unbalancing"""
return val > 1.1 or val < 0.9
[docs] def find_unbalanced(self,data):
"""Determine lookup array of unbalanced categories"""
ipiv=[]
for i in range(len(data)):
if self.unbalanced(data[i]):
ipiv.append(i)
return ipiv
[docs] def load_unbalancing(self,ax,dict,hosts):
"""Extract the data for plotting the hostname balancings between different categories in bar chart"""
import numpy as np
from pylab import cm
width=0.50
plts=[]
key_legend=[]
values_legend=[]
icol=1.0
show_initially=[]
for cat in self.classes:
if cat=='Unknown': continue
try:
unb=np.array(dict[cat][2:])
#print 'unbalancing',unb
#unb2=self.find_unbalanced(unb)
#print 'unbalanced objects',cat
#if hosts is not None and (cat=='Convolutions' or cat =='Communications'):
#print 'unb2',unb2,len(unb),len(hosts)
#print 'vals',[ [i,unb[i],hosts[i]] for i in unb2]
ind=np.arange(len(unb))
pltmp=ax.bar(ind,unb,width,color=cm.jet(icol/len(self.classes)))
plts.append(pltmp)
key_legend.append(pltmp[0])
values_legend.append(cat)
show_initially.append(dict[cat][0] > 5.0)
icol+=1.0
if (width > 0.05):
width -= 0.05
except Exception,e:
print 'EXCEPTION FOUND',e
print "cat",cat,"not found in workload data"
return None
if len(ind) > 2:
tmp=np.array(hosts) if hosts is not None else None
else:
tmp=np.array(["max","min"])
ax.set_ylabel('Load Unbalancing wrt average')
ax.set_title('Work Load of different classes')
if tmp is not None:
ax.set_xticks(ind+width/2.)
ax.set_xticklabels(tmp,rotation=90,verticalalignment='bottom')
ax.set_yticks(np.arange(0,2,0.25))
ax.axhline(1.0,color='k',linestyle='--')
leg=ax.legend(np.array(key_legend),np.array(values_legend))#,fancybox=True, shadow=True)
#leg.get_frame().set_alpha(0.4)
#show only the most relevant categories
return leg,plts,show_initially
def _onpick_curve(self,index,fig,event):
# on the pick event, find the orig line corresponding to the
# legend proxy line, and toggle the visibility
legline = event.artist
origline = self.lined[index][legline]
self._toggle_visible(origline,legline)
fig.canvas.draw()
def _toggle_visible(self,data,legend):
for val in data:
vis = not val.get_visible()
val.set_visible(vis)
#last value is like all the others
if vis:
legend.set_alpha(1.0)
else:
legend.set_alpha(0.2)
