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#!/fs/sz-user-supported/Linux-i686/bin/python2.5
import matplotlib
matplotlib.use('PS')
import pylab
import csv
def get_stats(filename):
stats = {}
statfile = open(filename)
stats = dict([(key, float(value)) for (key, value) in csv.reader(statfile)])
return stats
from pylab import arange,pi,sin,cos,sqrt
def set_figure_props():
fig_width_pt = 225.0 # Get this from LaTeX using \showthe\columnwidth
inches_per_pt = 1.0/72.27 # Convert pt to inch
golden_mean = (sqrt(5)-1.0)/2.0 # Aesthetic ratio
fig_width = fig_width_pt*inches_per_pt # width in inches
fig_height = 1.5*fig_width*golden_mean # height in inches
fig_size = [fig_width,fig_height]
params = {'backend': 'ps',
'axes.labelsize': 8,
'axes.linewidth': 0.5,
'text.fontsize': 8,
'xtick.labelsize': 7,
'ytick.labelsize': 7,
'legend.fontsize': 7,
'legend.linewidth':0.5,
'title.fontsize' : 8,
'text.usetex': True,
'figure.figsize': fig_size}
pylab.rcParams.update(params)
def draw_speedup_figures(outfile, fig_title):
query_lens = []
app_speedups = []
kernel_speedups = []
f = open(outfile)
headers = f.next()
headers = headers.strip()
headers = headers.split(',')
query_col = headers.index("QUERY")
kernel_col = headers.index("KERNEL_SPEEDUP")
mummer_speed_col = headers.index("MUMMER_SPEEDUP")
for vals in csv.reader(f, 'excel', delimiter=' '):
query_lens.append(int(vals[query_col]))
app_speedups.append(float(vals[mummer_speed_col]))
kernel_speedups.append(float(vals[kernel_col]))
draw_speedup_fig(query_lens,
kernel_speedups,
fig_title,
outfile + ".kernel_speedup.eps")
def draw_speedup_fig(x, y, fig_title, filename):
set_figure_props()
ax = pylab.subplot(111)
pylab.semilogx(x, y, linestyle=':', marker='v', basex=2)
pylab.xticks(x)
frm = pylab.FormatStrFormatter("%d")
ax.xaxis.set_major_formatter(frm)
ax.xaxis.grid(True, which="minor")
pylab.xlabel("Query length (bp - log scale)")
pylab.ylabel("Speedup")
pylab.title(fig_title, fontsize=9)
pylab.savefig(filename)
pylab.close()
def make_time_breakout():
statfiles = ["cbriggsae/cleanreads.fna-100.gpustats",
"lmonocytogenes/cleanreads.fna-20.gpustats",
"s_suis/cleanreads.fna-20.gpustats"
]
labels = [ "\emph{C. briggsae}",
"\emph{L. monocytogenes}",
"\emph{S. suis} "
]
stats = {}
convert_to_seconds = ["Total",
"Kernel",
"Print matches",
"Copy queries to GPU",
"Copy output from GPU",
"Copy suffix tree to GPU",
"Read queries from disk",
"Suffix tree constructions"]
for f in statfiles:
f_stats = get_stats(f)
for (key, value) in f_stats.iteritems():
if key in convert_to_seconds:
val = value / 1000.0 #float( value/f_stats["TOTAL"]
else:
val = int(value)
if key in stats:
stats[key].append(val)
else:
stats[key] = [val]
ind = arange(0,3.6,1.2 ) # the x locations for the groups
width = 0.35 # the width of the bars: can also be len(x) sequence
i = 0
## colors = [ "#e31a1c",
## "#377db8",
## "#4daf4a",
## "#984ea3",
## "#ffff33",
## "#ff7f00"]
colors = [ "#FF4500",
"#1E90FF",
"#90EE90",
"#FFD700",
"#DA70D6",
"#D2B48C"]
set_figure_props()
pylab.subplot(111)
transfer = []
for j in range(0, len(statfiles)):
transfer.append( stats["Copy suffix tree to GPU"][j] + stats["Copy output from GPU"][j] + stats["Copy queries to GPU"][j])
stats["Data transfer to GPU"] = transfer
del stats["Copy suffix tree to GPU"]
del stats["Copy output from GPU"]
del stats["Copy queries to GPU"]
del stats["Total"]
lengths = stats["Minimum substring length"]
del stats["Minimum substring length"]
del stats["Average query length"]
plots = []
running_totals = [0 for j in range(0,len(statfiles))]
for (category, series) in stats.iteritems():
plots.append(pylab.bar(ind, series, width, color=colors[i], bottom=running_totals))
running_totals = [running_totals[j] + series[j] for j in range(0, len(series))]
i += 1
#pylab.xticks(ind+width/2., labels )
pylab.xticks(ind +width/2., labels )
pylab.title("Time spent by phase in MUMmerGPU", fontsize=9)
pylab.ylabel("time (s)")
pylab.xlim(-width,len(ind))
pylab.ylim(0, 600)
pylab.legend( [p[0] for p in reversed(plots)], [key for key in reversed(stats.keys())] )
pylab.savefig('time_breakout.eps')
pylab.close()
make_time_breakout()
draw_speedup_figures("anthrax/speedup.out", "Kernel speedup, GPU vs. CPU")
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