def stacked_bar_plot(filenames, vecs, xticklabels=None):
cycles = []
for i, filename in enumerate(filenames):
pltutils.set_corpus_stats(dirname(filename))
_, _, _, cls, _, _ = pltutils.read_one_output(filename, vec=vecs[i])
cycles.append(cls)
pass
cycles = np.array(cycles)
cycles = np.transpose(cycles)
width = 0.75
ind = np.arange(len(filenames))
bottom = np.zeros(len(ind))
fig, ax = plt.subplots()
p = [None] * len(fns)
for i in range(len(fns)):
p[i] = ax.bar(ind,
cycles[i],
width,
bottom=bottom,
color=colors[fns[i]])
bottom = np.sum([bottom, cycles[i]], axis=0)
plt.legend(p, fns)
if xticklabels is not None:
ax.set_xticklabels(xticklabels)
ax.set_ylabel('cycle component')
ax.set_title('#cycle counts for different runs per group')
ax.set_xticks(ind)
plt.show()
def perf_plot(filenames, vecs, legends=None):
width = 1 / (len(filenames) + 1.) # width of bars
ind = np.arange(len(fns[:-3]))
fig, ax = plt.subplots()
p = [None] * len(filenames)
#print(fns)
for i, filename in enumerate(filenames):
pltutils.set_corpus_stats(dirname(filename))
_, _, flp, cls, _, perf = pltutils.read_one_output(filename,
vec=vecs[i])
p[i] = ax.bar(ind + i * width, perf[:-3], width, color=colors2[i])
ax.set_xticks(ind)
ax.set_xticklabels(fns)
plt.setp(ax.get_xticklabels(), rotation=30, horizontalalignment='center')
ax.set_ylabel('Performance [Flops/Cycle]', rotation="0", size=28)
ax.yaxis.set_label_coords(0.22, 1.03)
fig.tight_layout()
# ax.set_title('performance for different runs per group')
ax.tick_params(axis='x', labelsize=20)
ax.tick_params(axis='y', labelsize=24)
ax.set_facecolor((211.0 / 255, 211.0 / 255, 211.0 / 255))
if legends is not None:
plt.legend(p, legends, prop={'size': 22})
ax.grid(linestyle='--', linewidth=2, axis='y')
plt.show()
def read_one_timer_all_Ns(path, timer_name, label="", xaxis='N'):
data = {'x' : [], 'y' : []}
pltutils.set_corpus_stats(path)
for filename in listdir(path):
if("timings" in filename):
fname = join(path, filename)
K, N, _, _, _, perf = pltutils.read_one_output(fname)
data['x'].append(K if xaxis == 'K' else N)
data['y'].append(perf[fns.index(timer_name)])
# amend the label afterwards so we can obtain the automated cycle count for the timer
return {label + " " + timer_name : data}
def pie_chart(filenames, vecs, xticklabels=None):
if len(filenames) > 1:
print("Pie chart can only be done for one file")
sys.exit()
cycles = []
pltutils.set_corpus_stats(dirname(filenames[0]))
_, _, _, cls, _, _ = pltutils.read_one_output(filenames[0], vec=vecs[0])
cycles = cls
# cycles = np.array(cycles)
# cycles = np.transpose(cycles)
# print(cycles)
select_cycles = np.array(cycles)
# Manually pick the functions we want
select_cycles = select_cycles[np.array([1, 2, 3, 5, 6])]
# print(select_cycles)
# width = 0.75
ind = np.arange(len(filenames))
# bottom = np.zeros(len(ind))
print(plt.style.available)
plt.style.use('seaborn-colorblind')
fig, ax = plt.subplots()
# p = [None] * len(fns)
select_fns = np.array(fns)
select_fns = select_fns[np.array([1, 2, 3, 5, 6])]
# print(select_fns)
# p[i] = ax.bar(ind, cycles[i], width, bottom=bottom, color=colors[fns[i]])
ax.pie(select_cycles,
explode=None,
labels=select_fns,
autopct='%1.1f%%',
pctdistance=0.7,
startangle=45,
labeldistance=1.1,
textprops={'fontsize': 13})
ax.axis(
'equal') # Equal aspect ratio ensures that pie is drawn as a circle.
# bottom = np.sum([bottom, cycles[i]], axis=0)
# plt.legend(p, fns)
# if xticklabels is not None:
# ax.set_xticklabels(xticklabels)
# ax.set_ylabel('cycle component')
# ax.set_title('Distribution of the total runtime over functions', fontdict={'fontsize': 18})
# ax.set_xticks(ind)
plt.show()
def conv_plot(filenames, vecs, legends=None):
width = 1 / (len(filenames) + 1.) # width of bars
its = list(pltutils.iters.keys())
ind = np.arange(len(its))
fig, ax = plt.subplots()
p = [None] * len(filenames)
for i, filename in enumerate(filenames):
pltutils.set_corpus_stats(dirname(filename))
pltutils.read_one_output(filename, vec=vecs[i])
convs = [pltutils.iters[it] for it in its]
p[i] = ax.bar(ind + i * width, convs, width, color=colors2[i])
ax.set_ylabel('#convergence iters')
ax.set_title('#convergence iters for different runs per group')
ax.set_xticks(ind)
ax.set_xticklabels(its)
if legends is not None:
plt.legend(p, legends)
ax.grid(linestyle='--', linewidth=2, axis='y')
plt.show()
def avgc_plot(filenames, vecs, legends=None):
width = 1 / (len(filenames) + 1.) # width of bars
ind = np.arange(len(fns))
fig, ax = plt.subplots()
p = [None] * len(filenames)
for i, filename in enumerate(filenames):
pltutils.set_corpus_stats(dirname(filename))
_, _, _, _, avg_cycles, _ = pltutils.read_one_output(filename,
vec=vecs[i])
p[i] = ax.bar(ind + i * width, avg_cycles, width, color=colors2[i])
ax.set_ylabel('avg cycle count')
ax.set_title('Avg Cycles counts for different runs per group')
ax.set_xticks(ind)
ax.set_xticklabels(fns)
ax.set_yscale("log")
if legends is not None:
plt.legend(p, legends)
ax.grid(linestyle='--', linewidth=2, axis='y')
plt.show()
def benchmark(dirpath, vec=False, xaxis='N'):
pltutils.set_corpus_stats(dirpath)
data_1 = {} # "RUN_EM" : { x : [10, 15, 20], y : [3.2, 4.5, 6.7] }
data_2 = {} # "RUN_EM" : { x : [10, 15, 20], y : [3.2, 4.5, 6.7] }
_, axes = plt.subplots()
for filename in listdir(dirpath):
if filename.startswith("fast") or filename.startswith("slow"):
K, N, _, _, _, perf = pltutils.read_one_output(join(dirpath, filename), vec=vec)
dic = data_1 if filename[0] == 'f' else data_2
for i, fn in enumerate(pltutils.fns):
if not fn in dic: dic[fn] = {'x' : [], 'y' : []}
dic[fn]['x'].append(K if xaxis == 'K' else N)
dic[fn]['y'].append(perf[i])
set_up_perf_plot(axes, xaxis)
plot_line(plt, data_1)
plot_line(plt, data_2)
plt.show()
def bar_plot(filenames, vecs, legends=None):
width = 1 / (len(filenames) + 1.) # width of bars
ind = np.arange(len(fns[:7]))
fig, ax = plt.subplots()
p = [None] * len(filenames)
for i, filename in enumerate(filenames):
pltutils.set_corpus_stats(dirname(filename))
_, _, _, cycles, _, _ = pltutils.read_one_output(filename, vec=vecs[i])
p[i] = ax.bar(ind + i * width, cycles[:7], width, color=colors2[i])
ax.set_ylabel('Runtime [Cycles]', rotation="0", size=28)
ax.yaxis.set_label_coords(0, 1.03)
#ax.set_title('performance for different runs per group')
ax.tick_params(labelsize=24)
ax.set_facecolor((211.0 / 255, 211.0 / 255, 211.0 / 255))
ax.set_xticklabels(fns[:7])
ax.set_xticks(ind)
ax.set_yscale("log")
if legends is not None:
plt.legend(p, legends, prop={'size': 24})
ax.grid(linestyle='--', linewidth=2, axis='y')
plt.show()
def run_comp(filenames, vecs, legends=None):
ind = np.arange(len(filenames))
fig, ax = plt.subplots()
perfs = []
for i, filename in enumerate(filenames):
pltutils.set_corpus_stats(dirname(filename))
_, _, flp, cls, _, perf = pltutils.read_one_output(filename,
vec=vecs[i])
perfs.append(perf[0])
p = ax.plot(perfs,
linestyle='solid',
marker='o',
linewidth=2,
color='deeppink')
ax.set_ylabel('Performance [Flops/Cycle]', rotation="0", size=28)
ax.yaxis.set_label_coords(0.1, 1.03)
#ax.set_title('performance for different runs per group')
ax.tick_params(labelsize=24)
ax.set_facecolor((211.0 / 255, 211.0 / 255, 211.0 / 255))
ax.set_xticklabels(legends)
ax.set_xticks(ind)
ax.grid(linestyle='--', linewidth=2, axis='y')
plt.show()
def parse_perf_files(dir_path):
pltutils.set_corpus_stats(dir_path)
operational_intensity = []
memory_reads = []
memory_writes = []
flop_count = []
num_docs = []
with open(dir_path + '/info.txt') as f:
for ln in f:
if ln.startswith('Comment: '):
#Remove beginning and last quote/newline
comment = ln[len('Comment: "') : -2]
break
#Get the memory transfers from perf files
regex = re.compile(r'\d+')
for filename in os.listdir(dir_path):
if "perf" in filename:
K, N = map(int, re.findall(regex, filename))
num_docs.append((K, N))
for line in open(dir_path + "/" + filename):
if "LLC-load-misses" in line:
tokens = line.split()
number_parts = tokens[0].split(",")
memory_reads.append(float(''.join(number_parts)))
if "LLC-store-misses" in line:
tokens = line.split()
number_parts = tokens[0].split(",")
memory_writes.append(float(''.join(number_parts)))
memory_transfers = [x + y for x, y in zip(memory_reads, memory_writes)]
#Consider the number of bytes transfered as the number of cache misses * the cache line size
cache_line_size = 64.0
bytes_transfers = [x * cache_line_size for x in memory_transfers]
flop_count = [0] * len(bytes_transfers)
tuples_sorted = sorted(zip(num_docs, bytes_transfers))
num_docs = [x[0] for x in tuples_sorted]
bytes_transfers = [x[1] for x in tuples_sorted]
#Get the flop count and the performance from the timings
data = {'x' : [], 'y' : []}
for filename in os.listdir(dir_path):
if("timings" in filename):
fullname = join(dir_path, filename)
# Extract K and N from the filename
K, N = map(int, re.findall(regex, filename))
if (K, N) in num_docs:
k1, n1, flops, _, _, perf = pltutils.read_one_output(fullname)
assert k1 == K and n1 == N, "Wrong file"
flop_count[ num_docs.index((K, N)) ] = flops[ fns.index("RUN_EM") ]
data['x'].append((K,N))
data['y'].append(perf[ fns.index("RUN_EM") ])
#print(flop_count)
pass
pass
pass
operational_intensity = [x / y for x, y in zip(flop_count, bytes_transfers)]
plt_op = []
plt_perf = []
for i in range(len(num_docs)):
kn = num_docs[i]
if kn in data['x']:
idx = data['x'].index(kn)
assert flop_count[i] is not 0
plt_op.append(operational_intensity[i])
plt_perf.append(data['y'][idx])
return Run(plt_op, plt_perf, num_docs, comment)