def parse_bsize(t):
bsize, lis = t
x = []
y = []
for csv in humansorted(lis):
e = Experiment(csv)
size = None
total_dups = None
if "pf" in csv:
size = basename(dirname(csv)).split("_")[2]
total_dups = sum(e.get_duplicate_faults_64k())
else:
size = basename(dirname(csv)).split("_")[1]
total_dups = sum(e.get_duplicate_faults_4k())
size = size
x.append(size)
y.append(total_dups)
return (x, y, bsize)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('csv', type=str, help='Full path to CSV file')
parser.add_argument('-o', type=str, default="", help='output filename')
parser.add_argument('-d', type=str, default="", help='out dir')
args = parser.parse_args()
c = args.csv
m = "*"
if not ".txt" in args.csv:
print("Suspicious input file with no/wrong extension:", args.csv)
exit(1)
e = Experiment(c)
ranges, faults, batches, num_batches = e.get_raw_data()
e.print_info()
matplotlib.rcParams['agg.path.chunksize'] = 10000
#fig = plt.gcf()
#ax = fig.add_subplot(1,1,1)
#ax2 = ax.twiny()
read = []
rx = []
write = []
wx = []
for i, f in enumerate(faults):
if f.access_type == "r":
#read.append(f.fault_address)
#rx.append(i)
pass
elif f.access_type == "w":
#write.append(f.fault_address)
#wx.append(i)
pass
elif f.access_type == "p":
pass
else:
print("unaccounted for access type:", f.access_type)
true_bl = [len(batch) for batch in batches]
bl = [0] + true_bl
for i, b in enumerate(bl):
if i == 0:
continue
bl[i] += bl[i - 1]
del bl[0]
#print(batches)
sm_ids = sorted(e.count_utlb_client_pairs().keys())
print("sm_ids:", sm_ids)
cmap = plt.get_cmap('jet')
colors = cmap(np.linspace(0, 1.0, len(sm_ids)))
id_f_map = {sm_id: [] for sm_id in sm_ids}
for fault in faults:
mapping = (fault.utlb_id, fault.client_id)
id_f_map[mapping].append(fault)
times_between_batches = e.batch_times
avg = e.avg_time_batches()
print("avg time between batches:", avg)
print("min time between batches", min(times_between_batches))
print("max time between batches", max(times_between_batches))
print("total # deltas:", len(times_between_batches))
print("Num batches < 1e1:",
len([t for t in times_between_batches if t < 1e1]))
print("Num batches < 1e2:",
len([t for t in times_between_batches if t < 1e2]))
print("Num batches < 1e3:",
len([t for t in times_between_batches if t < 1e3]))
print("Num batches < 1e4:",
len([t for t in times_between_batches if t < 1e4]))
print("Num batches < 1e5:",
len([t for t in times_between_batches if t < 1e5]))
print("Num batches < 1e6:",
len([t for t in times_between_batches if t < 1e6]))
print("Num batches < 1e7:",
len([t for t in times_between_batches if t < 1e7]))
print("Num batches < 1e8:",
len([t for t in times_between_batches if t < 1e8]))
Q1 = np.quantile(times_between_batches, 0.25)
Q3 = np.quantile(times_between_batches, 0.75)
med = statistics.median(times_between_batches)
avg = np.mean(times_between_batches)
print("Q1, median, Q3:", Q1, ",", med, ",", Q3)
#plt.plot(times, counts, marker="*")
hist, bins, _ = plt.hist(times_between_batches)
#hist, bins, _ = plt.hist(times_between_batches, bins=16)
binlen = len(bins)
print("bins:", bins)
logbins = np.logspace(0.0, np.log10(bins[-1]), len(bins))
print("logbins:", logbins)
plt.clf()
hist, bins, _ = plt.hist(times_between_batches, bins=logbins)
ax = plt.gca()
plt.vlines([Q1, med, Q3],
0,
1,
transform=ax.get_xaxis_transform(),
label="Q1/Med/Q3")
plt.vlines([avg],
0,
1,
transform=ax.get_xaxis_transform(),
label="Avg",
color="r")
plt.xlim(xmin=1e0)
plt.ylim(ymin=0.0)
plt.xscale("log")
plt.xlabel("Time Between Batch Fault Arrival in Buffer (NS)")
plt.ylabel("Frequency")
plt.legend()
psize = basename(dirname(args.csv)) #.split("_")[-1]
print("psize:", psize)
figname = None
if args.o == "":
figname = (args.d + "/" + splitext(basename(args.csv))[0] + "-" +
psize + "-batch-time-dist.png").replace("_", "-")
else:
figname = args.o
if ".png" not in figname:
figname += ".png"
#figname = splitext(basename(args.csv))[0].split("_")[0] + "-" + psize + "-sm-time-dist.png"
plt.tight_layout()
print('saving figure:', figname)
plt.savefig(figname, dpi=500)
plt.close()
if ("pf") in args.csv:
xs = [
len(batch) - dup + len(pfbatch) for batch, dup, pfbatch in zip(
e.batches, e.get_duplicate_faults_64k(), e.pfbatches)
]
else:
xs = [
len(batch) - dup
for batch, dup in zip(e.batches, e.get_duplicate_faults_4k())
]
#xs = [len(batch) - e.get_duplicate_faults_4k() for batch in e.batches]
hist, bins, _ = plt.hist(xs, bins=len(logbins))
logbins = np.logspace(0.0, np.log10(bins[-1]), len(bins))
plt.clf()
hist, bins, _ = plt.hist(xs, bins=logbins)
#plt.hist([len(batch) for batch in e.batches])
plt.xlabel("Batch Sizes")
plt.ylabel("Frequency")
plt.xlim(xmin=1e0)
plt.ylim(ymin=0.0)
plt.xscale("log")
if args.o == "":
figname = (args.d + "/" + splitext(basename(args.csv))[0] + "-" +
psize + "-batch-size-dist.png").replace("_", "-")
else:
figname = args.o
if ".png" not in figname:
figname += ".png"
plt.tight_layout()
print('saving figure:', figname)
plt.savefig(figname, dpi=500)
plt.close()
ys = times_between_batches
size_time_plot(xs, ys, psize, args, "pf-dups")
if ("pf") in args.csv:
xs = [
len(batch) - dup
for batch, dup in zip(e.batches, e.get_duplicate_faults_64k())
]
else:
xs = [
len(batch) - dup
for batch, dup in zip(e.batches, e.get_duplicate_faults_4k())
]
#xs = [len(batch) - e.get_duplicate_faults_4k() for batch in e.batches]
size_time_plot(xs, ys, psize, args, "dups")
if ("pf") in args.csv:
xs = [len(batch) for batch in e.batches]
else:
xs = [len(batch) for batch in e.batches]
size_time_plot(xs, ys, psize, args, "")
div = 65536 if "pf" in args.csv else 4096
xs = []
for batch in batches:
vals = sorted(set([f.fault_address // div for f in batch]))
transfers = 0
prev = vals[0]
count = 0
for val in vals:
if val > 1 + prev or val % (2097152 // div) == 0:
transfers += 1
count = 0
else:
count += 1
prev = val
if count > 0:
transfers += 1
xs.append(transfers)
size_time_plot(xs, ys, psize, args, "transfers")