def get_reasons_for_fetch_stall(output_file):
grep_lines = mfgraph.grep(output_file, ".*Fetch i-cache miss.*")
line = string.split(grep_lines[0])
value_str = string.split(line[6], "%")
icache_miss = float(value_str[0])
grep_lines = mfgraph.grep(output_file, ".*Fetch squash.*")
line = string.split(grep_lines[0])
value_str = string.split(line[5], "%")
squash = float(value_str[0])
grep_lines = mfgraph.grep(output_file, ".*Fetch I-TLB miss.*")
line = string.split(grep_lines[0])
value_str = string.split(line[6], "%")
itlb_miss = float(value_str[0])
grep_lines = mfgraph.grep(output_file, ".*Window Full.*")
line = string.split(grep_lines[0])
value_str = string.split(line[5], "%")
window_full = float(value_str[0])
grep_lines = mfgraph.grep(output_file, ".*Fetch Barrier.*")
line = string.split(grep_lines[0])
value_str = string.split(line[5], "%")
barrier = float(value_str[0])
width = get_issue_width(output_file)
return [width, icache_miss, squash, itlb_miss, window_full, barrier]
def get_reasons_for_fetch_stall(output_file):
grep_lines = mfgraph.grep(output_file, ".*Fetch i-cache miss.*")
line = string.split(grep_lines[0])
value_str = string.split(line[6], "%")
icache_miss = float(value_str[0])
grep_lines = mfgraph.grep(output_file, ".*Fetch squash.*")
line = string.split(grep_lines[0])
value_str = string.split(line[5], "%")
squash = float(value_str[0])
grep_lines = mfgraph.grep(output_file, ".*Fetch I-TLB miss.*")
line = string.split(grep_lines[0])
value_str = string.split(line[6], "%")
itlb_miss = float(value_str[0])
grep_lines = mfgraph.grep(output_file, ".*Window Full.*")
line = string.split(grep_lines[0])
value_str = string.split(line[5], "%")
window_full = float(value_str[0])
grep_lines = mfgraph.grep(output_file, ".*Fetch Barrier.*")
line = string.split(grep_lines[0])
value_str = string.split(line[5], "%")
barrier = float(value_str[0])
width = get_issue_width(output_file)
return [width, icache_miss, squash, itlb_miss, window_full, barrier]
def get_tuning_data(results_dir):
map = {}
data = []
directories = glob.glob(results_dir + "/*")
for dir in directories:
filename = dir + "/ECperf.summary"
if os.access(filename, os.F_OK):
grep_lines = mfgraph.grep(filename, ".+Metric.+")
line = string.split(grep_lines[0])
throughput = float(line[3])
filename = dir + "/config.summary"
grep_lines = mfgraph.grep(filename, "Thread Count")
line = string.split(grep_lines[0])
threads = int(line[2])
grep_lines = mfgraph.grep(filename, "Tx Rate")
line = string.split(grep_lines[0])
tx_rate = int(line[2])
procs = ecperf_get_num_procs(directory = dir)
filename = dir + "/Mfg.summary"
grep_lines = mfgraph.grep(filename, "ECPerf Requirement for 90")
line = string.split(grep_lines[0])
passed_mfg = line[6]
filename = dir + "/Orders.summary"
grep_lines = mfgraph.grep(filename, "ECPerf Requirement for 90")
line = string.split(grep_lines[0])
passed_ords = line[6]
tuple = [procs,
throughput,
tx_rate,
threads,
passed_ords,
passed_mfg,
os.path.basename(dir)]
data.append(tuple)
# key = threads
# if map.has_key(key):
# map[key].append(tuple)
# else:
# map[key] = []
# map[key].append(tuple)
data.sort()
print "Run Directory: %s" % results_dir
print "procs score tx_rate threads ords mfg directory"
for tuple in data:
print "%d %.1f %d %d %s %s %s" % (tuple[0], tuple[1], tuple[2], tuple[3], tuple[4], tuple[5], tuple[6])
return data
def get_tuning_data(results_dir):
map = {}
data = []
directories = glob.glob(results_dir + "/*")
for dir in directories:
filename = dir + "/ECperf.summary"
if os.access(filename, os.F_OK):
grep_lines = mfgraph.grep(filename, ".+Metric.+")
line = string.split(grep_lines[0])
throughput = float(line[3])
filename = dir + "/config.summary"
grep_lines = mfgraph.grep(filename, "Thread Count")
line = string.split(grep_lines[0])
threads = int(line[2])
grep_lines = mfgraph.grep(filename, "Tx Rate")
line = string.split(grep_lines[0])
tx_rate = int(line[2])
procs = ecperf_get_num_procs(directory=dir)
filename = dir + "/Mfg.summary"
grep_lines = mfgraph.grep(filename, "ECPerf Requirement for 90")
line = string.split(grep_lines[0])
passed_mfg = line[6]
filename = dir + "/Orders.summary"
grep_lines = mfgraph.grep(filename, "ECPerf Requirement for 90")
line = string.split(grep_lines[0])
passed_ords = line[6]
tuple = [
procs, throughput, tx_rate, threads, passed_ords, passed_mfg,
os.path.basename(dir)
]
data.append(tuple)
# key = threads
# if map.has_key(key):
# map[key].append(tuple)
# else:
# map[key] = []
# map[key].append(tuple)
data.sort()
print "Run Directory: %s" % results_dir
print "procs score tx_rate threads ords mfg directory"
for tuple in data:
print "%d %.1f %d %d %s %s %s" % (tuple[0], tuple[1], tuple[2],
tuple[3], tuple[4], tuple[5],
tuple[6])
return data
def gen_scale(benchmarks):
configs = ["1p", "2p", "4p", "8p", "16p"]
base_config = "1p"
parameter = "Ruby_cycles"
stacks = [];
print "parsing..."
for benchmark in benchmarks:
assoc_data = {};
for config in configs:
sys.stderr.write(" %s %s\n" % (benchmark, config))
numbers = []
filenames = glob.glob(benchmark + "/*-" + config + "-*.stats")
for filename in filenames:
lines = mfgraph.grep(filename, parameter);
line = lines[0]
numbers.append(float(string.split(line)[1]))
med = mfgraph.median(numbers)
assoc_data[config] = med;
mfgraph.normalize(assoc_data, base_config)
bars = []
stack_data = [benchmark]
for config in configs:
bars.append([config, assoc_data[config]])
stacks.append([benchmark] + bars)
print "done."
return [mfgraph.stacked_bar_graph(stacks,
title = "Scalability",
ylabel = "normalized runtime",
colors = ["0 0 1"],
patterns = ["solid"])]
def get_c2c(cpustat_file):
grep_lines = mfgraph.grep(cpustat_file, ".+total.+pic0=Instr_cnt,pic1=EC_snoop_cb,sys")
c2c = 0
for g_line in grep_lines:
line = string.split(g_line)
c2c += long(line[5])
return c2c
def gen_scale(benchmarks):
configs = ["1p", "2p", "4p", "8p", "16p"]
base_config = "1p"
parameter = "Ruby_cycles"
stacks = []
print "parsing..."
for benchmark in benchmarks:
assoc_data = {}
for config in configs:
sys.stderr.write(" %s %s\n" % (benchmark, config))
numbers = []
filenames = glob.glob(benchmark + "/*-" + config + "-*.stats")
for filename in filenames:
lines = mfgraph.grep(filename, parameter)
line = lines[0]
numbers.append(float(string.split(line)[1]))
med = mfgraph.median(numbers)
assoc_data[config] = med
mfgraph.normalize(assoc_data, base_config)
bars = []
stack_data = [benchmark]
for config in configs:
bars.append([config, assoc_data[config]])
stacks.append([benchmark] + bars)
print "done."
return [
mfgraph.stacked_bar_graph(
stacks, title="Scalability", ylabel="normalized runtime", colors=["0 0 1"], patterns=["solid"]
)
]
def get_cpi(output_file):
print output_file
grep_lines = mfgraph.grep(output_file, ".*Instruction per cycle.*")
print grep_lines
line = string.split(grep_lines[0])
ipc = float(line[4])
cpi = 1.0 / ipc
return cpi
def ecperf_get_transactions(directory):
filename = directory + "/Orders.summary"
if os.access(filename, os.F_OK):
grep_lines = mfgraph.grep(filename, "Total number of transactions");
line = string.split(grep_lines[0])
orders = int(line[5])
filename = directory + "/Mfg.summary"
if os.access(filename, os.F_OK):
grep_lines = mfgraph.grep(filename, "Total Number of WorkOrders Processed");
line = string.split(grep_lines[0])
work_ords = int(line[6])
transactions = work_ords + orders
print "transactions: %d" % transactions
return transactions
return 0
def get_cpi(output_file):
print output_file
grep_lines = mfgraph.grep(output_file, ".*Instruction per cycle.*")
print grep_lines
line = string.split(grep_lines[0])
ipc = float(line[4])
cpi = 1.0/ipc
return cpi
def touched_by():
stats = [
"touched_by_block_address:",
"touched_by_weighted_block_address:",
"touched_by_macroblock_address:",
"touched_by_weighted_macroblock_address:",
# "touched_by_pc_address:",
# "touched_by_weighted_pc_address:",
]
stats_names = {
"touched_by_block_address:" : "(a) Percent blocks touched by n processors",
"touched_by_weighted_block_address:": "(b) Percent of misses to blocks touched by n processors",
"touched_by_macroblock_address:": "(c) Percent of macroblocks touched by n processors",
"touched_by_weighted_macroblock_address:": "(d) Percent of misses to macroblocks touched by n processors",
}
jgraph_input = []
cols = 1
row_space = 2.9
col_space = 3
num = 0
for stat in stats:
bars = []
for benchmark in benchmarks:
print stat, benchmark
group = [benchmark_names[benchmark]]
filenames = glob.glob("%s/*trace-profiler*.stats" % benchmark)
for filename in filenames:
line = mfgraph.grep(filename, stat)[0]
line = string.replace(line, "]", "")
line = string.replace(line, "[", "")
data = string.split(line)[2:]
data = map(float, data)
normalize_list(data)
for index in range(len(data)):
if index+1 in [1, 4, 8, 12, 16]:
group.append(["%d" % (index+1), data[index]*100.0])
else:
group.append(["", data[index]*100.0])
bars.append(group)
jgraph_input.append(mfgraph.stacked_bar_graph(bars,
title = stats_names[stat],
xsize = 6.5,
ysize = 2,
xlabel = "",
ylabel = "",
stack_space = 3,
x_translate = (num % cols) * col_space,
y_translate = (num / cols) * -row_space,
))
num += 1
mfgraph.run_jgraph("\n".join(jgraph_input), "touched-by")
def gen_misses(benchmarks):
configs = [
"1p-MOSI_bcast_opt", "2p-MOSI_bcast_opt", "4p-MOSI_bcast_opt",
"8p-MOSI_bcast_opt", "16p-MOSI_bcast_opt"
]
parameters = [
"Request_type_IFETCH", "Request_type_LD", "Request_type_ST",
"Request_type_ATOMIC"
]
stacks = []
for benchmark in benchmarks:
bars = []
for config in configs:
print " %s %s" % (benchmark, config)
filenames = glob.glob(benchmark + "/*-" + config + "-*.stats")
for filename in filenames:
numbers = []
lines = mfgraph.grep(filename, "instruction_executed")
line = string.split(lines[0])
insn = long(line[1])
for parameter in parameters:
lines = mfgraph.grep(filename, parameter)
line = string.split(lines[0])
map(string.strip, line)
numbers.append(1000.0 * (float(line[1]) / insn))
numbers = mfgraph.stack_bars(numbers)
config_label = string.split(config, "-")[0]
bars.append([config_label] + numbers)
stacks.append([benchmark] + bars)
labels = []
for label in parameters:
labels.append(string.split(label, "_")[2])
return [
mfgraph.stacked_bar_graph(
stacks,
bar_segment_labels=labels,
title="Breakdown of misses",
ylabel="misses per thousand instructions",
patterns=["solid"],
)
]
def get_data(filename, str, index=1):
data = mfgraph.grep(filename, str)
if data:
line = data[0]
line = string.replace(line, "]", " ")
line = string.replace(line, "[", " ")
return float(string.split(line)[index])
else:
return None
def get_reasons_for_retire_stall(output_file):
grep_lines = mfgraph.grep(output_file, ".*Retire Updating.*")
line = string.split(grep_lines[0])
value_str = string.split(line[5], "%")
updating = float(value_str[0])
grep_lines = mfgraph.grep(output_file, ".*Retire Squash.*")
line = string.split(grep_lines[0])
value_str = string.split(line[5], "%")
squash = float(value_str[0])
grep_lines = mfgraph.grep(output_file, ".*Retire Limit.*")
line = string.split(grep_lines[0])
value_str = string.split(line[5], "%")
limit = float(value_str[0])
width = get_issue_width(output_file)
return [width, updating, squash, limit]
def ecperf_get_avg_mem_use_after(directory):
filename = directory + "/gc.summary"
grep_lines = mfgraph.grep(filename, ".+after.+")
if (grep_lines != []):
line = string.split(grep_lines[0])
mem_use = float(line[2]) / 1024
return mem_use
else:
return 0
def ecperf_get_throughput(directory):
filename = directory + "/ECperf.summary"
if os.access(filename, os.F_OK):
grep_lines = mfgraph.grep(filename, ".+Metric.+")
line = string.split(grep_lines[0])
throughput = float(line[3])
return throughput
else:
return 0
def ecperf_get_transactions(directory):
filename = directory + "/Orders.summary"
if os.access(filename, os.F_OK):
grep_lines = mfgraph.grep(filename, "Total number of transactions")
line = string.split(grep_lines[0])
orders = int(line[5])
filename = directory + "/Mfg.summary"
if os.access(filename, os.F_OK):
grep_lines = mfgraph.grep(filename,
"Total Number of WorkOrders Processed")
line = string.split(grep_lines[0])
work_ords = int(line[6])
transactions = work_ords + orders
print "transactions: %d" % transactions
return transactions
return 0
def ecperf_get_avg_mem_use_before(directory):
filename = directory + "/gc.summary"
grep_lines = mfgraph.grep(filename, ".+before.+");
if(grep_lines != []):
line = string.split(grep_lines[0])
mem_use = float(line[2])/1024
return mem_use
else:
return 0
def ecperf_get_num_procs(directory):
filename = directory + "/config.summary"
if os.access(filename, os.F_OK):
grep_lines = mfgraph.grep(filename, "Processors")
line = string.split(grep_lines[0])
procs = int(line[1])
return procs
else:
return 0
def ecperf_get_throughput(directory):
filename = directory + "/ECperf.summary"
if os.access(filename, os.F_OK):
grep_lines = mfgraph.grep(filename, ".+Metric.+")
line = string.split(grep_lines[0])
throughput = float(line[3])
return throughput
else:
return 0
def ecperf_get_stdystate(directory):
filename = directory + "/ECperf.summary"
if os.access(filename, os.F_OK):
grep_lines = mfgraph.grep(filename, "stdyState")
line = string.split(grep_lines[0])
stdy_state = int(line[4])
return stdy_state
return 0
def ecperf_get_num_procs(directory):
filename = directory + "/config.summary"
if os.access(filename, os.F_OK):
grep_lines = mfgraph.grep(filename, "Processors")
line = string.split(grep_lines[0])
procs = int(line[1])
return procs
else:
return 0
def ecperf_get_stdystate(directory):
filename = directory + "/ECperf.summary"
if os.access(filename, os.F_OK):
grep_lines = mfgraph.grep(filename, "stdyState")
line = string.split(grep_lines[0])
stdy_state = int(line[4])
return stdy_state
return 0
def get_reasons_for_retire_stall(output_file):
grep_lines = mfgraph.grep(output_file, ".*Retire Updating.*")
line = string.split(grep_lines[0])
value_str = string.split(line[5], "%")
updating = float(value_str[0])
grep_lines = mfgraph.grep(output_file, ".*Retire Squash.*")
line = string.split(grep_lines[0])
value_str = string.split(line[5], "%")
squash = float(value_str[0])
grep_lines = mfgraph.grep(output_file, ".*Retire Limit.*")
line = string.split(grep_lines[0])
value_str = string.split(line[5], "%")
limit = float(value_str[0])
width = get_issue_width(output_file)
return [width, updating, squash, limit]
def get_l2_miss_ratio(cpustat_file):
ratio = 0.0
grep_lines = mfgraph.grep(cpustat_file, ".+total.+pic0=EC_ref,pic1=EC_hit,sys")
for g_line in grep_lines:
line = string.split(g_line)
refs = float(line[3])
hits = float(line[4])
ratio = (refs - hits)/refs
#print "ratio: %f" % ratio
return ratio
def gen_cache_state(benchmarks):
configs = ["1p", "2p", "4p", "8p", "16p"]
parameters = ["GETS NP", "GETS I", "GETX NP", "GETX I", "GETX S", "GETX O"]
stacks = []
for benchmark in benchmarks:
bars = []
for config in configs:
filenames = glob.glob(benchmark + "/*-" + config + "-*.stats")
print " %s %s" % (benchmark, config), filenames
numbers = []
for parameter in parameters:
sum = 0
for filename in filenames:
# print benchmark, config, parameter, filename
lines = mfgraph.grep(filename, "instruction_executed")
line = string.split(lines[0])
insn = long(line[1])
lines = mfgraph.grep(filename, parameter)
for line in lines:
fields = string.split(line)
# print fields
sum += int(fields[3])
numbers.append(float(sum) / float(insn))
numbers = mfgraph.stack_bars(numbers)
bars.append([config] + numbers)
stacks.append([benchmark] + bars)
labels = []
for label in parameters:
labels.append(label)
return [
mfgraph.stacked_bar_graph(
stacks,
bar_segment_labels=labels,
title="Cache Misses by state",
ylabel="Number",
)
]
def get_l2_misses(cpustat_file):
l2_misses = 0
grep_lines = mfgraph.grep(cpustat_file, ".+total.+pic0=EC_ref,pic1=EC_hit,sys")
count = 0
for g_line in grep_lines:
line = string.split(g_line)
l2_misses += (long(line[3]) - long(line[4]))
count += 1
if count == 0:
return -1
else:
return l2_misses/count
def gen_cache_state(benchmarks):
configs = ["1p", "2p", "4p", "8p", "16p"]
parameters = ["GETS NP", "GETS I", "GETX NP", "GETX I", "GETX S", "GETX O"]
stacks = []
for benchmark in benchmarks:
bars = []
for config in configs:
filenames = glob.glob(benchmark + "/*-" + config + "-*.stats")
print " %s %s" % (benchmark, config), filenames
numbers = []
for parameter in parameters:
sum = 0
for filename in filenames:
# print benchmark, config, parameter, filename
lines = mfgraph.grep(filename, "instruction_executed");
line = string.split(lines[0])
insn = long(line[1])
lines = mfgraph.grep(filename, parameter);
for line in lines:
fields = string.split(line)
# print fields
sum += int(fields[3])
numbers.append(float(sum)/float(insn))
numbers = mfgraph.stack_bars(numbers)
bars.append([config] + numbers)
stacks.append([benchmark] + bars)
labels = []
for label in parameters:
labels.append(label)
return [mfgraph.stacked_bar_graph(stacks,
bar_segment_labels = labels,
title = "Cache Misses by state",
ylabel = "Number",
)]
def get_branch_prediction_rate_data(branch_type, predictor_type):
data = []
files = glob.glob("%s/*.opal" % results_dir)
for file in files:
grep_lines = mfgraph.grep(file, ".*Configuration File named.*")
if not re.match(".*branchpredictors.*", grep_lines[0]):
#print "Non-branch pred config: " + grep_lines[0]
continue
grep_lines = mfgraph.grep(file, "BRANCHPRED_TYPE.*")
if not re.match(".*" + predictor_type + ".*", grep_lines[0]):
#print "branch pred doesn't match (%s)" % grep_lines[0]
continue
pht_bits = int(extract_value(file, "BRANCHPRED_PHT_BITS", 2))
grep_lines = mfgraph.grep(file, ".*%s.*" % branch_type)
line = string.split(grep_lines[2]) # use total count
value_str = string.split(line[7], "%")
pct_right = float(value_str[0])
data.append([pht_bits, pct_right])
return data
def get_instructions(cpustat_file):
grep_lines = mfgraph.grep(cpustat_file, ".+total.+pic0=Cycle_cnt,pic1=Instr_cnt")
count = 0
instructions = 0
for g_line in grep_lines:
#print "line: " + g_line
line = string.split(g_line)
instructions += long(line[4])
count += 1
if count == 0:
return -1
else:
return instructions/count
def get_branch_prediction_rate_data(branch_type, predictor_type):
data = []
files = glob.glob("%s/*.opal" % results_dir)
for file in files:
grep_lines = mfgraph.grep(file, ".*Configuration File named.*")
if not re.match(".*branchpredictors.*", grep_lines[0]):
#print "Non-branch pred config: " + grep_lines[0]
continue
grep_lines = mfgraph.grep(file, "BRANCHPRED_TYPE.*")
if not re.match(".*" + predictor_type + ".*", grep_lines[0]):
#print "branch pred doesn't match (%s)" % grep_lines[0]
continue
pht_bits = int(extract_value(file, "BRANCHPRED_PHT_BITS", 2))
grep_lines = mfgraph.grep(file, ".*%s.*" % branch_type)
line = string.split(grep_lines[2]) # use total count
value_str = string.split(line[7], "%")
pct_right = float(value_str[0])
data.append([pht_bits, pct_right])
return data
def gen_misses(benchmarks):
configs = ["1p-MOSI_bcast_opt", "2p-MOSI_bcast_opt", "4p-MOSI_bcast_opt", "8p-MOSI_bcast_opt", "16p-MOSI_bcast_opt"]
parameters = ["Request_type_IFETCH", "Request_type_LD", "Request_type_ST", "Request_type_ATOMIC"]
stacks = []
for benchmark in benchmarks:
bars = []
for config in configs:
print " %s %s" % (benchmark, config)
filenames = glob.glob(benchmark + "/*-" + config + "-*.stats")
for filename in filenames:
numbers = []
lines = mfgraph.grep(filename, "instruction_executed");
line = string.split(lines[0])
insn = long(line[1])
for parameter in parameters:
lines = mfgraph.grep(filename, parameter);
line = string.split(lines[0])
map(string.strip, line)
numbers.append(1000.0*(float(line[1])/insn))
numbers = mfgraph.stack_bars(numbers)
config_label = string.split(config, "-")[0]
bars.append([config_label] + numbers)
stacks.append([benchmark] + bars)
labels = []
for label in parameters:
labels.append(string.split(label, "_")[2])
return [mfgraph.stacked_bar_graph(stacks,
bar_segment_labels = labels,
title = "Breakdown of misses",
ylabel = "misses per thousand instructions",
patterns = ["solid"],
)]
def gen_sharing(benchmarks, normalize=1):
configs = ["1p", "2p", "4p", "8p", "16p"]
stacks = []
for benchmark in benchmarks:
bars = []
for config in configs:
print " %s %s" % (benchmark, config)
filenames = glob.glob(benchmark + "/*-" + config + "-*.stats")
for filename in filenames:
lines = mfgraph.grep(filename, "instruction_executed")
line = string.split(lines[0])
insn = long(line[1])
numbers = get_sharing(filename)
if normalize:
sum = reduce(lambda x, y: x + y, numbers)
for index in range(len(numbers)):
numbers[index] = (numbers[index] / sum) * 100.0
else:
for index in range(len(numbers)):
numbers[index] = (numbers[index] /
float(insn)) * 1000.0
numbers = mfgraph.stack_bars(numbers)
bars.append([config] + numbers)
stacks.append([benchmark] + bars)
if normalize:
y_axis_label = "percent of misses"
else:
y_axis_label = "misses per thousand instructions",
return [
mfgraph.stacked_bar_graph(
stacks,
bar_segment_labels=labels,
title="Breakdown of misses",
ylabel=y_axis_label,
patterns=[
"stripe -45", "stripe -45", "stripe -45", "solid", "solid"
],
)
]
def gen_protocol(benchmarks):
# configs = ["8p-perfect", "8p-MOSI_bcast_opt", "8p-MOSI_GS"]
configs = ["8p-MOSI_bcast_opt", "8p-MOSI_GS"]
base_config = "8p-MOSI_bcast_opt"
parameter = "Ruby_cycles"
stacks = [];
print "parsing..."
for benchmark in benchmarks:
assoc_data = {};
for config in configs:
sys.stderr.write(" %s %s\n" % (benchmark, config))
numbers = []
filenames = glob.glob(benchmark + "/*-" + config + "-*.stats")
for filename in filenames:
lines = mfgraph.grep(filename, parameter);
line = lines[0]
numbers.append(float(string.split(line)[1]))
print numbers
med = mfgraph.median(numbers)
stddev = mfgraph.stddev(numbers)
min_val = min(numbers)
max_val = max(numbers)
assoc_data[config] = [med, min_val, max_val];
mfgraph.normalize(assoc_data, base_config)
bars = []
stack_data = [benchmark]
for config in configs:
bars.append([config, assoc_data[config]])
stacks.append([benchmark] + bars)
print "done."
print stacks
return [mfgraph.stacked_bar_graph(stacks,
title = "Snooping vs Directory",
ylabel = "normalized runtime",
colors = [".5 .5 1"],
patterns = ["solid"])]
def get_gc_time_per_xact_data(results_dir):
data = []
directories = glob.glob(results_dir + "/*")
for dir in directories:
procs = ecperf_get_num_procs(directory = dir)
filename = dir + "/gc.summary"
if os.access(filename, os.F_OK):
grep_lines = mfgraph.grep(filename, ".+time.+");
line = string.split(grep_lines[0])
gc_time = float(line[3])
transactions = ecperf_get_transactions(directory = dir)
if transactions == 0:
continue
tuple = [procs, (gc_time/transactions)]
data.append(tuple)
return data
def get_gc_time_per_xact_data(results_dir):
data = []
directories = glob.glob(results_dir + "/*")
for dir in directories:
procs = ecperf_get_num_procs(directory=dir)
filename = dir + "/gc.summary"
if os.access(filename, os.F_OK):
grep_lines = mfgraph.grep(filename, ".+time.+")
line = string.split(grep_lines[0])
gc_time = float(line[3])
transactions = ecperf_get_transactions(directory=dir)
if transactions == 0:
continue
tuple = [procs, (gc_time / transactions)]
data.append(tuple)
return data
def gen_data_size(benchmarks):
configs = ["1p-MOSI_bcast_opt", "2p-MOSI_bcast_opt", "4p-MOSI_bcast_opt", "8p-MOSI_bcast_opt", "16p-MOSI_bcast_opt"]
parameters = ["C GET_INSTR", "C GETS", "C GETX"]
stacks = []
for benchmark in benchmarks:
bars = []
for config in configs:
print " %s %s" % (benchmark, config)
filenames = glob.glob(benchmark + "/*-" + config + "-*.stats")
for filename in filenames:
numbers = []
for parameter in parameters:
lines = mfgraph.grep(filename, parameter);
line = string.split(lines[0])
map(string.strip, line)
num = string.split(line[2], "%")
num = (64L*(long(num[0])))/(1024.0*1024.0)
numbers.append(num)
numbers = mfgraph.stack_bars(numbers)
number = reduce(lambda x,y:x+y, numbers)
config_label = string.split(config, "-")[0]
bars.append([config_label] + [number])
stacks.append([benchmark] + bars)
# labels = []
# for label in parameters:
# labels.append(string.split(label)[1])
return [mfgraph.stacked_bar_graph(stacks,
# bar_segment_labels = labels,
title = "Memory touched",
ylabel = "Mbytes",
patterns = ["solid"],
xsize = 8.5,
)]
def get_time_breakdown_data(results_dir):
tuple_list = []
directories = glob.glob(results_dir + "/*")
for dir in directories:
#print "working in directory %s." % dir
procs = ecperf_get_num_procs(directory=dir)
filename = dir + "/appserver_mpstat.summary"
if os.access(filename, os.F_OK):
grep_lines = mfgraph.grep(filename, "AVG.+")
line = string.split(grep_lines[0])
if (len(line) > 3):
usr = float(line[12])
sys = float(line[13])
wt = float(line[14])
idl = float(line[15])
else:
usr = 0
sys = 0
wt = 0
idl = 0
gc_time = ecperf_get_gc_time(dir)
run_time = ecperf_get_stdystate(dir)
if run_time == 0:
continue
proc_ratio = float(procs - 1) / procs
gc_idle = proc_ratio * (gc_time / run_time) * 100
adj_idl = idl - gc_idle
if adj_idl < 0:
adj_idl = 0
gc_idle = idl
tuple = [procs, usr, sys, wt, adj_idl, gc_idle]
tuple_list.append(tuple)
return tuple_list
def get_time_breakdown_data(results_dir):
tuple_list = []
directories = glob.glob(results_dir + "/*")
for dir in directories:
#print "working in directory %s." % dir
procs = ecperf_get_num_procs(directory = dir)
filename = dir + "/appserver_mpstat.summary"
if os.access(filename, os.F_OK):
grep_lines = mfgraph.grep(filename, "AVG.+");
line = string.split(grep_lines[0])
if(len(line) > 3):
usr = float(line[12])
sys = float(line[13])
wt = float(line[14])
idl = float(line[15])
else:
usr = 0
sys = 0
wt = 0
idl = 0
gc_time = ecperf_get_gc_time(dir)
run_time = ecperf_get_stdystate(dir)
if run_time == 0:
continue
proc_ratio = float(procs - 1)/procs
gc_idle = proc_ratio * (gc_time/run_time) * 100
adj_idl = idl - gc_idle
if adj_idl < 0:
adj_idl = 0
gc_idle = idl
tuple = [procs, usr, sys, wt, adj_idl, gc_idle]
tuple_list.append(tuple)
return tuple_list
def gen_sharing(benchmarks, normalize = 1):
configs = ["1p", "2p", "4p", "8p", "16p"]
stacks = []
for benchmark in benchmarks:
bars = []
for config in configs:
print " %s %s" % (benchmark, config)
filenames = glob.glob(benchmark + "/*-" + config + "-*.stats")
for filename in filenames:
lines = mfgraph.grep(filename, "instruction_executed");
line = string.split(lines[0])
insn = long(line[1])
numbers = get_sharing(filename)
if normalize:
sum = reduce(lambda x,y: x+y, numbers)
for index in range(len(numbers)):
numbers[index] = (numbers[index] / sum) * 100.0
else:
for index in range(len(numbers)):
numbers[index] = (numbers[index] / float(insn)) * 1000.0
numbers = mfgraph.stack_bars(numbers)
bars.append([config] + numbers)
stacks.append([benchmark] + bars)
if normalize:
y_axis_label = "percent of misses"
else:
y_axis_label = "misses per thousand instructions",
return [mfgraph.stacked_bar_graph(stacks,
bar_segment_labels = labels,
title = "Breakdown of misses",
ylabel = y_axis_label,
patterns = ["stripe -45", "stripe -45", "stripe -45", "solid", "solid"],
)]
def touched_by():
stats = [
"touched_by_block_address:",
"touched_by_weighted_block_address:",
# "touched_by_macroblock_address:",
# "touched_by_weighted_macroblock_address:",
# "touched_by_supermacroblock_address:",
# "touched_by_weighted_supermacroblock_address:",
# "last_n_block_touched_by",
# "last_n_macroblock_touched_by",
]
yaxis_names = [
"Percent of all blocks",
"Percent of all misses",
"Percent of all macroblocks",
"Percent of all misses",
"Percent of all macroblocks",
"Percent of all misses",
# "Percent",
# "Percent",
]
stats_names = {
"touched_by_block_address:":
"(a) Percent of data blocks (64B) touched by n processors",
"touched_by_weighted_block_address:":
"(b) Percent of misses to data blocks (64B) touched by n processors",
# "touched_by_macroblock_address:": "(c) Percent of data macroblocks (1024B) touched by n processors",
# "touched_by_weighted_macroblock_address:": "(d) Percent of misses to data macroblocks (1024B) touched by n processors",
# "touched_by_supermacroblock_address:": "(e) Percent of 4kB macroblocks touched by n processors",
# "touched_by_weighted_supermacroblock_address:": "(f) Percent of misses to 4kB macroblocks touched by n processors",
# "last_n_block_touched_by" : "(e) Percent of misses touched by n processors in the last 64 misses to the block",
# "last_n_macroblock_touched_by" : "(f) Percent of misses touched by n processors in the last 64 misses to the macroblock",
}
jgraph_input = []
cols = 1
row_space = 2.2
col_space = 3
num = 0
for stat in stats:
bars = []
for benchmark in benchmarks:
print stat, benchmark
group = [benchmark_names[benchmark]]
filenames = glob.glob("*/%s-*gs320*.stats" % benchmark)
print benchmark, filenames
for filename in filenames:
line = mfgraph.grep(filename, stat)[0]
line = string.replace(line, "]", "")
line = string.replace(line, "[", "")
data = string.split(line)[2:]
# data = string.split(line)[14:]
data = map(float, data)
print data
# new_data = []
# sum = 0.0
# for item in data:
# new_data.append(item + sum)
# sum += item
# data = new_data
print data
# for index in range(len(data)):
# data[index] = data[index]/sum
print data
normalize_list(data)
for index in range(len(data)):
if index + 1 in [1, 4, 8, 12, 16]:
group.append(["%d" % (index + 1), data[index] * 100.0])
else:
group.append(["", data[index] * 100.0])
bars.append(group)
jgraph_input.append(
mfgraph.stacked_bar_graph(
bars,
title=stats_names[stat],
title_fontsize="12",
title_font="Times-Roman",
title_y=-25.0,
xsize=6.5,
ysize=1.5,
xlabel="",
ymax=100.01,
ylabel=yaxis_names[num],
colors=[".5 .5 .5"],
patterns=["solid"],
stack_name_location=12.0,
stack_space=3,
x_translate=(num % cols) * col_space,
y_translate=(num / cols) * -row_space,
))
num += 1
mfgraph.run_jgraph("\n".join(jgraph_input), "touched-by")
def maskpred_traces():
## read in all data
global data_map
data_map = {}
for benchmark in benchmarks:
print benchmark
data_map[benchmark] = {} # init map
# gs320 directory protocol
filenames = glob.glob("*/%s-*gs320*.stats" % benchmark)
for filename in filenames:
control_line = mfgraph.grep(filename, " switch_16_messages_out_Control")[0]
control_line = string.replace(control_line, "[", " ");
control_line = string.split(control_line)
data_line = mfgraph.grep(filename, " switch_16_messages_out_Data")[0]
data_line = string.replace(data_line, "[", " ");
data_line = string.split(data_line)
# to calculate the request bandwidth, we add up all the
# outgoing control messages and subtract the number of PutXs
# (which conveniently happens to be the number of virtual
# network zero data messages sent. We need to subtract out
# the number of PutXs, since the GS320 protocol sends a
# forwarded control message to 'ack' a PutX from a processor.
control_msgs = float(control_line[1]) - float(data_line[2])
sharing_misses = get_data(filename, "sharing_misses:")
total_misses = get_data(filename, "Total_misses:")
control_msgs_per_miss = control_msgs/total_misses
cycles = get_data(filename, "Ruby_cycles:")
if not data_map[benchmark].has_key("Directory"):
data_map[benchmark]["Directory"] = []
indirections = 100.0 * (sharing_misses/total_misses)
data_map[benchmark]["Directory"].append([control_msgs_per_miss, indirections, cycles])
# mask prediction data
filenames = glob.glob("*/%s-*mcast*.stats" % benchmark)
filenames.sort()
for filename in filenames:
predictor = string.split(filename, "-")[3]
# calculate indirections
data_lines = mfgraph.grep(filename, "multicast_retries:")
if not data_lines:
continue # missing data
lst = string.split(string.replace(data_lines[0], "]", ""))
total_misses = float(lst[6])
total_misses_alt = get_data(filename, "Total_misses:")
non_retry = float(lst[13])
retry = total_misses - non_retry
indirections = 100.0 * (retry/total_misses)
cycles = get_data(filename, "Ruby_cycles:")
# calculate bandwidth
data_lines = mfgraph.grep(filename, " switch_16_messages_out_Control:")
if not data_lines:
continue # missing data
lst = string.split(string.replace(data_lines[0], "]", ""))
control_msgs = float(lst[1])
control_msgs_per_miss = control_msgs/total_misses
print " ", predictor, "->", benchmark, control_msgs_per_miss, indirections, cycles
if not data_map[benchmark].has_key(predictor):
data_map[benchmark][predictor] = []
data_map[benchmark][predictor].append([control_msgs_per_miss, indirections, cycles])
## Make the graphs
all_data = []
all_parameters = []
for benchmark in benchmarks:
print benchmark
# collect data
data = []
# mask prediction data
for predictor in data_map[benchmark].keys():
include_list = []
include_list.append("^Directory")
include_list.append("^AlwaysBroadcast")
#include_list.append("^AlwaysUnicast")
# Graph1
# include_list.append("Counter:Implicit:5:DataBlock:0:0")
# include_list.append("Owner:Implicit:DataBlock:0:0")
# include_list.append("BroadcastCounter:Implicit:DataBlock:0:0")
# include_list.append("OwnerGroup:Implicit:DataBlock:0:0")
# include_list.append("OwnerBroadcast:Implicit:DataBlock:0:0")
# Graph2
# include_list.append("Counter:Implicit:5:.*:0:0")
# include_list.append("Owner:Implicit:.*:0:0")
# include_list.append("BroadcastCounter:Implicit:.*:0:0")
# include_list.append("OwnerGroup:Implicit:.*:0:0")
# include_list.append("OwnerBroadcast:Implicit:.*:0:0")
# Graph3
# include_list.append("Owner:Implicit:DataBlock:.*:0")
# include_list.append("Counter:Implicit:5:DataBlock:.*:0")
# include_list.append("OwnerGroup:Implicit:DataBlock:.*:0")
# include_list.append("OwnerGroupMod:Implicit:DataBlock:.*:0")
# include_list.append("OwnerBroadcast:Implicit:DataBlock:.*:0")
# include_list.append("BroadcastCounter:Implicit:DataBlock:.*:0")
# Graph4
# include_list.append("Owner:Implicit:DataBlock:4:.*")
# include_list.append("Counter:Implicit:5:DataBlock:4:.*")
# include_list.append("BroadcastCounter:Implicit:DataBlock:4:.*")
# include_list.append("OwnerGroup:Implicit:DataBlock:4:.*")
# include_list.append("OwnerGroupMod:Implicit:DataBlock:4:.*")
# include_list.append("OwnerBroadcast:Implicit:DataBlock:4:.*")
include_list.append("^StickySpatial:Both:1:DataBlock:0:.*")
# include_list.append("^OwnerGroup:.*:DataBlock:0:0")
# include_list.append("^Owner:Implicit:DataBlock:4:.*")
# include_list.append("^Counter:.*:5:DataBlock:0:0")
# include_list.append("^OwnerGroup:.*:DataBlock:0:0")
# include_list.append("^BroadcastCounter:Implicit:DataBlock:4:.*")
# include_list.append("^OwnerGroup:Implicit:DataBlock:.*:0")
# include_list.append("^OwnerGroupMod:Implicit:DataBlock:0:0")
# include_list.append("^OwnerBroadcast:Implicit:DataBlock:0:0")
# include_list.append("^OwnerBroadcastMod:Implicit:DataBlock:0:0")
# include_list.append("^BroadcastCounter:Implicit:1:DataBlock:4:.*")
# include_list.append("^OwnerGroup:Implicit:DataBlock:[06]:0")
# include_list.append("^BroadcastCounter:Implicit:1:DataBlock:0:0")
# include_list.append("^Counter:Implicit:1:DataBlock:[06]:0")
# include_list.append("^Pairwise:Implicit:DataBlock:4:.*")
# include_list.append("^StickySpatial:Implicit:2:DataBlock:0:0")
# include_list.append("^Counter:Implicit:1:DataBlock:.*:0")
# include_list.append("^BroadcastCounter:Implicit:1:DataBlock:.*:0")
# include_list.append("^Pairwise:Implicit:DataBlock:.*:0")
# include_list.append("^Counter:Implicit:1:PC:.*:0")
# include_list.append("^BroadcastCounter:Implicit:1:PC:.*:0")
# include_list.append("^Pairwise:Implicit:PC:.*:0")
# include_list.append("^StickySpatial:.*:0:DataBlock:8")
include = 0 # false
for pat in include_list:
if re.compile(pat).search(predictor):
include = 1 # true
if not include:
# print " ", predictor, "-> skipped"
continue
predictor_desc = predictor_name_transform(predictor)
(control_msgs_per_miss, indirections, cycles) = get_maskpred_data(benchmark, predictor)
(dir_control_msgs_per_miss, dir_indirections, dir_cycles) = get_maskpred_data(benchmark, "Directory")
# indirections = 100*indirections/dir_indirections
print " ", predictor, "->", benchmark, predictor_desc, control_msgs_per_miss, indirections
data.append([predictor_desc, [control_msgs_per_miss, indirections]])
# graph the data
all_data.append(data)
all_parameters.append({ "title" : benchmark_names[benchmark] })
# only display the legend on the last graph
all_parameters[-1]["legend"] = "on"
output = mfgraph.multi_graph(all_data,
all_parameters,
legend = "off",
xsize = 1.8,
ysize = 1.8,
xlabel = "control messages per miss",
ylabel = "indirections (percent of all misses)",
# linetype = ["dotted"] + (["none"] * 10),
linetype = ["none"] * 10,
colors = ["1 0 0", "0 0 1", "0 .5 0", "0 0 0", "1 0 1"],
fills = ["1 0 0", "0 0 1", "0 .5 0", "0 .5 1", "1 0 1"],
xmin = 0.0,
ymin = 0.0,
cols = 3,
# ymax = 100.0,
marktype = (["circle", "box", "diamond", "triangle"] * 10),
# marktype = ["none"] + (["circle", "box", "diamond", "triangle"] * 10),
title_fontsize = "12",
legend_hack = "yes",
)
mfgraph.run_jgraph(output, "traces")
def ecperf_get_gc_time(directory):
filename = directory + "/gc.summary"
grep_lines = mfgraph.grep(filename, ".+time.+")
line = string.split(grep_lines[0])
gc_time = float(line[3])
return gc_time
def ecperf_get_tx_rate(directory):
filename = directory + "/config.summary"
grep_lines = mfgraph.grep(filename, "Tx")
line = string.split(grep_lines[0])
rate = int(line[2])
return rate
def extract_value(filename, key, position):
grep_lines = mfgraph.grep(filename, ".*" + key + ".*")
line = string.split(grep_lines[0])
return line[position]
def maskpred_traces():
## read in all data
global data_map
data_map = {}
for benchmark in benchmarks:
print benchmark
data_map[benchmark] = {} # init map
# gs320 directory protocol
filenames = glob.glob("*/%s-*gs320*.stats" % benchmark)
for filename in filenames:
control_line = mfgraph.grep(filename,
" switch_16_messages_out_Control")[0]
control_line = string.replace(control_line, "[", " ")
control_line = string.split(control_line)
data_line = mfgraph.grep(filename,
" switch_16_messages_out_Data")[0]
data_line = string.replace(data_line, "[", " ")
data_line = string.split(data_line)
# to calculate the request bandwidth, we add up all the
# outgoing control messages and subtract the number of PutXs
# (which conveniently happens to be the number of virtual
# network zero data messages sent. We need to subtract out
# the number of PutXs, since the GS320 protocol sends a
# forwarded control message to 'ack' a PutX from a processor.
control_msgs = float(control_line[1]) - float(data_line[2])
sharing_misses = get_data(filename, "sharing_misses:")
total_misses = get_data(filename, "Total_misses:")
control_msgs_per_miss = control_msgs / total_misses
cycles = get_data(filename, "Ruby_cycles:")
if not data_map[benchmark].has_key("Directory"):
data_map[benchmark]["Directory"] = []
indirections = 100.0 * (sharing_misses / total_misses)
data_map[benchmark]["Directory"].append(
[control_msgs_per_miss, indirections, cycles])
# mask prediction data
filenames = glob.glob("*/%s-*mcast*.stats" % benchmark)
filenames.sort()
for filename in filenames:
predictor = string.split(filename, "-")[3]
# calculate indirections
data_lines = mfgraph.grep(filename, "multicast_retries:")
if not data_lines:
continue # missing data
lst = string.split(string.replace(data_lines[0], "]", ""))
total_misses = float(lst[6])
total_misses_alt = get_data(filename, "Total_misses:")
non_retry = float(lst[13])
retry = total_misses - non_retry
indirections = 100.0 * (retry / total_misses)
cycles = get_data(filename, "Ruby_cycles:")
# calculate bandwidth
data_lines = mfgraph.grep(filename,
" switch_16_messages_out_Control:")
if not data_lines:
continue # missing data
lst = string.split(string.replace(data_lines[0], "]", ""))
control_msgs = float(lst[1])
control_msgs_per_miss = control_msgs / total_misses
print " ", predictor, "->", benchmark, control_msgs_per_miss, indirections, cycles
if not data_map[benchmark].has_key(predictor):
data_map[benchmark][predictor] = []
data_map[benchmark][predictor].append(
[control_msgs_per_miss, indirections, cycles])
## Make the graphs
all_data = []
all_parameters = []
for benchmark in benchmarks:
print benchmark
# collect data
data = []
# mask prediction data
for predictor in data_map[benchmark].keys():
include_list = []
include_list.append("^Directory")
include_list.append("^AlwaysBroadcast")
#include_list.append("^AlwaysUnicast")
# Graph1
# include_list.append("Counter:Implicit:5:DataBlock:0:0")
# include_list.append("Owner:Implicit:DataBlock:0:0")
# include_list.append("BroadcastCounter:Implicit:DataBlock:0:0")
# include_list.append("OwnerGroup:Implicit:DataBlock:0:0")
# include_list.append("OwnerBroadcast:Implicit:DataBlock:0:0")
# Graph2
# include_list.append("Counter:Implicit:5:.*:0:0")
# include_list.append("Owner:Implicit:.*:0:0")
# include_list.append("BroadcastCounter:Implicit:.*:0:0")
# include_list.append("OwnerGroup:Implicit:.*:0:0")
# include_list.append("OwnerBroadcast:Implicit:.*:0:0")
# Graph3
# include_list.append("Owner:Implicit:DataBlock:.*:0")
# include_list.append("Counter:Implicit:5:DataBlock:.*:0")
# include_list.append("OwnerGroup:Implicit:DataBlock:.*:0")
# include_list.append("OwnerGroupMod:Implicit:DataBlock:.*:0")
# include_list.append("OwnerBroadcast:Implicit:DataBlock:.*:0")
# include_list.append("BroadcastCounter:Implicit:DataBlock:.*:0")
# Graph4
# include_list.append("Owner:Implicit:DataBlock:4:.*")
# include_list.append("Counter:Implicit:5:DataBlock:4:.*")
# include_list.append("BroadcastCounter:Implicit:DataBlock:4:.*")
# include_list.append("OwnerGroup:Implicit:DataBlock:4:.*")
# include_list.append("OwnerGroupMod:Implicit:DataBlock:4:.*")
# include_list.append("OwnerBroadcast:Implicit:DataBlock:4:.*")
include_list.append("^StickySpatial:Both:1:DataBlock:0:.*")
# include_list.append("^OwnerGroup:.*:DataBlock:0:0")
# include_list.append("^Owner:Implicit:DataBlock:4:.*")
# include_list.append("^Counter:.*:5:DataBlock:0:0")
# include_list.append("^OwnerGroup:.*:DataBlock:0:0")
# include_list.append("^BroadcastCounter:Implicit:DataBlock:4:.*")
# include_list.append("^OwnerGroup:Implicit:DataBlock:.*:0")
# include_list.append("^OwnerGroupMod:Implicit:DataBlock:0:0")
# include_list.append("^OwnerBroadcast:Implicit:DataBlock:0:0")
# include_list.append("^OwnerBroadcastMod:Implicit:DataBlock:0:0")
# include_list.append("^BroadcastCounter:Implicit:1:DataBlock:4:.*")
# include_list.append("^OwnerGroup:Implicit:DataBlock:[06]:0")
# include_list.append("^BroadcastCounter:Implicit:1:DataBlock:0:0")
# include_list.append("^Counter:Implicit:1:DataBlock:[06]:0")
# include_list.append("^Pairwise:Implicit:DataBlock:4:.*")
# include_list.append("^StickySpatial:Implicit:2:DataBlock:0:0")
# include_list.append("^Counter:Implicit:1:DataBlock:.*:0")
# include_list.append("^BroadcastCounter:Implicit:1:DataBlock:.*:0")
# include_list.append("^Pairwise:Implicit:DataBlock:.*:0")
# include_list.append("^Counter:Implicit:1:PC:.*:0")
# include_list.append("^BroadcastCounter:Implicit:1:PC:.*:0")
# include_list.append("^Pairwise:Implicit:PC:.*:0")
# include_list.append("^StickySpatial:.*:0:DataBlock:8")
include = 0 # false
for pat in include_list:
if re.compile(pat).search(predictor):
include = 1 # true
if not include:
# print " ", predictor, "-> skipped"
continue
predictor_desc = predictor_name_transform(predictor)
(control_msgs_per_miss, indirections,
cycles) = get_maskpred_data(benchmark, predictor)
(dir_control_msgs_per_miss, dir_indirections,
dir_cycles) = get_maskpred_data(benchmark, "Directory")
# indirections = 100*indirections/dir_indirections
print " ", predictor, "->", benchmark, predictor_desc, control_msgs_per_miss, indirections
data.append(
[predictor_desc, [control_msgs_per_miss, indirections]])
# graph the data
all_data.append(data)
all_parameters.append({"title": benchmark_names[benchmark]})
# only display the legend on the last graph
all_parameters[-1]["legend"] = "on"
output = mfgraph.multi_graph(
all_data,
all_parameters,
legend="off",
xsize=1.8,
ysize=1.8,
xlabel="control messages per miss",
ylabel="indirections (percent of all misses)",
# linetype = ["dotted"] + (["none"] * 10),
linetype=["none"] * 10,
colors=["1 0 0", "0 0 1", "0 .5 0", "0 0 0", "1 0 1"],
fills=["1 0 0", "0 0 1", "0 .5 0", "0 .5 1", "1 0 1"],
xmin=0.0,
ymin=0.0,
cols=3,
# ymax = 100.0,
marktype=(["circle", "box", "diamond", "triangle"] * 10),
# marktype = ["none"] + (["circle", "box", "diamond", "triangle"] * 10),
title_fontsize="12",
legend_hack="yes",
)
mfgraph.run_jgraph(output, "traces")
def touched_by():
stats = [
"touched_by_block_address:",
"touched_by_weighted_block_address:",
# "touched_by_macroblock_address:",
# "touched_by_weighted_macroblock_address:",
# "touched_by_supermacroblock_address:",
# "touched_by_weighted_supermacroblock_address:",
# "last_n_block_touched_by",
# "last_n_macroblock_touched_by",
]
yaxis_names = [
"Percent of all blocks",
"Percent of all misses",
"Percent of all macroblocks",
"Percent of all misses",
"Percent of all macroblocks",
"Percent of all misses",
# "Percent",
# "Percent",
]
stats_names = {
"touched_by_block_address:" : "(a) Percent of data blocks (64B) touched by n processors",
"touched_by_weighted_block_address:": "(b) Percent of misses to data blocks (64B) touched by n processors",
# "touched_by_macroblock_address:": "(c) Percent of data macroblocks (1024B) touched by n processors",
# "touched_by_weighted_macroblock_address:": "(d) Percent of misses to data macroblocks (1024B) touched by n processors",
# "touched_by_supermacroblock_address:": "(e) Percent of 4kB macroblocks touched by n processors",
# "touched_by_weighted_supermacroblock_address:": "(f) Percent of misses to 4kB macroblocks touched by n processors",
# "last_n_block_touched_by" : "(e) Percent of misses touched by n processors in the last 64 misses to the block",
# "last_n_macroblock_touched_by" : "(f) Percent of misses touched by n processors in the last 64 misses to the macroblock",
}
jgraph_input = []
cols = 1
row_space = 2.2
col_space = 3
num = 0
for stat in stats:
bars = []
for benchmark in benchmarks:
print stat, benchmark
group = [benchmark_names[benchmark]]
filenames = glob.glob("*/%s-*gs320*.stats" % benchmark)
print benchmark, filenames
for filename in filenames:
line = mfgraph.grep(filename, stat)[0]
line = string.replace(line, "]", "")
line = string.replace(line, "[", "")
data = string.split(line)[2:]
# data = string.split(line)[14:]
data = map(float, data)
print data
# new_data = []
# sum = 0.0
# for item in data:
# new_data.append(item + sum)
# sum += item
# data = new_data
print data
# for index in range(len(data)):
# data[index] = data[index]/sum
print data
normalize_list(data)
for index in range(len(data)):
if index+1 in [1, 4, 8, 12, 16]:
group.append(["%d" % (index+1), data[index]*100.0])
else:
group.append(["", data[index]*100.0])
bars.append(group)
jgraph_input.append(mfgraph.stacked_bar_graph(bars,
title = stats_names[stat],
title_fontsize = "12",
title_font = "Times-Roman",
title_y = -25.0,
xsize = 6.5,
ysize = 1.5,
xlabel = "",
ymax = 100.01,
ylabel = yaxis_names[num],
colors = [".5 .5 .5"],
patterns = ["solid"],
stack_name_location = 12.0,
stack_space = 3,
x_translate = (num % cols) * col_space,
y_translate = (num / cols) * -row_space,
))
num += 1
mfgraph.run_jgraph("\n".join(jgraph_input), "touched-by")
def get_histo(filename, str):
line = mfgraph.grep(filename, str)[0]
line = string.replace(line, "]", " ")
line = string.split(line)
return line[13:]
def ecperf_get_gc_time(directory):
filename = directory + "/gc.summary"
grep_lines = mfgraph.grep(filename, ".+time.+");
line = string.split(grep_lines[0])
gc_time = float(line[3])
return gc_time
def get_histo(filename, str):
line = mfgraph.grep(filename, str)[0]
line = string.replace(line, "]", " ")
line = string.split(line)
return line[13:]
def ecperf_get_tx_rate(directory):
filename = directory + "/config.summary"
grep_lines = mfgraph.grep(filename, "Tx")
line = string.split(grep_lines[0])
rate = int(line[2])
return rate
def get_data(filename, str):
data = mfgraph.grep(filename, str)
return float(string.split(data[0])[1])
def get_issue_width(output_file):
grep_lines = mfgraph.grep(output_file, ".*IWINDOW_WIN_SIZE.*")
line = string.split(grep_lines[0])
width = int(line[2])
return width
def touched_by():
stats = [
"touched_by_block_address:",
"touched_by_weighted_block_address:",
"touched_by_macroblock_address:",
"touched_by_weighted_macroblock_address:",
# "touched_by_pc_address:",
# "touched_by_weighted_pc_address:",
]
stats_names = {
"touched_by_block_address:":
"(a) Percent blocks touched by n processors",
"touched_by_weighted_block_address:":
"(b) Percent of misses to blocks touched by n processors",
"touched_by_macroblock_address:":
"(c) Percent of macroblocks touched by n processors",
"touched_by_weighted_macroblock_address:":
"(d) Percent of misses to macroblocks touched by n processors",
}
jgraph_input = []
cols = 1
row_space = 2.9
col_space = 3
num = 0
for stat in stats:
bars = []
for benchmark in benchmarks:
print stat, benchmark
group = [benchmark_names[benchmark]]
filenames = glob.glob("%s/*trace-profiler*.stats" % benchmark)
for filename in filenames:
line = mfgraph.grep(filename, stat)[0]
line = string.replace(line, "]", "")
line = string.replace(line, "[", "")
data = string.split(line)[2:]
data = map(float, data)
normalize_list(data)
for index in range(len(data)):
if index + 1 in [1, 4, 8, 12, 16]:
group.append(["%d" % (index + 1), data[index] * 100.0])
else:
group.append(["", data[index] * 100.0])
bars.append(group)
jgraph_input.append(
mfgraph.stacked_bar_graph(
bars,
title=stats_names[stat],
xsize=6.5,
ysize=2,
xlabel="",
ylabel="",
stack_space=3,
x_translate=(num % cols) * col_space,
y_translate=(num / cols) * -row_space,
))
num += 1
mfgraph.run_jgraph("\n".join(jgraph_input), "touched-by")
def get_data(filename, str):
data = mfgraph.grep(filename, str)
return float(string.split(data[0])[1])
