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 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 make_graph(graph):
name = graph[0]
print name
name = "graphs/" + name
benchmarks = graph[1]
data_points = graph[2:]
## calculate the line configurations
marktype_lst = []
mrotate_lst = []
color_lst = []
fill_lst = []
linetype_lst = []
for series in data_points:
## the lines connecting the points
marktype_lst.append("none")
mrotate_lst.append(0)
color_lst.append(grey)
fill_lst.append(grey)
linetype_lst.append("solid")
## the data points
for (predictor, mark) in series:
if predictor in ("None", "AlwaysBroadcast"):
marktype_lst.append("none")
mrotate_lst.append(0)
color_lst.append(black)
fill_lst.append(black)
linetype_lst.append("dotted")
(marktype, mrotate) = predictor_to_shape(predictor)
marktype_lst.append(marktype)
mrotate_lst.append(mrotate)
(color, fill) = mark
color_lst.append(color)
fill_lst.append(fill)
linetype_lst.append("none")
## Make the graphs
all_data = []
all_parameters = []
for benchmark in benchmarks:
print " ", benchmark
# collect data
data = []
for series in data_points:
segments = []
points = []
for data_point in series:
predictor = data_point[0]
predictor_desc = predictor_name_transform(predictor)
# get the data
#lst = (-1, -1, -1, -1)
pred_data = get_maskpred_data(benchmark, predictor)
dir_data = get_maskpred_data(benchmark, "None")
bcast_data = get_maskpred_data(benchmark, "AlwaysBroadcast")
if None in (pred_data, dir_data, bcast_data):
x_value = -1
y_value = -1
else:
(control_msgs_per_miss, indirections, cycles, total_bandwidth) = pred_data
(dir_control_msgs_per_miss, dir_indirections, dir_cycles, dir_total_bandwidth) = dir_data
(bcast_control_msgs_per_miss, bcast_indirections, bcast_cycles, bcast_total_bandwidth) = bcast_data
normalized_cycles = 100*cycles/dir_cycles # correct one
normalized_bandwidth = 100*total_bandwidth/bcast_total_bandwidth # correct one
if not runtime:
x_value = control_msgs_per_miss
y_value = indirections
else:
x_value = normalized_bandwidth
y_value = normalized_cycles
print " ", predictor, "->", benchmark, predictor_desc, x_value, y_value
if predictor == "None":
points.append(["", [x_value, y_value], [x_value, 0]])
if predictor == "AlwaysBroadcast":
points.append(["", [x_value, y_value], [0, y_value]])
points.append([predictor_desc, [x_value, y_value]])
segments.append([x_value, y_value])
data.append([""] + segments)
for line in points:
data.append(line)
# 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"
## if len(benchmarks) == 6:
## all_parameters[-1]["legend_x"] = -80
## all_parameters[-1]["legend_y"] = -70
## all_parameters[-1]["legend_default"] = "hjl vjt"
if not runtime:
xlabel = "request messages per miss"
ylabel = "indirections (percent of all misses)"
else:
xlabel = "normalized traffic per miss"
ylabel = "normalized runtime"
output = mfgraph.multi_graph(all_data,
all_parameters,
legend = "off",
xsize = 1.8,
ysize = 1.8,
xlabel = xlabel,
ylabel = ylabel,
linetype = linetype_lst,
colors = color_lst,
fills = fill_lst,
xmin = 0.0,
ymin = 0.0,
cols = 3,
#ymax = 100.0,
marktype = marktype_lst,
mrotate = mrotate_lst,
title_fontsize = "12",
legend_hack = "yes",
)
mfgraph.run_jgraph(output, name)