def generate_generic_cpustat_per_xact(jgraphs, results_dir, parameter, title, ylabel):
user_data = get_cpustat_per_xact_data(results_dir, "USER", parameter)
kernel_data = get_cpustat_per_xact_data(results_dir, "KERNEL", parameter)
combined_data = get_cpustat_per_xact_data(results_dir, "COMBINED", parameter)
lines = []
lines.append(mfgraph.merge_data(group_title = "User", data = user_data))
lines.append(mfgraph.merge_data(group_title = "Kernel", data = kernel_data))
lines.append(mfgraph.merge_data(group_title = "Combined", data = combined_data))
draw_std_line(jgraphs, title, ylabel, lines)
def generate_throughput(jgraphs, results_dir):
# ECperf Data
data = get_throughput_data(results_dir)
ecperf_data = mfgraph.merge_data(group_title = "ECperf", data = data)
# Graph ECperf Data
linear = get_linear(data=ecperf_data)
linear_data = mfgraph.merge_data(group_title = "Linear", data = linear)
lines = [ecperf_data, linear_data]
draw_std_line(jgraphs, "Throughput", "BBops/min", lines)
def generate_throughput_no_gc(jgraphs, results_dir):
ylabel = "Throughput"
# Gather ECperf Data
data = get_throughput_data(results_dir)
data_no_gc = get_throughput_no_gc_data(results_dir)
ecperf_data = mfgraph.merge_data(group_title = "ECperf", data = data)
ecperf_data_no_gc = mfgraph.merge_data(group_title = "ECperf no GC", data = data_no_gc)
# Graph ECperf Data
linear = get_linear(data=ecperf_data)
linear_data = mfgraph.merge_data(group_title = "Linear", data = linear)
lines = [linear_data, ecperf_data, ecperf_data_no_gc]
draw_std_line(jgraphs, "Effect of GC on Throughput", ylabel, lines)
def generate_gc_time_per_xact(jgraphs, results_dir):
ylabel = "GC Time Per Transaction"
# Gather ECperf data
data = get_gc_time_per_xact_data(results_dir)
ecperf_data = mfgraph.merge_data(group_title = "ECperf", data = data)
#draw the graph
lines = [ecperf_data]
draw_std_line(jgraphs, "GC Time/Tranaction", ylabel, lines)
def generate_indirect_branch_prediction_rate(jgraphs, results_dir):
gshare_data = mfgraph.merge_data(group_title="GSHARE",
data=get_branch_prediction_rate_data("INDIRE", "GSHARE"))
agree_data = mfgraph.merge_data(group_title="AGREE",
data=get_branch_prediction_rate_data("INDIRE", "AGREE"))
yags_data = mfgraph.merge_data(group_title="YAGS",
data=get_branch_prediction_rate_data("INDIRE", "YAGS"))
print gshare_data
lines = [gshare_data, agree_data, yags_data]
jgraphs.append(mfgraph.line_graph(lines,
title = "INDIRECT Branch Prediction Accuracy",
xlabel = "PHT Bits",
ylabel = "% Correct",
xsize = 6,
ysize = 4.5,
ymin = 80,
ymax = 100
))
def generate_tuning(jgraphs, results_dir):
ylabel = "Throughput in BBops/min"
data_map = get_tuning_data(results_dir)
lines = []
for key in data_map.keys():
label = "%d threads" % key
lines.append(mfgraph.merge_data(group_title = label, data = data_map[key]))
draw_std_line(jgraphs, "Tuning Data", ylabel, lines)
def generate_gc_time(jgraphs, results_dir):
xlabel = "Processors"
ylabel = "Time Spent in Garbage Collection (%)"
# Gather ECperf data
data = get_gc_time_data(results_dir)
ecperf_data = mfgraph.merge_data(group_title = "ECperf", data = data)
lines = [ecperf_data]
draw_std_line(jgraphs, "GC Time", "GC Time (s)", lines)
def generate_tuning(jgraphs, results_dir):
ylabel = "Throughput in BBops/min"
data_map = get_tuning_data(results_dir)
lines = []
for key in data_map.keys():
label = "%d threads" % key
lines.append(mfgraph.merge_data(group_title=label, data=data_map[key]))
draw_std_line(jgraphs, "Tuning Data", ylabel, lines)
def generate_gc_time(jgraphs, results_dir):
xlabel = "Processors"
ylabel = "Time Spent in Garbage Collection (%)"
# Gather ECperf data
data = get_gc_time_data(results_dir)
ecperf_data = mfgraph.merge_data(group_title="ECperf", data=data)
lines = [ecperf_data]
draw_std_line(jgraphs, "GC Time", "GC Time (s)", lines)
def generate_indirect_branch_prediction_rate(jgraphs, results_dir):
gshare_data = mfgraph.merge_data(group_title="GSHARE",
data=get_branch_prediction_rate_data(
"INDIRE", "GSHARE"))
agree_data = mfgraph.merge_data(group_title="AGREE",
data=get_branch_prediction_rate_data(
"INDIRE", "AGREE"))
yags_data = mfgraph.merge_data(group_title="YAGS",
data=get_branch_prediction_rate_data(
"INDIRE", "YAGS"))
print gshare_data
lines = [gshare_data, agree_data, yags_data]
jgraphs.append(
mfgraph.line_graph(lines,
title="INDIRECT Branch Prediction Accuracy",
xlabel="PHT Bits",
ylabel="% Correct",
xsize=6,
ysize=4.5,
ymin=80,
ymax=100))
