You can not select more than 25 topics
Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
142 lines
4.8 KiB
142 lines
4.8 KiB
import os
|
|
import csv
|
|
import numpy as np
|
|
import pandas as pd
|
|
import seaborn as sns
|
|
import matplotlib.pyplot as plt
|
|
|
|
output_path = "./plots"
|
|
hbm_result = "./evaluation-results/current/qdp-xeonmax-hbm-tca4-tcb0-tcj1-tmul32-wl4294967296-cs2097152.csv"
|
|
dram_result = "./evaluation-results/current/qdp-xeonmax-dram-tca2-tcb0-tcj1-tmul32-wl4294967296-cs2097152.csv"
|
|
prefetch_result = "./evaluation-results/current/qdp-xeonmax-prefetch-tca1-tcb1-tcj1-tmul32-wl4294967296-cs8388608.csv"
|
|
distprefetch_result = "./evaluation-results/current/qdp-xeonmax-distprefetch-tca1-tcb1-tcj1-tmul32-wl4294967296-cs8388608.csv"
|
|
|
|
tt_name = "rt-ns"
|
|
function_names = [ "scana-run", "scanb-run", "aggrj-run" ]
|
|
fn_nice = [ "Scan A", "Scan B", "Aggregate" ]
|
|
|
|
def read_timings_from_csv(fname) -> tuple[list[float], list[str]]:
|
|
t = {}
|
|
|
|
row_count = 0
|
|
|
|
with open(fname, newline='') as csvfile:
|
|
reader = csv.DictReader(csvfile, delimiter=';')
|
|
for row in reader:
|
|
row_count = row_count + 1
|
|
for i in range(len(function_names)):
|
|
t[fn_nice[i]] = t.get(fn_nice[i], 0) + int(row[function_names[i]])
|
|
|
|
t = {key: value / (1000 * 1000 * row_count) for key, value in t.items() if value != 0}
|
|
|
|
return list(t.values()), list(t.keys())
|
|
|
|
|
|
def read_total_time_from_csv(fname) -> float:
|
|
time = 0
|
|
row_count = 0
|
|
|
|
with open(fname, newline='') as csvfile:
|
|
reader = csv.DictReader(csvfile, delimiter=';')
|
|
for row in reader:
|
|
row_count = row_count + 1
|
|
time += int(row["rt-ns"])
|
|
|
|
return time / (1000 * 1000 * row_count)
|
|
|
|
|
|
def read_cache_hitrate_from_csv(fname) -> float:
|
|
hitrate = 0
|
|
row_count = 0
|
|
|
|
with open(fname, newline='') as csvfile:
|
|
reader = csv.DictReader(csvfile, delimiter=';')
|
|
for row in reader:
|
|
row_count = row_count + 1
|
|
hitrate += float(row["cache-hr"])
|
|
|
|
return (hitrate * 100) / row_count
|
|
|
|
|
|
def generate_speedup_table():
|
|
baseline = read_total_time_from_csv(dram_result)
|
|
columns = [ "Configuration", "Speedup", "Cache Hitrate" ]
|
|
|
|
names = [
|
|
"DDR-SDRAM (Baseline)",
|
|
"HBM (Upper Limit)",
|
|
"Prefetching",
|
|
"Prefetching, Distributed Columns"
|
|
]
|
|
|
|
rawtime = [
|
|
read_total_time_from_csv(dram_result),
|
|
read_total_time_from_csv(hbm_result),
|
|
read_total_time_from_csv(prefetch_result),
|
|
read_total_time_from_csv(distprefetch_result),
|
|
]
|
|
|
|
speedup = [
|
|
baseline / rawtime[0],
|
|
baseline / rawtime[1],
|
|
baseline / rawtime[2],
|
|
baseline / rawtime[3]
|
|
]
|
|
|
|
cachehr = [
|
|
0,
|
|
0,
|
|
read_cache_hitrate_from_csv(prefetch_result),
|
|
read_cache_hitrate_from_csv(distprefetch_result)
|
|
]
|
|
|
|
data = [
|
|
[ names[0], f"x{speedup[0]:1.2f}", r" \textemdash " ],
|
|
[ names[1], f"x{speedup[1]:1.2f}", r" \textemdash " ],
|
|
[ names[2], f"x{speedup[2]:1.2f}", f"{cachehr[2]:2.2f} \%" ],
|
|
[ names[3], f"x{speedup[3]:1.2f}", f"{cachehr[3]:2.2f} \%" ]
|
|
]
|
|
|
|
return pd.DataFrame(data, columns=columns)
|
|
|
|
|
|
def tex_table(df, fname):
|
|
with open(os.path.join(output_path, fname), "w") as of:
|
|
of.write(df.to_latex(index=False))
|
|
|
|
|
|
# loops over all possible configuration combinations and calls
|
|
# process_file_to_dataset for them in order to build a dataframe
|
|
# which is then displayed and saved
|
|
def donut_plot(data: tuple[list[float], list[str]], fname):
|
|
palette_color = sns.color_palette('mako_r')
|
|
fig, ax = plt.subplots(figsize=(6, 3), subplot_kw=dict(aspect="equal"))
|
|
|
|
wedges, texts = ax.pie(data[0], wedgeprops=dict(width=0.5), startangle=-40, colors=palette_color)
|
|
|
|
bbox_props = dict(boxstyle="square,pad=0.3", fc="w", ec="k", lw=0.72)
|
|
kw = dict(arrowprops=dict(arrowstyle="-"), bbox=bbox_props, zorder=0, va="center")
|
|
|
|
for i, p in enumerate(wedges):
|
|
ang = (p.theta2 - p.theta1)/2. + p.theta1
|
|
y = np.sin(np.deg2rad(ang))
|
|
x = np.cos(np.deg2rad(ang))
|
|
horizontalalignment = {-1: "right", 1: "left"}[int(np.sign(x))]
|
|
connectionstyle = f"angle,angleA=0,angleB={ang}"
|
|
kw["arrowprops"].update({"connectionstyle": connectionstyle})
|
|
ax.annotate(f"{data[1][i]} - {data[0][i]:2.2f} ms", xy=(x, y), xytext=(1.35*np.sign(x), 1.4*y), horizontalalignment=horizontalalignment, **kw)
|
|
|
|
plt.rcParams.update({'font.size': 18})
|
|
fig.savefig(os.path.join(output_path, fname), bbox_inches='tight')
|
|
|
|
|
|
def main():
|
|
donut_plot(read_timings_from_csv(prefetch_result), "plot-timing-prefetch.pdf")
|
|
donut_plot(read_timings_from_csv(distprefetch_result), "plot-timing-distprefetch.pdf")
|
|
donut_plot(read_timings_from_csv(dram_result), "plot-timing-dram.pdf")
|
|
donut_plot(read_timings_from_csv(hbm_result), "plot-timing-hbm.pdf")
|
|
tex_table(generate_speedup_table(), "table-qdpspeedup.tex")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|