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.
77 lines
3.0 KiB
77 lines
3.0 KiB
import os
|
|
import csv
|
|
import numpy as np
|
|
import seaborn as sns
|
|
import matplotlib.pyplot as plt
|
|
|
|
output_path = "./plots"
|
|
prefetch_result = "./evaluation-results/qdp-xeonmax-prefetch-tca2-tcb1-tcj1-tmul8-wl4294967296-cs16777216.csv"
|
|
dram_result = "./evaluation-results/qdp-xeonmax-dram-tca2-tcb0-tcj1-tmul8-wl4294967296-cs2097152.csv"
|
|
|
|
tt_name = "rt-ns"
|
|
function_names = [ "scana-run", "scanb-run", "aggrj-run" ]
|
|
fn_nice = [ "Scan A, Filter", "Scan B, Prefetch", "Aggregate, Project + Sum" ]
|
|
|
|
def read_timings_from_csv(fname) -> tuple[list[float], list[str]]:
|
|
t = {}
|
|
total_time = 0
|
|
|
|
# Read data from CSV file
|
|
with open(fname, newline='') as csvfile:
|
|
reader = csv.DictReader(csvfile, delimiter=';')
|
|
for row in reader:
|
|
total_time += int(row[tt_name])
|
|
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 * 100 / total_time for key, value in t.items() if value != 0}
|
|
|
|
total = sum(list(t.values()))
|
|
if total < 100.0:
|
|
t["Waiting / Other"] = 100.0 - total
|
|
|
|
return list(t.values()), list(t.keys())
|
|
|
|
|
|
def get_data_prefetch_cache_access() -> tuple[list[float], list[str]]:
|
|
total = 0.3
|
|
data = [ 0.07, 0.19, 0.04 ]
|
|
data = [ x * 100 / total for x in data ]
|
|
keys = ["numa_alloc_onnode", "dml::make_mem_move_task", "dml::hardware_device::submit"]
|
|
|
|
return data,keys
|
|
|
|
def get_data_prefetch_total() -> tuple[list[float], list[str]]:
|
|
return read_timings_from_csv(prefetch_result)
|
|
|
|
def get_data_dram_total() -> tuple[list[float], list[str]]:
|
|
return read_timings_from_csv(dram_result)
|
|
|
|
# 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 main(data: tuple[list[float], list[str]], fname):
|
|
palette_color = sns.color_palette('mako')
|
|
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]:2.1f}%", xy=(x, y), xytext=(1.35*np.sign(x), 1.4*y), horizontalalignment=horizontalalignment, **kw)
|
|
|
|
fig.savefig(os.path.join(output_path, fname), bbox_inches='tight')
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main(get_data_prefetch_cache_access(), "plot-timing-prefetch-cacheaccess.pdf")
|
|
main(get_data_prefetch_total(), "plot-timing-prefetch-totalexec.pdf")
|
|
main(get_data_dram_total(), "plot-timing-dram-totalexec.pdf")
|