[Quy Anh] «Elliot» Nguyen
477 lines
17 KiB
Python
477 lines
17 KiB
Python
from matplotlib import ticker
|
|
import pandas as pd
|
|
import numpy as np
|
|
import matplotlib.pyplot as plt
|
|
import matplotlib.patheffects as pe
|
|
#import seaborn as sns # tried it, the default look was worse
|
|
|
|
df = pd.read_csv("analysis/results.csv")
|
|
|
|
# peaks for the ryzen ai 7 350 (zen5) at 2GHz. 2 fma units and the datapath is 256 bit
|
|
# -> scalar 6, double 24, float 48 flops/cycle
|
|
SCALAR = 6
|
|
PEAK_D = 24
|
|
PEAK_F = 48
|
|
maroon = "#A71D5B"
|
|
|
|
|
|
def get(v, n=5000):
|
|
return df[(df.variant == v) & (df.n == n)]["flops_per_cycle"].values[0]
|
|
|
|
|
|
def getline(v):
|
|
s = df[df.variant == v].sort_values("n")
|
|
return s.n.values, s["flops_per_cycle"].values
|
|
|
|
|
|
def label_lines_above(ax,
|
|
entries,
|
|
fontsize=9,
|
|
rotate=False,
|
|
dy_frac=0.012,
|
|
halo=0):
|
|
"""Place each line's description on the line.
|
|
entries: (x, y, label, color) with two optional extra elements:
|
|
[4] xpos -> pin the label to that data-x (else labels are spread out)
|
|
[5] side -> "above" (default) or "below" to put the text under the line
|
|
Set halo>0 to draw a white outline behind the text. Call AFTER
|
|
set_xlim/set_ylim and tight_layout so any rotation angle is correct.
|
|
"""
|
|
logx = ax.get_xscale() == "log"
|
|
logy = ax.get_yscale() == "log"
|
|
fx = (lambda v: np.log10(v)) if logx else (lambda v: v)
|
|
fxi = (lambda v: 10**v) if logx else (lambda v: v)
|
|
fy = (lambda v: np.log10(v)) if logy else (lambda v: v)
|
|
fyi = (lambda v: 10**v) if logy else (lambda v: v)
|
|
|
|
ent = []
|
|
for e in entries:
|
|
x, y, lab, c = e[0], e[1], e[2], e[3]
|
|
if len(x) == 0:
|
|
continue
|
|
ent.append((np.asarray(x, float), np.asarray(y, float), lab, c,
|
|
e[4] if len(e) > 4 else None, e[5] if len(e) > 5 else "above"))
|
|
|
|
n = len(ent)
|
|
y0, y1 = ax.get_ylim()
|
|
dy = (fy(y1) - fy(y0)) * dy_frac
|
|
|
|
for i, (x, y, lab, c, xpos, side) in enumerate(ent):
|
|
xlo, xhi = fx(x.min()), fx(x.max())
|
|
if xpos is None: # spread across lines
|
|
frac = (i + 1) / (n + 1)
|
|
xt = fxi(xlo + frac * (xhi - xlo))
|
|
else:
|
|
xt = xpos
|
|
yt = fyi(np.interp(fx(xt), fx(x), fy(y))) # y on the line at xt
|
|
|
|
rot = 0
|
|
if rotate: # match slope in pixels
|
|
dxt = fxi(fx(xt) + 0.01 * (xhi - xlo))
|
|
ya = fyi(np.interp(fx(xt), fx(x), fy(y)))
|
|
yb = fyi(np.interp(fx(dxt), fx(x), fy(y)))
|
|
p1 = ax.transData.transform((xt, ya))
|
|
p2 = ax.transData.transform((dxt, yb))
|
|
rot = np.degrees(np.arctan2(p2[1] - p1[1], p2[0] - p1[0]))
|
|
|
|
below = side == "below"
|
|
ypos = fyi(fy(yt) - dy) if below else fyi(fy(yt) + dy)
|
|
va = "top" if below else "bottom"
|
|
txt = ax.text(xt,
|
|
ypos,
|
|
lab,
|
|
color=c,
|
|
fontsize=fontsize,
|
|
ha="center",
|
|
va=va,
|
|
fontweight="bold",
|
|
rotation=rot,
|
|
rotation_mode="anchor",
|
|
clip_on=False,
|
|
zorder=5)
|
|
if halo:
|
|
txt.set_path_effects([pe.withStroke(linewidth=halo, foreground="white")])
|
|
|
|
|
|
base = get("variant_baseline")
|
|
|
|
# plot 1: the two precision tracks (this is the main results plot)
|
|
# double tops out ~7, float goes to 24. also shows the avx2-float stuff already does most of the work
|
|
tracks = [
|
|
("variant_baseline", "Scalar Baseline (double)", "#7a7a7a", "-"),
|
|
("variant_AVX512_1", "AVX-512 (double)", "#4f86d6", "-"),
|
|
("variant_inlining_7_SoA_AVX2", "AVX-256 (double)", "#9bbbe0", "--"),
|
|
("variant_inlining_8_SoA_AVX512_polyexp_moreavx",
|
|
"AVX-512 + PolyExp (double)", "#2f5d9e", "-"),
|
|
("variant_float_inlining_SoA_AVX2_polyexp", "AVX-256 + PolyExp (float)",
|
|
"#e58aa6", "--"),
|
|
("variant_float_inlining_SoA_AVX2_polyexp_rsqrt_loopopt",
|
|
"AVX-256 + PolyExp + RSQRT (float)", "#d05a82", "--"),
|
|
("variant_float_inlining_SoA_AVX512_polyexp_moreavx_ilp_2_rsqrt_morersqrt",
|
|
"AVX-512 + PolyExp + RSQRT + ILP (float)", maroon, "-"),
|
|
]
|
|
|
|
tick_vals = np.array([50, 100, 200, 500, 1000, 2000, 5000, 10000])
|
|
target_positions = np.arange(len(tick_vals))
|
|
|
|
|
|
def map_x_to_linear(x_val):
|
|
return np.interp(x_val, tick_vals, target_positions)
|
|
|
|
|
|
fig, ax = plt.subplots(figsize=(9, 4.7))
|
|
ends = []
|
|
|
|
for v, lab, c, ls in tracks:
|
|
x_raw, y = getline(v)
|
|
x_mapped = map_x_to_linear(x_raw)
|
|
|
|
ax.plot(x_mapped,
|
|
y,
|
|
ls,
|
|
color=c,
|
|
marker="o",
|
|
ms=4,
|
|
lw=2.4 if c == maroon else 1.8)
|
|
|
|
ends.append((x_mapped, y, lab, c))
|
|
|
|
tick_vals = [50, 100, 200, 500, 1000, 2000, 5000, 10000]
|
|
target_positions = list(range(len(tick_vals)))
|
|
|
|
start_x_pos = 0
|
|
|
|
for yv, t in [(PEAK_D, f"Peak π par. w/ doubles ({PEAK_D} flops/cycle)"),
|
|
(PEAK_F, f"Peak π par. w/ floats ({PEAK_F} flops/cycle)")]:
|
|
ax.axhline(yv, ls=":", color="#999", lw=1.2)
|
|
ax.text(start_x_pos + 0.2,
|
|
yv * 1.03,
|
|
t,
|
|
fontsize=9,
|
|
color="#888",
|
|
fontweight="bold")
|
|
|
|
ax.set_yscale("log", base=2)
|
|
|
|
ax.set_xticks(target_positions)
|
|
ax.set_xticklabels(["50", "100", "200", "500", "1k", "2k", "5k", "10k"],
|
|
fontweight="bold",
|
|
fontsize=9)
|
|
|
|
ax.set_xlim(-0.5, len(tick_vals) - 0.5)
|
|
|
|
ax.yaxis.set_major_formatter(ticker.FuncFormatter(lambda x, _: str(int(x))))
|
|
for label in ax.get_yticklabels():
|
|
label.set_fontweight('bold')
|
|
label.set_fontsize(9)
|
|
ax.set_ylim(1, 64)
|
|
ax.set_xlabel("Number of Pedestrians", fontweight="bold", fontsize=9)
|
|
ax.grid(color="#e9e9e9")
|
|
ax.spines["top"].set_visible(False)
|
|
ax.spines["right"].set_visible(False)
|
|
plt.title(
|
|
'Performance [flops/cycle]\nCPU: AMD Ryzen AI 7 350 w/ Radeon 860M @ 2 GHz\nCompiler: g++ (Debian 15.2.0-17) 15.2.0\nFlags: -O3 -march=native -std=c++23 -Wall -Wextra -flto',
|
|
loc='left',
|
|
size=9,
|
|
fontweight="bold")
|
|
plt.tight_layout()
|
|
|
|
label_lines_above(ax, ends, fontsize=7.5, dy_frac=0.012)
|
|
|
|
plt.savefig("analysis/fig_tracks.svg", bbox_inches="tight")
|
|
|
|
# plot 2: all te milestones + the symmetry one that got slower
|
|
bars = [
|
|
("baseline", "variant_baseline"),
|
|
("AVX2 (double)", "variant_inlining_7_SoA_AVX2"),
|
|
("AVX-512 (double)", "variant_AVX512_1"),
|
|
("+ polyexp", "variant_inlining_8_SoA_AVX512_polyexp_moreavx"),
|
|
("+ float", "variant_float_inlining_SoA_AVX512_polyexp_moreavx"),
|
|
("+ rsqrt & ILP",
|
|
"variant_float_inlining_SoA_AVX512_polyexp_moreavx_ilp_2_rsqrt_morersqrt"),
|
|
("symmetry\n(half the pairs)", "variant_inlining_7_symmetry_2"),
|
|
]
|
|
names = [b[0] for b in bars]
|
|
spd = [get(b[1]) / base for b in bars]
|
|
cols = [
|
|
"#7a7a7a", "#3b6fb0", "#2f9e8f", "#e08a1e", "#c0436a", maroon, "#cf2b2b"
|
|
]
|
|
|
|
fig, ax = plt.subplots(figsize=(9, 4.7))
|
|
yp = np.arange(len(names))[::-1]
|
|
ax.barh(yp, spd, color=cols, height=0.66)
|
|
for yy, s in zip(yp, spd):
|
|
ax.text(s + 0.25,
|
|
yy,
|
|
str(round(s, 1)) + "x",
|
|
va="center",
|
|
fontsize=11,
|
|
fontweight="bold",
|
|
color="#cf2b2b" if s < 1 else "#222")
|
|
ax.axvline(1, ls="--", color="#888", lw=1)
|
|
ax.set_yticks(yp)
|
|
ax.set_yticklabels(names)
|
|
ax.set_xlim(0, 26)
|
|
ax.set_xlabel("speedup over baseline (n=5000)")
|
|
#ax.set_title("optimization milestones (and the one that backfired)", loc="left", fontsize=13)
|
|
ax.grid(axis="x", color="#eee")
|
|
ax.grid(axis="y", visible=False)
|
|
ax.spines["top"].set_visible(False)
|
|
ax.spines["right"].set_visible(False)
|
|
plt.tight_layout()
|
|
plt.savefig("analysis/fig_ladder.png", dpi=170, bbox_inches="tight")
|
|
|
|
|
|
# plot 3: roofline (n = 5000). same 7 variants as fig_tracks, same colors, inline labels.
|
|
# float variants move HALF the bytes -> 2x the operational intensity of the double ones
|
|
# (I_double ≈ 2.8k, I_float ≈ 5.6k), so each point uses its OWN I.
|
|
# memory roof from a measured single-thread read: sysbench 37618 MiB/s -> ~19.7 bytes/cycle @ 2 GHz.
|
|
def getI(v, n=5000):
|
|
return df[(df.variant == v) & (df.n == n)]["I"].values[0]
|
|
|
|
|
|
bw = ((37967.45 + 38536.77 + 38249.60) /
|
|
3) * 1024**2 / 2e9 # ≈ 20 bytes/cycle @ 2 GHz
|
|
|
|
# (variant, label, color, group) group "d" = double column, "f" = float column
|
|
roof = [
|
|
("variant_baseline", "Scalar Baseline (double)", "#7a7a7a", "d"),
|
|
("variant_inlining_7_SoA_AVX2", "AVX-256 (double)", "#9bbbe0", "d"),
|
|
("variant_AVX512_1", "AVX-512 (double) ", "#4f86d6", "d"),
|
|
("variant_inlining_8_SoA_AVX512_polyexp_moreavx",
|
|
"AVX-512 + PolyExp (double)", "#2f5d9e", "d"),
|
|
("variant_float_inlining_SoA_AVX2_polyexp", "AVX-256 + PolyExp (float)",
|
|
"#e58aa6", "f"),
|
|
("variant_float_inlining_SoA_AVX2_polyexp_rsqrt_loopopt",
|
|
"AVX-256 + PolyExp + RSQRT (float)", "#d05a82", "f"),
|
|
("variant_float_inlining_SoA_AVX512_polyexp_moreavx_ilp_2_rsqrt_morersqrt",
|
|
"AVX-512 + PolyExp + RSQRT + ILP (float)", maroon, "f"),
|
|
]
|
|
LABX = {"d": 2400, "f": 4900} # label right-edge sits just left of each column
|
|
|
|
fig, ax = plt.subplots(figsize=(9.5, 5.0))
|
|
xx = np.logspace(-1, 4.2, 200)
|
|
ax.plot(xx, np.minimum(bw * xx, PEAK_F), color="#bbbbbb", lw=1.4)
|
|
ax.text(0.165,
|
|
4,
|
|
"DRAM Read β seq. (≈ 20.0 bytes/cycle)",
|
|
fontsize=8,
|
|
color="#888",
|
|
fontweight="bold",
|
|
rotation=46,
|
|
ha="left")
|
|
|
|
for pk, t in [(SCALAR, f"Peak π seq. ({SCALAR} flops/cycle)"),
|
|
(PEAK_D, f"Peak π par. w/ doubles ({PEAK_D} flops/cycle)"),
|
|
(PEAK_F, f"Peak π par. w/ floats ({PEAK_F} flops/cycle)")]:
|
|
ax.axhline(pk, ls="--", color="#aaaaaa", lw=1.0)
|
|
ax.text(2.5, pk * 1.05, t, fontsize=8, color="#888", fontweight="bold")
|
|
|
|
for v, lab, c, grp in roof:
|
|
x, y = getI(v), get(v)
|
|
ax.plot(x, y, "o", ms=10, color=c, mec="white", mew=1.4, zorder=5)
|
|
t = ax.text(LABX[grp],
|
|
y,
|
|
lab,
|
|
color=c,
|
|
fontsize=8,
|
|
fontweight="bold",
|
|
ha="right",
|
|
va="center",
|
|
zorder=6)
|
|
t.set_path_effects([pe.withStroke(linewidth=2.5, foreground="white")])
|
|
|
|
ax.text(2812, 0.95, "I ≈ 2.8K", fontsize=8, color="#777", ha="center", va="top")
|
|
ax.text(5625, 10, "I ≈ 5.6K", fontsize=8, color="#777", ha="center", va="top")
|
|
|
|
ax.set_yscale("log", base=2)
|
|
ax.yaxis.set_major_formatter(ticker.FuncFormatter(lambda x, _: str(int(x))))
|
|
|
|
|
|
def fraction_formatter(x, pos):
|
|
if x == 0:
|
|
return '0'
|
|
log2_x = np.log2(x)
|
|
if abs(log2_x - round(log2_x)) < 1e-6:
|
|
exponent = int(round(log2_x))
|
|
if exponent < 0:
|
|
denominator = 2**(-exponent)
|
|
return f'1/{denominator}'
|
|
elif exponent == 0:
|
|
return '1'
|
|
else:
|
|
return str(int(x))
|
|
return f'{x:.2f}'
|
|
|
|
|
|
formatter = ticker.FuncFormatter(fraction_formatter)
|
|
|
|
plt.xscale('log', base=2)
|
|
plt.gca().xaxis.set_major_formatter(formatter)
|
|
|
|
ax.set_xlim(0.1, 16000)
|
|
ax.set_ylim(0.6, 70)
|
|
ax.set_xlabel("Operational Intensity [flops/byte]",
|
|
fontweight="bold",
|
|
fontsize=9)
|
|
# ax.set_ylabel("Performance [flops/cycle]", fontweight="bold", fontsize=9)
|
|
plt.title(
|
|
'Performance [flops/cycle]\nCPU: AMD Ryzen AI 7 350 w/ Radeon 860M @ 2 GHz\nCompiler: g++ (Debian 15.2.0-17) 15.2.0\nFlags: -O3 -march=native -std=c++23 -Wall -Wextra -flto',
|
|
loc='left',
|
|
size=9,
|
|
fontweight="bold")
|
|
ax.grid(color="#ededed")
|
|
ax.spines["top"].set_visible(False)
|
|
ax.spines["right"].set_visible(False)
|
|
plt.tight_layout()
|
|
plt.savefig("analysis/fig_roofline.svg", bbox_inches="tight")
|
|
|
|
# plot 4: exp vs the polynomial approx (just to show its basically exact)
|
|
x = np.linspace(-6, 0, 300)
|
|
ex = np.exp(x)
|
|
co = np.polyfit(x, ex, 5) # degree 5 same as in the kernel
|
|
ap = np.polyval(co, x)
|
|
|
|
fig, ax = plt.subplots(figsize=(4.6, 3.7))
|
|
ax.plot(x, ex, color="#222", lw=2.4, label="exp(x)")
|
|
ax.plot(x, ap, "--", color=maroon, lw=2, label="poly approx")
|
|
ax.set_xlabel("x")
|
|
ax.set_title("exp -> polynomial", loc="left", fontsize=12)
|
|
ax.grid(color="#ededed")
|
|
ax.spines["top"].set_visible(False)
|
|
ax.spines["right"].set_visible(False)
|
|
ax.legend(frameon=False, fontsize=10)
|
|
plt.tight_layout()
|
|
plt.savefig("analysis/fig_exp.png", dpi=170, bbox_inches="tight")
|
|
|
|
# plot 5: the milestone ladder as lines (this is plot0 + line1, line2, ... for the doc).
|
|
# one line per relevant optimization step across n, baseline at the bottom.
|
|
# unlike fig_tracks this includes the scalar steps so you can see where each jump comes from.
|
|
# tuple: (variant, label, color, linestyle, xpos|None, side) where side is
|
|
# "above" or "below" -> alternate the crowded float labels above/below their line.
|
|
milestones = [
|
|
("variant_baseline", "baseline", "#7a7a7a", "-", None, "above"),
|
|
#("variant_cutoff_callsite", "baseline + cutoff (best scalar)", "#9e8f2f", "-", None, "below"),
|
|
("variant_inlining_7_SoA_AVX2", "AVX2 (double)", "#3b6fb0", "--", None,
|
|
"above"),
|
|
("variant_inlining_7_SoA_AVX2_polyexp", "AVX2 + polyexp (double)", "#2f9e8f",
|
|
"--", None, "below"),
|
|
("variant_inlining_8_SoA_AVX512_polyexp_moreavx", "AVX-512 (double)",
|
|
"#2f5d9e", "-", None, "below"),
|
|
("variant_float_inlining_SoA_AVX2_polyexp", "float AVX2 + polyexp", "#e58aa6",
|
|
"--", 600, "above"),
|
|
("variant_float_inlining_SoA_AVX2_polyexp_rsqrt_loopopt",
|
|
"float AVX2 + rsqrt", "#e08a1e", "--", 2200, "below"),
|
|
("variant_float_inlining_SoA_AVX512_polyexp_moreavx", "float AVX-512",
|
|
"#c0436a", "-", 1500, "above"),
|
|
("variant_float_inlining_SoA_AVX512_polyexp_moreavx_ilp_2_rsqrt_morersqrt",
|
|
"float AVX-512 + rsqrt + ILP (best)", maroon, "-", 900, "above"),
|
|
]
|
|
|
|
fig, ax = plt.subplots(figsize=(9.5, 5.2))
|
|
ends = []
|
|
for v, lab, c, ls, xpos, side in milestones:
|
|
x, y = getline(v)
|
|
if len(x) == 0: # variant not in results.csv -> skip instead of crashing
|
|
print("skip (not in csv):", v)
|
|
continue
|
|
ax.plot(x, y, ls, color=c, marker="o", ms=4, lw=2.4 if c == maroon else 1.7)
|
|
ends.append((x, y, lab, c, xpos, side))
|
|
|
|
for yv, t in [(PEAK_D, "double peak (16)"), (PEAK_F, "float peak (32)")]:
|
|
ax.axhline(yv, ls=":", color="#999", lw=1.2)
|
|
ax.text(52, yv * 1.03, t, fontsize=9.5, color="#888")
|
|
|
|
ax.set_xscale("log")
|
|
ax.set_yscale("log")
|
|
ax.set_xticks([50, 100, 200, 500, 1000, 2000, 5000, 10000])
|
|
ax.set_xticklabels(["50", "100", "200", "500", "1k", "2k", "5k", "10k"])
|
|
ax.set_yticks([1, 2, 4, 8, 16, 32, 64])
|
|
ax.set_yticklabels(["1", "2", "4", "8", "16", "32", "64"])
|
|
ax.set_xlim(46, 13000)
|
|
ax.set_ylim(0.8, 40)
|
|
ax.set_xlabel("Number of Pedestrians")
|
|
ax.set_ylabel("Performance [flops/cycle]")
|
|
ax.grid(color="#e9e9e9")
|
|
ax.spines["top"].set_visible(False)
|
|
ax.spines["right"].set_visible(False)
|
|
plt.tight_layout()
|
|
label_lines_above(ax, ends, fontsize=6.5, dy_frac=0.02, halo=0)
|
|
plt.savefig("analysis/fig_milestones.png", dpi=170, bbox_inches="tight")
|
|
|
|
#plt.show()
|
|
|
|
# plot 6: wall-clock speedup over baseline (runtime, not performance).
|
|
# the fair view for optimizations that change the flop count -- cutoff skips ~40% of the
|
|
# inner loop, symmetry does half the pairs -- so flops/cycle misrepresents them. T_base /
|
|
# T_variant compares time-to-solve the same n regardless of how many flops each one does.
|
|
# curated to the milestones that carry the story + symmetry as the cautionary line.
|
|
basec = df[df.variant == "variant_baseline"].set_index("n")["cycles"].to_dict()
|
|
|
|
|
|
def speedline(v):
|
|
s = df[df.variant == v].sort_values("n") # pyright: ignore[reportCallIssue]
|
|
ns = [n for n in s.n.values if n in basec] # baseline has no n=10000 run
|
|
sp = [basec[n] / s[s.n == n]["cycles"].values[0] for n in ns]
|
|
return np.array(ns, float), np.array(sp, float)
|
|
|
|
|
|
# (variant, label, color, linestyle, xpos|None, side)
|
|
speed = [
|
|
("variant_cutoff_callsite", "Scalar Cutoff (double)", "#9e8f2f", "-", 260,
|
|
"above"),
|
|
("variant_inlining_7_symmetry_2", "Symmetry (double)", "#cf2b2b", "-", 320,
|
|
"below"),
|
|
("variant_inlining_7_SoA_AVX2", "AVX-256 (double)", "#9bbbe0", "--", 650,
|
|
"below"),
|
|
("variant_inlining_8_SoA_AVX512_polyexp_moreavx",
|
|
"AVX-512 + PolyExp (double)", "#2f5d9e", "-", 430, "above"),
|
|
("variant_float_inlining_SoA_AVX2_polyexp_rsqrt_loopopt",
|
|
"AVX-256 + PolyExp + RSQRT (float)", "#d05a82", "--", 950, "below"),
|
|
("variant_float_inlining_SoA_AVX512_polyexp_moreavx_ilp_2_rsqrt_morersqrt",
|
|
"AVX-512 + PolyExp + RSQRT + ILP (float)", maroon, "-", 680, "above"),
|
|
]
|
|
|
|
fig, ax = plt.subplots(figsize=(9, 4.7))
|
|
ends = []
|
|
for v, lab, c, ls, xpos, side in speed:
|
|
x, y = speedline(v)
|
|
if len(x) == 0:
|
|
print("skip (not in csv):", v)
|
|
continue
|
|
ax.plot(x, y, ls, color=c, marker="o", ms=4, lw=2.4 if c == maroon else 1.8)
|
|
ends.append((x, y, lab, c, xpos, side))
|
|
ax.axhline(1, ls=":", color="#999", lw=1.3)
|
|
ax.text(52, 1.03, "baseline (1x)", fontsize=9, color="#888")
|
|
ax.set_xscale("log")
|
|
ax.set_yscale("log")
|
|
|
|
xlim_log = np.log10(ax.get_xlim())
|
|
num_ticks = 7
|
|
linear_positions = np.linspace(xlim_log[0], xlim_log[1], num_ticks)
|
|
tick_positions = 10**linear_positions
|
|
ax.set_xticks(tick_positions)
|
|
ax.set_xticklabels(["50", "100", "200", "500", "1k", "2k", "5k"])
|
|
|
|
ax.set_yticks([1, 2, 5, 10, 20])
|
|
ax.set_yticklabels(["1x", "2x", "5x", "10x", "20x"])
|
|
|
|
ax.tick_params(axis='both', which='minor', length=0)
|
|
ax.tick_params(axis='both', which='major', length=4)
|
|
|
|
ax.set_xlim(46, 7000)
|
|
ax.set_ylim(0.8, 28)
|
|
ax.set_xlabel("Number of Pedestrians", fontweight="bold", fontsize=9)
|
|
# ax.set_ylabel("Speedup", fontweight="bold", fontsize=9)
|
|
plt.title(
|
|
'Runtime Speedup\nCPU: AMD Ryzen AI 7 350 w/ Radeon 860M @ 2 GHz\nCompiler: g++ (Debian 15.2.0-17) 15.2.0\nFlags: -O3 -march=native -std=c++23 -Wall -Wextra -flto',
|
|
loc='left',
|
|
size=9,
|
|
fontweight="bold")
|
|
ax.grid(color="#e9e9e9")
|
|
ax.spines["top"].set_visible(False)
|
|
ax.spines["right"].set_visible(False)
|
|
plt.tight_layout()
|
|
label_lines_above(ax, ends, fontsize=7.5, dy_frac=0.02, halo=2)
|
|
plt.savefig("analysis/fig_speedup.svg", bbox_inches="tight")
|