Merge branch 'elliot/plot-update' into 'main'
Plot Update See merge request course-asl-fastcode26/team70!54
This commit is contained in:
Linus Baumberger
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+357
-182
@@ -1,3 +1,4 @@
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from matplotlib import ticker
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import pandas as pd
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import numpy as np
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import matplotlib.pyplot as plt
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@@ -7,140 +8,216 @@ import matplotlib.patheffects as pe
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df = pd.read_csv("analysis/results.csv")
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# peaks for the ryzen ai 7 350 (zen5) at 2GHz. 2 fma units and the datapath is 256 bit
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# -> scalar 4, double 16, float 32 flops/cycle
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SCALAR = 4
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PEAK_D = 16
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PEAK_F = 32
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# -> scalar 6, double 24, float 48 flops/cycle
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SCALAR = 6
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PEAK_D = 24
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PEAK_F = 48
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maroon = "#A71D5B"
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def get(v, n=5000):
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return df[(df.variant==v) & (df.n==n)]["flops_per_cycle"].values[0]
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return df[(df.variant == v) & (df.n == n)]["flops_per_cycle"].values[0]
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def getline(v):
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s = df[df.variant == v].sort_values("n")
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return s.n.values, s["flops_per_cycle"].values
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s = df[df.variant == v].sort_values("n")
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return s.n.values, s["flops_per_cycle"].values
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def label_lines_above(ax, entries, fontsize=9, rotate=False, dy_frac=0.012, halo=0):
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"""Place each line's description on the line.
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def label_lines_above(ax,
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entries,
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fontsize=9,
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rotate=False,
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dy_frac=0.012,
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halo=0):
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"""Place each line's description on the line.
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entries: (x, y, label, color) with two optional extra elements:
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[4] xpos -> pin the label to that data-x (else labels are spread out)
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[5] side -> "above" (default) or "below" to put the text under the line
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Set halo>0 to draw a white outline behind the text. Call AFTER
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set_xlim/set_ylim and tight_layout so any rotation angle is correct.
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"""
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logx = ax.get_xscale() == "log"
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logy = ax.get_yscale() == "log"
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fx = (lambda v: np.log10(v)) if logx else (lambda v: v)
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fxi = (lambda v: 10**v) if logx else (lambda v: v)
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fy = (lambda v: np.log10(v)) if logy else (lambda v: v)
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fyi = (lambda v: 10**v) if logy else (lambda v: v)
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"""
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logx = ax.get_xscale() == "log"
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logy = ax.get_yscale() == "log"
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fx = (lambda v: np.log10(v)) if logx else (lambda v: v)
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fxi = (lambda v: 10**v) if logx else (lambda v: v)
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fy = (lambda v: np.log10(v)) if logy else (lambda v: v)
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fyi = (lambda v: 10**v) if logy else (lambda v: v)
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ent = []
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for e in entries:
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x, y, lab, c = e[0], e[1], e[2], e[3]
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if len(x) == 0:
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continue
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ent.append((np.asarray(x, float), np.asarray(y, float), lab, c,
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e[4] if len(e) > 4 else None,
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e[5] if len(e) > 5 else "above"))
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ent = []
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for e in entries:
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x, y, lab, c = e[0], e[1], e[2], e[3]
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if len(x) == 0:
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continue
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ent.append((np.asarray(x, float), np.asarray(y, float), lab, c,
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e[4] if len(e) > 4 else None, e[5] if len(e) > 5 else "above"))
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n = len(ent)
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y0, y1 = ax.get_ylim()
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dy = (fy(y1) - fy(y0)) * dy_frac
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n = len(ent)
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y0, y1 = ax.get_ylim()
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dy = (fy(y1) - fy(y0)) * dy_frac
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for i, (x, y, lab, c, xpos, side) in enumerate(ent):
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xlo, xhi = fx(x.min()), fx(x.max())
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if xpos is None: # spread across lines
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frac = (i + 1) / (n + 1)
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xt = fxi(xlo + frac * (xhi - xlo))
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else:
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xt = xpos
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yt = fyi(np.interp(fx(xt), fx(x), fy(y))) # y on the line at xt
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for i, (x, y, lab, c, xpos, side) in enumerate(ent):
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xlo, xhi = fx(x.min()), fx(x.max())
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if xpos is None: # spread across lines
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frac = (i + 1) / (n + 1)
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xt = fxi(xlo + frac * (xhi - xlo))
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else:
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xt = xpos
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yt = fyi(np.interp(fx(xt), fx(x), fy(y))) # y on the line at xt
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rot = 0
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if rotate: # match slope in pixels
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dxt = fxi(fx(xt) + 0.01 * (xhi - xlo))
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ya = fyi(np.interp(fx(xt), fx(x), fy(y)))
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yb = fyi(np.interp(fx(dxt), fx(x), fy(y)))
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p1 = ax.transData.transform((xt, ya))
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p2 = ax.transData.transform((dxt, yb))
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rot = np.degrees(np.arctan2(p2[1] - p1[1], p2[0] - p1[0]))
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rot = 0
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if rotate: # match slope in pixels
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dxt = fxi(fx(xt) + 0.01 * (xhi - xlo))
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ya = fyi(np.interp(fx(xt), fx(x), fy(y)))
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yb = fyi(np.interp(fx(dxt), fx(x), fy(y)))
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p1 = ax.transData.transform((xt, ya))
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p2 = ax.transData.transform((dxt, yb))
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rot = np.degrees(np.arctan2(p2[1] - p1[1], p2[0] - p1[0]))
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below = side == "below"
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ypos = fyi(fy(yt) - dy) if below else fyi(fy(yt) + dy)
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va = "top" if below else "bottom"
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txt = ax.text(xt,
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ypos,
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lab,
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color=c,
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fontsize=fontsize,
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ha="center",
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va=va,
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fontweight="bold",
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rotation=rot,
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rotation_mode="anchor",
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clip_on=False,
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zorder=5)
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if halo:
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txt.set_path_effects([pe.withStroke(linewidth=halo, foreground="white")])
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below = side == "below"
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ypos = fyi(fy(yt) - dy) if below else fyi(fy(yt) + dy)
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va = "top" if below else "bottom"
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txt = ax.text(xt, ypos, lab, color=c, fontsize=fontsize,
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ha="center", va=va, fontweight="bold",
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rotation=rot, rotation_mode="anchor", clip_on=False, zorder=5)
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if halo:
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txt.set_path_effects([pe.withStroke(linewidth=halo, foreground="white")])
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base = get("variant_baseline")
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# plot 1: the two precision tracks (this is the main results plot)
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# double tops out ~7, float goes to 24. also shows the avx2-float stuff already does most of the work
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tracks = [
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("variant_baseline", "baseline (scalar)", "#7a7a7a", "-"),
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("variant_AVX512_1", "double, AVX-512 ", "#4f86d6", "-"),
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("variant_inlining_7_SoA_AVX2", "double, AVX-256", "#9bbbe0", "--"),
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("variant_inlining_8_SoA_AVX512_polyexp_moreavx", "double, AVX-512 + polyexp", "#2f5d9e", "-"),
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("variant_float_inlining_SoA_AVX2_polyexp", "float, AVX-256 + polyexp", "#e58aa6", "--"),
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("variant_float_inlining_SoA_AVX2_polyexp_rsqrt_loopopt", "float, AVX-256 + polyexp + rsqrt", "#d05a82", "--"),
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("variant_float_inlining_SoA_AVX512_polyexp_moreavx_ilp_2_rsqrt_morersqrt", "float, AVX-512 + rsqrt + ILP (best)", maroon, "-"),
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("variant_baseline", "Scalar Baseline (double)", "#7a7a7a", "-"),
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("variant_AVX512_1", "AVX-512 (double)", "#4f86d6", "-"),
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("variant_inlining_7_SoA_AVX2", "AVX-256 (double)", "#9bbbe0", "--"),
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("variant_inlining_8_SoA_AVX512_polyexp_moreavx",
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"AVX-512 + PolyExp (double)", "#2f5d9e", "-"),
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("variant_float_inlining_SoA_AVX2_polyexp", "AVX-256 + PolyExp (float)",
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"#e58aa6", "--"),
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("variant_float_inlining_SoA_AVX2_polyexp_rsqrt_loopopt",
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"AVX-256 + PolyExp + RSQRT (float)", "#d05a82", "--"),
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("variant_float_inlining_SoA_AVX512_polyexp_moreavx_ilp_2_rsqrt_morersqrt",
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"AVX-512 + PolyExp + RSQRT + ILP (float)", maroon, "-"),
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]
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fig, ax = plt.subplots(figsize=(9,4.7))
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tick_vals = np.array([50, 100, 200, 500, 1000, 2000, 5000, 10000])
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target_positions = np.arange(len(tick_vals))
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def map_x_to_linear(x_val):
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return np.interp(x_val, tick_vals, target_positions)
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fig, ax = plt.subplots(figsize=(9, 4.7))
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ends = []
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for v,lab,c,ls in tracks:
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x,y = getline(v)
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ax.plot(x, y, ls, color=c, marker="o", ms=4, lw= 2.4 if c==maroon else 1.8)
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ends.append((x, y, lab, c))
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for yv,t in [(PEAK_D,"double peak (16)"), (PEAK_F,"float peak (32)")]:
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ax.axhline(yv, ls=":", color="#999", lw=1.2)
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ax.text(52, yv*1.03, t, fontsize=9.5, color="#888")
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for v, lab, c, ls in tracks:
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x_raw, y = getline(v)
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x_mapped = map_x_to_linear(x_raw)
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ax.set_xscale("log"); ax.set_yscale("log")
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ax.set_xticks([50,100,200,500,1000,2000,5000,10000])
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ax.set_xticklabels(["50","100","200","500","1k","2k","5k","10k"])
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ax.set_yticks([1,2,4,8,16,32]); ax.set_yticklabels(["1","2","4","8","16","32"])
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ax.set_xlim(46,13000); ax.set_ylim(1,40)
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ax.set_xlabel("number of pedestrians n")
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ax.set_ylabel("performance [flops/cycle]")
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ax.plot(x_mapped,
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y,
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ls,
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color=c,
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marker="o",
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ms=4,
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lw=2.4 if c == maroon else 1.8)
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ends.append((x_mapped, y, lab, c))
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tick_vals = [50, 100, 200, 500, 1000, 2000, 5000, 10000]
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target_positions = list(range(len(tick_vals)))
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start_x_pos = 0
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for yv, t in [(PEAK_D, f"Peak π par. w/ doubles ({PEAK_D} flops/cycle)"),
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(PEAK_F, f"Peak π par. w/ floats ({PEAK_F} flops/cycle)")]:
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ax.axhline(yv, ls=":", color="#999", lw=1.2)
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ax.text(start_x_pos + 0.2,
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yv * 1.03,
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t,
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fontsize=9,
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color="#888",
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fontweight="bold")
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ax.set_yscale("log", base=2)
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ax.set_xticks(target_positions)
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ax.set_xticklabels(["50", "100", "200", "500", "1k", "2k", "5k", "10k"],
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fontweight="bold",
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fontsize=9)
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ax.set_xlim(-0.5, len(tick_vals) - 0.5)
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ax.yaxis.set_major_formatter(ticker.FuncFormatter(lambda x, _: str(int(x))))
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for label in ax.get_yticklabels():
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label.set_fontweight('bold')
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label.set_fontsize(9)
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ax.set_ylim(1, 64)
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ax.set_xlabel("Number of Pedestrians", fontweight="bold", fontsize=9)
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ax.grid(color="#e9e9e9")
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ax.spines["top"].set_visible(False); ax.spines["right"].set_visible(False)
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ax.spines["top"].set_visible(False)
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ax.spines["right"].set_visible(False)
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plt.title(
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'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',
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loc='left',
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size=9,
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fontweight="bold")
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plt.tight_layout()
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label_lines_above(ax, ends, fontsize=7.5, dy_frac=0.012)
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plt.savefig("analysis/fig_tracks.png", dpi=170, bbox_inches="tight")
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plt.savefig("analysis/fig_tracks.svg", bbox_inches="tight")
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# plot 2: all te milestones + the symmetry one that got slower
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# plot 2: all te milestones + the symmetry one that got slower
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bars = [
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("baseline", "variant_baseline"),
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("AVX2 (double)", "variant_inlining_7_SoA_AVX2"),
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("AVX-512 (double)", "variant_AVX512_1"),
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("+ polyexp", "variant_inlining_8_SoA_AVX512_polyexp_moreavx"),
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("+ float", "variant_float_inlining_SoA_AVX512_polyexp_moreavx"),
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("+ rsqrt & ILP", "variant_float_inlining_SoA_AVX512_polyexp_moreavx_ilp_2_rsqrt_morersqrt"),
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("symmetry\n(half the pairs)", "variant_inlining_7_symmetry_2"),
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("baseline", "variant_baseline"),
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("AVX2 (double)", "variant_inlining_7_SoA_AVX2"),
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("AVX-512 (double)", "variant_AVX512_1"),
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("+ polyexp", "variant_inlining_8_SoA_AVX512_polyexp_moreavx"),
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("+ float", "variant_float_inlining_SoA_AVX512_polyexp_moreavx"),
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("+ rsqrt & ILP",
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"variant_float_inlining_SoA_AVX512_polyexp_moreavx_ilp_2_rsqrt_morersqrt"),
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("symmetry\n(half the pairs)", "variant_inlining_7_symmetry_2"),
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]
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names = [b[0] for b in bars]
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spd = [get(b[1])/base for b in bars]
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cols = ["#7a7a7a","#3b6fb0","#2f9e8f","#e08a1e","#c0436a", maroon, "#cf2b2b"]
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spd = [get(b[1]) / base for b in bars]
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cols = [
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"#7a7a7a", "#3b6fb0", "#2f9e8f", "#e08a1e", "#c0436a", maroon, "#cf2b2b"
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]
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fig,ax = plt.subplots(figsize=(9,4.7))
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fig, ax = plt.subplots(figsize=(9, 4.7))
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yp = np.arange(len(names))[::-1]
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ax.barh(yp, spd, color=cols, height=0.66)
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for yy,s in zip(yp,spd):
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ax.text(s+0.25, yy, str(round(s,1))+"x", va="center", fontsize=11, fontweight="bold",
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color = "#cf2b2b" if s<1 else "#222")
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for yy, s in zip(yp, spd):
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ax.text(s + 0.25,
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yy,
|
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str(round(s, 1)) + "x",
|
||||
va="center",
|
||||
fontsize=11,
|
||||
fontweight="bold",
|
||||
color="#cf2b2b" if s < 1 else "#222")
|
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ax.axvline(1, ls="--", color="#888", lw=1)
|
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ax.set_yticks(yp); ax.set_yticklabels(names)
|
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ax.set_xlim(0,26)
|
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ax.set_yticks(yp)
|
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ax.set_yticklabels(names)
|
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ax.set_xlim(0, 26)
|
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ax.set_xlabel("speedup over baseline (n=5000)")
|
||||
#ax.set_title("optimization milestones (and the one that backfired)", loc="left", fontsize=13)
|
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ax.grid(axis="x", color="#eee"); ax.grid(axis="y", visible=False)
|
||||
ax.spines["top"].set_visible(False); ax.spines["right"].set_visible(False)
|
||||
ax.grid(axis="x", color="#eee")
|
||||
ax.grid(axis="y", visible=False)
|
||||
ax.spines["top"].set_visible(False)
|
||||
ax.spines["right"].set_visible(False)
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||||
plt.tight_layout()
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plt.savefig("analysis/fig_ladder.png", dpi=170, bbox_inches="tight")
|
||||
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||||
@@ -150,61 +227,119 @@ plt.savefig("analysis/fig_ladder.png", dpi=170, bbox_inches="tight")
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# (I_double ≈ 2.8k, I_float ≈ 5.6k), so each point uses its OWN I.
|
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# memory roof from a measured single-thread read: sysbench 37618 MiB/s -> ~19.7 bytes/cycle @ 2 GHz.
|
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def getI(v, n=5000):
|
||||
return df[(df.variant==v) & (df.n==n)]["I"].values[0]
|
||||
bw = 37618.01 * 1024**2 / 2e9 # MiB/s -> bytes/cycle at 2 GHz (≈ 19.72)
|
||||
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", "baseline (scalar)", "#7a7a7a", "d"),
|
||||
("variant_inlining_7_SoA_AVX2", "double, AVX-256", "#9bbbe0", "d"),
|
||||
("variant_AVX512_1", "double, AVX-512 ", "#4f86d6", "d"),
|
||||
("variant_inlining_8_SoA_AVX512_polyexp_moreavx", "double, AVX-512 + polyexp", "#2f5d9e", "d"),
|
||||
("variant_float_inlining_SoA_AVX2_polyexp", "float, AVX-256 + polyexp", "#e58aa6", "f"),
|
||||
("variant_float_inlining_SoA_AVX2_polyexp_rsqrt_loopopt", "float, AVX2 + polyexp + rsqrt", "#d05a82", "f"),
|
||||
("variant_float_inlining_SoA_AVX512_polyexp_moreavx_ilp_2_rsqrt_morersqrt", "float, AVX-512 + rsqrt + ILP (best)", maroon, "f"),
|
||||
("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
|
||||
LABX = {"d": 2400, "f": 4900} # label right-edge sits just left of each column
|
||||
|
||||
fig, ax = plt.subplots(figsize=(9.5,5.0))
|
||||
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(0.115, 0.85, "mem roof: DRAM read ≈ 37.6 GiB/s (≈19.7 B/cycle)", fontsize=8.3, color="#9a9a9a", ha="left")
|
||||
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,"scalar (4)"), (PEAK_D,"double (16)"), (PEAK_F,"float (32)")]:
|
||||
ax.axhline(pk, ls="--", color="#aaaaaa", lw=1.0)
|
||||
ax.text(0.11, pk*1.05, t+" peak", fontsize=9, color="#888")
|
||||
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")])
|
||||
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, 0.74, "I ≈ 2.8k", fontsize=8, color="#777", ha="center", va="top")
|
||||
ax.text(5625, 0.74, "I ≈ 5.6k", fontsize=8, color="#777", ha="center", va="top")
|
||||
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_xscale("log"); ax.set_yscale("log")
|
||||
ax.set_xlim(0.1, 16000); ax.set_ylim(0.6, 46)
|
||||
ax.set_xlabel("operational intensity I [flops/byte]")
|
||||
ax.set_ylabel("performance [flops/cycle]")
|
||||
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)
|
||||
ax.spines["top"].set_visible(False)
|
||||
ax.spines["right"].set_visible(False)
|
||||
plt.tight_layout()
|
||||
plt.savefig("analysis/fig_roofline.png", dpi=170, bbox_inches="tight")
|
||||
plt.savefig("analysis/fig_roofline.svg", bbox_inches="tight")
|
||||
|
||||
# plot 4: exp vs the polynomial approx (just to show its basically exact)
|
||||
# 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
|
||||
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))
|
||||
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.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")
|
||||
@@ -215,47 +350,56 @@ plt.savefig("analysis/fig_exp.png", dpi=170, bbox_inches="tight")
|
||||
# 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"),
|
||||
("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))
|
||||
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 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")
|
||||
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]); ax.set_yticklabels(["1","2","4","8","16","32"])
|
||||
ax.set_xlim(46,13000); ax.set_ylim(0.8,40)
|
||||
ax.set_xlabel("number of pedestrians n")
|
||||
ax.set_ylabel("performance [flops/cycle]")
|
||||
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)
|
||||
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()
|
||||
print("done")
|
||||
|
||||
# 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
|
||||
@@ -263,39 +407,70 @@ print("done")
|
||||
# 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")
|
||||
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)
|
||||
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 (approx)", "#9e8f2f", "-", 260, "above"),
|
||||
("variant_inlining_7_symmetry_2", "symmetry (half the pairs)", "#cf2b2b", "-", 320, "below"),
|
||||
("variant_inlining_7_SoA_AVX2", "double, AVX2", "#9bbbe0", "--", 650, "below"),
|
||||
("variant_inlining_8_SoA_AVX512_polyexp_moreavx", "double, AVX-512 + polyexp", "#2f5d9e", "-", 430, "above"),
|
||||
("variant_float_inlining_SoA_AVX2_polyexp_rsqrt_loopopt", "float, AVX2 + polyexp + rsqrt", "#d05a82", "--", 950, "below"),
|
||||
("variant_float_inlining_SoA_AVX512_polyexp_moreavx_ilp_2_rsqrt_morersqrt", "float, AVX-512 + rsqrt + ILP (best)", maroon, "-", 680, "above"),
|
||||
("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))
|
||||
|
||||
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))
|
||||
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")
|
||||
ax.set_xticks([50,100,200,500,1000,2000,5000]); 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.set_xlim(46,7000); ax.set_ylim(0.8,28)
|
||||
ax.set_xlabel("number of pedestrians n")
|
||||
ax.set_ylabel("speedup over baseline (wall-clock cycles)")
|
||||
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)
|
||||
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.png", dpi=170, bbox_inches="tight")
|
||||
plt.savefig("analysis/fig_speedup.svg", bbox_inches="tight")
|
||||
|
||||
Reference in New Issue
Block a user