updated create csv and updated plots
This commit is contained in:
Linus Baumberger
committed by
[Quy Anh] «Elliot» Nguyen
[Quy Anh] «Elliot» Nguyen
parent
daf75faaf3
commit
ac9ef23559
+84
-30
@@ -81,13 +81,14 @@ 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_inlining_7_SoA_AVX2", "double, AVX2", "#9bbbe0", "--"),
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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, AVX2 + polyexp", "#e58aa6", "--"),
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("variant_float_inlining_SoA_AVX2_polyexp_rsqrt_loopopt", "float, AVX2 + polyexp + rsqrt", "#d05a82", "--"),
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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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]
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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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@@ -144,43 +145,53 @@ plt.tight_layout()
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plt.savefig("analysis/fig_ladder.png", dpi=170, bbox_inches="tight")
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# plot3 roofline
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# every variant has the same I at a given n (W and Q dont change between variants), so the
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# points just stack vertically. we're miles to the right of the ridge -> compute bound.
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fig,ax = plt.subplots(figsize=(9,4.7))
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bw = 60 # bytes/cycle, ~120 GB/s lpddr5x / 2GHz. rough
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xx = np.logspace(-1, 3.6, 200)
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ax.plot(xx, np.minimum(bw*xx, PEAK_F), color="#cccccc", lw=1.2)
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# plot 3: roofline (n = 5000). same 7 variants as fig_tracks, same colors, inline labels.
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# float variants move HALF the bytes -> 2x the operational intensity of the double ones
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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):
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return df[(df.variant==v) & (df.n==n)]["I"].values[0]
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bw = 37618.01 * 1024**2 / 2e9 # MiB/s -> bytes/cycle at 2 GHz (≈ 19.72)
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for pk,t in [(SCALAR,"scalar (4)"),(PEAK_D,"double (16)"),(PEAK_F,"float (32)")]:
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ax.axhline(pk, ls="--", color="#999", lw=1.1)
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ax.text(0.11, pk*1.04, t+" peak", fontsize=9.5, color="#888")
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I5000 = df[(df.variant=="variant_baseline") & (df.n==5000)]["I"].values[0]
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pts = [
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("variant_baseline", "#7a7a7a"),
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("variant_AVX512_1", "#2f9e8f"),
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("variant_inlining_8_SoA_AVX512_polyexp_moreavx", "#e08a1e"),
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("variant_float_inlining_SoA_AVX512_polyexp_moreavx", "#c0436a"),
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("variant_float_inlining_SoA_AVX512_polyexp_moreavx_ilp_2_rsqrt_morersqrt", maroon),
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# (variant, label, color, group) group "d" = double column, "f" = float column
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roof = [
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("variant_baseline", "baseline (scalar)", "#7a7a7a", "d"),
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("variant_inlining_7_SoA_AVX2", "double, AVX-256", "#9bbbe0", "d"),
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("variant_AVX512_1", "double, AVX-512 ", "#4f86d6", "d"),
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("variant_inlining_8_SoA_AVX512_polyexp_moreavx", "double, AVX-512 + polyexp", "#2f5d9e", "d"),
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("variant_float_inlining_SoA_AVX2_polyexp", "float, AVX-256 + polyexp", "#e58aa6", "f"),
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("variant_float_inlining_SoA_AVX2_polyexp_rsqrt_loopopt", "float, AVX2 + polyexp + rsqrt", "#d05a82", "f"),
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("variant_float_inlining_SoA_AVX512_polyexp_moreavx_ilp_2_rsqrt_morersqrt", "float, AVX-512 + rsqrt + ILP (best)", maroon, "f"),
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]
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for v,c in pts:
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ax.plot(I5000, get(v), "o", ms=9, color=c, mec="white", mew=1)
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LABX = {"d": 2400, "f": 4900} # label right-edge sits just left of each column
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fig, ax = plt.subplots(figsize=(9.5,5.0))
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xx = np.logspace(-1, 4.2, 200)
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ax.plot(xx, np.minimum(bw*xx, PEAK_F), color="#bbbbbb", lw=1.4)
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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")
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ax.text(I5000, 1.3, "n=5000", fontsize=10, color="#555")
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for pk,t in [(SCALAR,"scalar (4)"), (PEAK_D,"double (16)"), (PEAK_F,"float (32)")]:
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ax.axhline(pk, ls="--", color="#aaaaaa", lw=1.0)
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ax.text(0.11, pk*1.05, t+" peak", fontsize=9, color="#888")
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for v,lab,c,grp in roof:
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x,y = getI(v), get(v)
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ax.plot(x, y, "o", ms=10, color=c, mec="white", mew=1.4, zorder=5)
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t = ax.text(LABX[grp], y, lab, color=c, fontsize=8, fontweight="bold", ha="right", va="center", zorder=6)
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t.set_path_effects([pe.withStroke(linewidth=2.5, foreground="white")])
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ax.text(2812, 0.74, "I ≈ 2.8k", fontsize=8, color="#777", ha="center", va="top")
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ax.text(5625, 0.74, "I ≈ 5.6k", fontsize=8, color="#777", ha="center", va="top")
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ax.set_xscale("log"); ax.set_yscale("log")
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ax.set_xlim(0.1, 6500); ax.set_ylim(0.7, 45)
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ax.set_xlim(0.1, 16000); ax.set_ylim(0.6, 46)
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ax.set_xlabel("operational intensity I [flops/byte]")
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ax.set_ylabel("performance [flops/cycle]")
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#ax.set_title("roofline: deeply compute-bound (I grows with n; work O(n^2), data O(n))", loc="left", fontsize=12)
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ax.grid(color="#ededed")
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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_roofline.png", dpi=170, bbox_inches="tight")
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# plot 4: exp vs the polynomial approx (just to show its basically exact)
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x = np.linspace(-6, 0, 300)
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ex = np.exp(x)
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@@ -205,7 +216,7 @@ plt.savefig("analysis/fig_exp.png", dpi=170, bbox_inches="tight")
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# "above" or "below" -> alternate the crowded float labels above/below their line.
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milestones = [
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("variant_baseline", "baseline", "#7a7a7a", "-", None, "above"),
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("variant_cutoff_callsite", "baseline + cutoff (best scalar)", "#9e8f2f", "-", None, "below"),
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#("variant_cutoff_callsite", "baseline + cutoff (best scalar)", "#9e8f2f", "-", None, "below"),
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("variant_inlining_7_SoA_AVX2", "AVX2 (double)", "#3b6fb0", "--", None, "above"),
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("variant_inlining_7_SoA_AVX2_polyexp", "AVX2 + polyexp (double)", "#2f9e8f", "--", None, "below"),
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("variant_inlining_8_SoA_AVX512_polyexp_moreavx", "AVX-512 (double)", "#2f5d9e", "-", None, "below"),
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@@ -244,4 +255,47 @@ plt.savefig("analysis/fig_milestones.png", dpi=170, bbox_inches="tight")
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#plt.show()
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print("done")
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print("done")
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# plot 6: wall-clock speedup over baseline (runtime, not performance).
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# the fair view for optimizations that change the flop count -- cutoff skips ~40% of the
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# inner loop, symmetry does half the pairs -- so flops/cycle misrepresents them. T_base /
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# T_variant compares time-to-solve the same n regardless of how many flops each one does.
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# curated to the milestones that carry the story + symmetry as the cautionary line.
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basec = df[df.variant == "variant_baseline"].set_index("n")["cycles"].to_dict()
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def speedline(v):
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s = df[df.variant == v].sort_values("n")
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ns = [n for n in s.n.values if n in basec] # baseline has no n=10000 run
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sp = [basec[n] / s[s.n == n]["cycles"].values[0] for n in ns]
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return np.array(ns, float), np.array(sp, float)
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# (variant, label, color, linestyle, xpos|None, side)
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speed = [
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("variant_cutoff_callsite", "scalar + cutoff (approx)", "#9e8f2f", "-", 260, "above"),
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("variant_inlining_7_symmetry_2", "symmetry (half the pairs)", "#cf2b2b", "-", 320, "below"),
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("variant_inlining_7_SoA_AVX2", "double, AVX2", "#9bbbe0", "--", 650, "below"),
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("variant_inlining_8_SoA_AVX512_polyexp_moreavx", "double, AVX-512 + polyexp", "#2f5d9e", "-", 430, "above"),
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("variant_float_inlining_SoA_AVX2_polyexp_rsqrt_loopopt", "float, AVX2 + polyexp + rsqrt", "#d05a82", "--", 950, "below"),
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("variant_float_inlining_SoA_AVX512_polyexp_moreavx_ilp_2_rsqrt_morersqrt", "float, AVX-512 + rsqrt + ILP (best)", maroon, "-", 680, "above"),
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]
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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,xpos,side in speed:
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x,y = speedline(v)
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if len(x) == 0:
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print("skip (not in csv):", v); continue
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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, xpos, side))
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ax.axhline(1, ls=":", color="#999", lw=1.3)
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ax.text(52, 1.03, "baseline (1x)", fontsize=9, color="#888")
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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]); ax.set_xticklabels(["50","100","200","500","1k","2k","5k"])
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ax.set_yticks([1,2,5,10,20]); ax.set_yticklabels(["1x","2x","5x","10x","20x"])
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ax.set_xlim(46,7000); ax.set_ylim(0.8,28)
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ax.set_xlabel("number of pedestrians n")
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ax.set_ylabel("speedup over baseline (wall-clock cycles)")
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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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plt.tight_layout()
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label_lines_above(ax, ends, fontsize=7.5, dy_frac=0.02, halo=2)
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plt.savefig("analysis/fig_speedup.png", dpi=170, bbox_inches="tight")
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