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Dusty disc performance results#

Solver rate of the dusty disc perf test (dustydisc_perftest.py) on an H200, as a function of the number of dust species.

  9 import matplotlib as mpl
 10 import matplotlib.pyplot as plt
 11 import numpy as np
 12
 13 mpl.rcParams.update(
 14     {
 15         "font.family": "serif",
 16         "mathtext.fontset": "cm",
 17         "font.size": 14,
 18         "axes.labelsize": 16,
 19         "axes.titlesize": 16,
 20         "xtick.labelsize": 13,
 21         "ytick.labelsize": 13,
 22         "legend.fontsize": 13,
 23         "axes.facecolor": "white",
 24         "axes.linewidth": 1.0,
 25         "xtick.direction": "in",
 26         "ytick.direction": "in",
 27         "xtick.top": True,
 28         "ytick.right": True,
 29         "xtick.major.size": 8,
 30         "ytick.major.size": 8,
 31         "xtick.minor.visible": True,
 32         "ytick.minor.visible": True,
 33         "legend.frameon": True,
 34         "legend.fancybox": False,
 35         "legend.edgecolor": "black",
 36     }
 37 )
 38
 39 RESULTS_H200_BASELINE = [
 40     {"kernel": "M4", "ndust": 0, "rate": np.float64(20743174.163988266)},
 41     {"kernel": "M6", "ndust": 0, "rate": np.float64(13799390.088761112)},
 42     {"kernel": "M6", "ndust": 1, "rate": np.float64(12633702.042326497)},
 43     {"kernel": "M6", "ndust": 2, "rate": np.float64(12087424.370965026)},
 44     {"kernel": "M6", "ndust": 3, "rate": np.float64(11881595.212850157)},
 45     {"kernel": "M6", "ndust": 4, "rate": np.float64(11300411.830739705)},
 46     {"kernel": "M6", "ndust": 5, "rate": np.float64(11222855.348508552)},
 47     {"kernel": "M6", "ndust": 6, "rate": np.float64(10755208.603137823)},
 48     {"kernel": "M6", "ndust": 7, "rate": np.float64(10688006.42580047)},
 49     {"kernel": "M6", "ndust": 8, "rate": np.float64(10142698.857492624)},
 50     {"kernel": "M6", "ndust": 9, "rate": np.float64(10214823.874985265)},
 51     {"kernel": "M6", "ndust": 10, "rate": np.float64(9761393.55028728)},
 52     {"kernel": "M6", "ndust": 11, "rate": np.float64(9792483.14187795)},
 53     {"kernel": "M6", "ndust": 12, "rate": np.float64(9219263.745632991)},
 54     {"kernel": "M6", "ndust": 13, "rate": np.float64(9388890.664223792)},
 55     {"kernel": "M6", "ndust": 14, "rate": np.float64(8948305.414125301)},
 56     {"kernel": "M6", "ndust": 15, "rate": np.float64(9006443.219386235)},
 57     {"kernel": "M6", "ndust": 16, "rate": np.float64(8591518.90067488)},
 58     {"kernel": "M6", "ndust": 17, "rate": np.float64(8663145.798459316)},
 59     {"kernel": "M6", "ndust": 18, "rate": np.float64(8252145.146574976)},
 60     {"kernel": "M6", "ndust": 19, "rate": np.float64(8367198.978522308)},
 61     {"kernel": "M6", "ndust": 20, "rate": np.float64(7823688.987683165)},
 62     {"kernel": "M6", "ndust": 21, "rate": np.float64(8078987.197870458)},
 63     {"kernel": "M6", "ndust": 22, "rate": np.float64(7692061.528587866)},
 64     {"kernel": "M6", "ndust": 23, "rate": np.float64(7820297.588177133)},
 65     {"kernel": "M6", "ndust": 24, "rate": np.float64(7334507.940110926)},
 66     {"kernel": "M6", "ndust": 25, "rate": np.float64(7578401.6513876775)},
 67     {"kernel": "M6", "ndust": 26, "rate": np.float64(7200301.321665853)},
 68     {"kernel": "M6", "ndust": 27, "rate": np.float64(7341230.143859156)},
 69     {"kernel": "M6", "ndust": 28, "rate": np.float64(6858490.479193717)},
 70     {"kernel": "M6", "ndust": 29, "rate": np.float64(7151851.505432058)},
 71     {"kernel": "M6", "ndust": 30, "rate": np.float64(6780589.608352743)},
 72     {"kernel": "M6", "ndust": 31, "rate": np.float64(6957826.151235339)},
 73     {"kernel": "M6", "ndust": 32, "rate": np.float64(6871322.974704008)},
 74     {"kernel": "M6", "ndust": 33, "rate": np.float64(6766758.272378567)},
 75     {"kernel": "M6", "ndust": 34, "rate": np.float64(6416124.696613544)},
 76     {"kernel": "M6", "ndust": 35, "rate": np.float64(6595107.764492851)},
 77     {"kernel": "M6", "ndust": 36, "rate": np.float64(6124688.97834106)},
 78     {"kernel": "M6", "ndust": 37, "rate": np.float64(6428013.769450867)},
 79     {"kernel": "M6", "ndust": 38, "rate": np.float64(6106585.969761499)},
 80     {"kernel": "M6", "ndust": 39, "rate": np.float64(6261067.583511858)},
 81     {"kernel": "M6", "ndust": 40, "rate": np.float64(5846567.91142668)},
 82     {"kernel": "M6", "ndust": 41, "rate": np.float64(6130846.680557474)},
 83     {"kernel": "M6", "ndust": 42, "rate": np.float64(5781410.068906898)},
 84     {"kernel": "M6", "ndust": 43, "rate": np.float64(5978457.374960809)},
 85     {"kernel": "M6", "ndust": 44, "rate": np.float64(5510755.835376551)},
 86     {"kernel": "M6", "ndust": 45, "rate": np.float64(5847274.351241548)},
 87     {"kernel": "M6", "ndust": 46, "rate": np.float64(5533625.961877414)},
 88     {"kernel": "M6", "ndust": 47, "rate": np.float64(5712080.273564)},
 89     {"kernel": "M6", "ndust": 48, "rate": np.float64(5405527.541108767)},
 90     {"kernel": "M6", "ndust": 49, "rate": np.float64(5590447.5575348595)},
 91     {"kernel": "M6", "ndust": 50, "rate": np.float64(5307054.447021384)},
 92     {"kernel": "M6", "ndust": 51, "rate": np.float64(5474498.7673062915)},
 93     {"kernel": "M6", "ndust": 52, "rate": np.float64(5065365.2100912845)},
 94     {"kernel": "M6", "ndust": 53, "rate": np.float64(5353810.653996148)},
 95     {"kernel": "M6", "ndust": 54, "rate": np.float64(5066047.936317459)},
 96     {"kernel": "M6", "ndust": 55, "rate": np.float64(5242961.026497445)},
 97     {"kernel": "M6", "ndust": 56, "rate": np.float64(4883427.61396701)},
 98     {"kernel": "M6", "ndust": 57, "rate": np.float64(5132179.980544963)},
 99     {"kernel": "M6", "ndust": 58, "rate": np.float64(4853087.799315191)},
100     {"kernel": "M6", "ndust": 59, "rate": np.float64(5020866.021284079)},
101     {"kernel": "M6", "ndust": 60, "rate": np.float64(4618314.988822764)},
102     {"kernel": "M6", "ndust": 61, "rate": np.float64(4921603.58455902)},
103     {"kernel": "M6", "ndust": 62, "rate": np.float64(4648092.049223436)},
104 ]
105
106
107 def _lookup_rate(results, kernel, ndust):
108     for entry in results:
109         if entry["kernel"] == kernel and entry["ndust"] == ndust:
110             return float(entry["rate"])
111     return None
112
113
114 def plot_perf_results(results, title="Dusty disc performance"):
115     visible_ndust = {1, 5, 10, 20, 30, 40, 50, 60, 70}
116     labels = ["gas only M4", "gas only M6"]
117     rates_off = [
118         _lookup_rate(results, "M4", 0),
119         _lookup_rate(results, "M6", 0),
120     ]
121
122     ndust_values = sorted({entry["ndust"] for entry in results if entry["ndust"] > 0})
123     for ndust in ndust_values:
124         labels.append(str(ndust))
125         rates_off.append(_lookup_rate(results, "M6", ndust))
126
127     fig, ax = plt.subplots(figsize=(6.4, 6))
128     gas_count = 2
129     gas_spread = 2.0
130     ndust_gap = 4.0
131     x = np.arange(len(labels), dtype=float)
132     x[1:] += gas_spread
133     x[gas_count:] += ndust_gap
134     width = 2
135
136     gas_x = x[:gas_count]
137     gas_y = rates_off[:gas_count]
138     dust_x = x[gas_count:]
139     dust_y = rates_off[gas_count:]
140
141     ax.bar(gas_x, gas_y, width, label="coala off", color="C0")
142     ax.plot(dust_x, dust_y, color="C0", marker="o", markersize=3)
143
144     separator_x = (x[gas_count - 1] + x[gas_count]) / 2
145     ax.axvline(separator_x, color="0.4", linewidth=1.2, linestyle="-", zorder=0)
146
147     # ax.set_yscale("log")
148     # ax.set_ylim(1e6,3e7)
149     ax.set_ylabel("rate (particles/s)")
150     ax.set_xlabel("dust species", labelpad=-16)
151     ax.set_title(title)
152
153     baseline_rate = _lookup_rate(results, "M6", 0)
154     if baseline_rate:
155         ax.tick_params(axis="y", right=False, which="both")
156         secax = ax.secondary_yaxis(
157             "right", functions=(lambda r: r / baseline_rate, lambda s: s * baseline_rate)
158         )
159         secax.set_ylabel("speedup relative to M6 (gas only)")
160         for frac in (1.00, 0.75, 0.5):
161             y = frac * baseline_rate
162             ax.axhline(y, color="red", linewidth=1.0, linestyle="--", zorder=0)
163             ax.text(
164                 x[-1],
165                 y,
166                 f"{frac:.0%}",
167                 color="red",
168                 fontsize=11,
169                 va="bottom",
170                 ha="right",
171             )
172     ax.set_xticks(x)
173     visible_indices = {0, 1}
174     for ndust in visible_ndust:
175         visible_indices.add(1 + ndust)
176     tick_labels = [labels[i] if i in visible_indices else "" for i in range(len(labels))]
177     ax.set_xticklabels(tick_labels, rotation=90, fontsize=12)
178     for i, tick in enumerate(ax.xaxis.get_major_ticks()):
179         if i not in visible_indices:
180             tick.tick1line.set_visible(False)
181             tick.tick2line.set_visible(False)
182     # ax.legend()
183     ax.grid(axis="y", which="both", linestyle=":", alpha=0.5)
184     fig.tight_layout()
185     return fig, ax

Plot the results

190 fig, ax = plot_perf_results(RESULTS_H200_BASELINE, title="Dusty disc performance (H200)")
191 plt.show()
Dusty disc performance (H200)

Total running time of the script: (0 minutes 0.477 seconds)

Estimated memory usage: 162 MB

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