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f10431d24f
Improve reproducibility by documenting the steps needed to run the benchmarks and generate the plots. Also simplify plot generation a bit.
84 lines
2.5 KiB
Python
84 lines
2.5 KiB
Python
import sys
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import matplotlib.pyplot as plt
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import pandas as pd
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import scienceplots # noqa: F401
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plt.style.use(["science"])
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plt.rcParams.update({
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"text.usetex": True,
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"font.family": "serif",
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"font.serif": ["Times"],
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})
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# Get CSV filenames from command line arguments
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if len(sys.argv) < 2:
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print("Usage: python script.py <csv_filename> [<csv_filename> ...]")
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sys.exit(1)
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csv_filenames = sys.argv[1:]
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# Output filename
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output_filename = "compute-impact.pdf"
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# Read data from all CSV files and concatenate
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dfs = [pd.read_csv(filename) for filename in csv_filenames]
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df = pd.concat(dfs, ignore_index=True)
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# Create figure and axis
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fig, ax = plt.subplots(figsize=(10, 6))
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# Get unique systems and thread counts
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systems = df["system"].unique()
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thread_counts = sorted(df["threads"].unique())
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# Get colors from the current color cycle
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prop_cycle = plt.rcParams["axes.prop_cycle"]
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colors_list = prop_cycle.by_key()["color"]
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# Plot a line for each system-thread combination
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markers = ["o", "s", "^", "D"]
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linestyles = ["-", "--", "-.", ":"]
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plot_idx = 0
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for sys_idx, system in enumerate(systems):
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df_system = df[df["system"] == system]
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for thread_idx, threads in enumerate(thread_counts):
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df_thread = df_system[df_system["threads"] == threads].sort_values("compute")
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if len(df_thread) > 0:
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ax.plot(df_thread["compute"], df_thread["throughput"],
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marker=markers[thread_idx % len(markers)],
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color=colors_list[plot_idx % len(colors_list)],
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linestyle=linestyles[sys_idx % len(linestyles)],
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linewidth=2, markersize=8,
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label=f'{system} ({threads} thread{"s" if threads > 1 else ""})')
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plot_idx += 1
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# Customize the plot
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ax.set_xlabel(r"Compute Time (microseconds)", fontsize=14, fontweight="bold")
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ax.set_ylabel("Throughput (rows/second)", fontsize=14, fontweight="bold")
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# Set y-axis to start from 0 with dynamic upper limit
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max_throughput = df["throughput"].max()
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ax.set_ylim(0, max_throughput * 1.15) # Add 15% tolerance for legend space
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# Format y-axis labels
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ax.yaxis.set_major_formatter(plt.FuncFormatter(lambda x, p: f"{int(x/1000)}k"))
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# Add legend
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ax.legend(loc="lower left", frameon=True, fontsize=11)
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# Add grid for better readability
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ax.grid(axis="both", alpha=0.3, linestyle="--")
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ax.set_axisbelow(True)
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# Adjust layout
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plt.tight_layout()
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# Save the figure
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plt.savefig(output_filename, dpi=300, bbox_inches="tight")
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print(f"Saved plot to {output_filename}")
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# Display the plot
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plt.show()
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