49 lines
1.4 KiB
Python
49 lines
1.4 KiB
Python
import matplotlib.pyplot as plt
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import seaborn as sns
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import pandas as pd
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import numpy as np
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from pathlib import Path
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df = pd.read_csv("pim_results.csv")
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sns.set_theme()
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def calc_speedup(x):
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return x.iat[0] / x.iat[1]
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workload_sets = [["vadd", "vmul", "haxpy"], ["gemv", "gemv_layers"]]
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for workload_set in workload_sets:
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workload_filter = df["workload"].isin(workload_set)
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for frequency in df["frequency"].unique():
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frequency_filter = df["frequency"] == frequency
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filtered_df = df[workload_filter & frequency_filter]
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print(filtered_df)
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preprocessed_df = filtered_df.groupby(["workload", "level", "frequency"], as_index=False).agg({"ticks": calc_speedup}).rename(columns={"ticks":"speedup"})
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print(preprocessed_df)
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# preprocessed_df.to_csv("plot.csv", index=False)
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g = sns.catplot(
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data=preprocessed_df, kind="bar",
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x="level", y="speedup", hue="workload",
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palette="dark", alpha=.6, height=6
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)
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g.despine(left=True)
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g.set_axis_labels("", "Speedup")
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g.set(title=frequency)
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g.legend.set_title("")
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for workload in workload_set:
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export_df = preprocessed_df[preprocessed_df["workload"] == workload]
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filename = f"{workload}_{frequency}.csv"
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directory = Path("plots_out")
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export_df.to_csv(directory / filename, index=False)
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plt.show()
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