Wallclock time plots

torch_plots.py

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+import matplotlib.pyplot as plt
+import seaborn as sns
+import polars as pl
+
+from pathlib import Path
+
+out_directory = Path("torch_plots_out")
+
+system_mapping = {
+    "HBM": "hbm",
+    "PIM-HBM": "pim"
+}
+
+gem_df = pl.read_csv("pim_results.csv")
+gem_df = gem_df.with_columns(pl.col("system").replace(system_mapping))
+gem_df = gem_df.with_columns(
+    pl.concat_str(["system", "frequency"], separator="_").alias("system")
+)
+gem_df = gem_df.select(["workload", "level", "system", "ticks"])
+
+vega_df = pl.read_csv("vega_results.csv")
+vega_df = vega_df.rename({"runtime": "ticks"})
+vega_df = vega_df.with_columns(pl.lit("vega").alias("system"))
+
+tesla_df = pl.read_csv("tesla_results.csv")
+tesla_df = tesla_df.rename({"runtime": "ticks"})
+tesla_df = tesla_df.with_columns(pl.lit("tesla").alias("system"))
+
+df = pl.concat([gem_df, vega_df, tesla_df], how="diagonal")
+
+workload_sets = [["vadd", "vmul", "haxpy"], ["gemv", "dnn"]]
+
+workload_mapping = {
+    "gemv_layers": "dnn",
+}
+
+df = df.with_columns(pl.col("workload").replace(workload_mapping))
+
+# for workload_set in workload_sets:
+#     temp_df = df.filter(pl.col("workload").is_in(workload_set))
+
+g = sns.catplot(
+    data=df.to_pandas(),
+    kind="bar",
+    x="level",
+    y="ticks",
+    hue="system",
+    col="workload",
+    palette="dark",
+    alpha=0.6,
+    height=6,
+)
+
+for name, data in df.group_by("system"):
+    data = data.pivot(index=["level"], columns=["workload"], values=["ticks"])
+    data.write_csv(out_directory / f"{name}.csv")
+    print(data)
+
+plt.show()