Wallclock time plots

latex_table.py

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+import matplotlib.pyplot as plt
+import seaborn as sns
+import polars as pl
+import numpy as np
+
+from pathlib import Path
+
+workload_order = {val: idx for idx, val in enumerate(["vadd", "vmul", "haxpy", "gemv", "gemv_layers"])}
+
+workload_mapping = {
+    "vadd": "VADD",
+    "vmul": "VMUL",
+    "haxpy": "HAXPY",
+    "gemv": "GEMV",
+    "gemv_layers": "DNN",
+}
+
+out_directory = Path("tables_out")
+
+df = pl.read_csv("pim_results.csv")
+df = df.select(["workload", "level", "system", "frequency", "ticks"])
+
+for name, data in df.group_by(["frequency"], maintain_order=True):
+    data = data.pivot(index=["workload", "level"], columns=["system"], values=["ticks"])
+    data = data.sort(pl.col("workload").replace(workload_order))
+    data = data.with_columns(pl.col("workload").replace(workload_mapping))
+    data = data.rename({"HBM": "hbm", "PIM-HBM": "pim"})
+    print(data)
+
+    data.write_csv(out_directory / f"simulations_{name[0]}.csv")
+
+vega_df = pl.read_csv("vega_results.csv")
+vega_df = vega_df.with_columns(system=pl.lit("vega"))
+
+tesla_df = pl.read_csv("tesla_results.csv")
+tesla_df = tesla_df.with_columns(system=pl.lit("tesla"))
+
+torch_df = pl.concat([vega_df, tesla_df])
+
+torch_df = torch_df.pivot(index=["workload", "level"], columns=["system"], values=["runtime"])
+torch_df = torch_df.sort(pl.col("workload").replace(workload_order))
+torch_df = torch_df.with_columns(pl.col("workload").replace(workload_mapping))
+print(torch_df)
+
+torch_df.write_csv(out_directory / "torch.csv")