Merge pull request #2 from raphaeldichler/main

Add source code of 'Evaluation of the Performance of the Memory Tagging Extensions'

1 files changed, 72 insertions, 0 deletions

diff --git a/archive/2024/winter/bsc_dichler/scripts/plot/contiguous.py b/archive/2024/winter/bsc_dichler/scripts/plot/contiguous.py
new file mode 100644
index 000000000..24981138e
--- /dev/null
+++ b/archive/2024/winter/bsc_dichler/scripts/plot/contiguous.py
@@ -0,0 +1,72 @@
+from pathlib import Path
+import pandas as pd
+import matplotlib.pyplot as plt
+
+
+def plot(output_root: Path, format):
+    output_root = output_root / Path("contiguous")
+    csv_file1 = output_root / Path("load16.csv")
+    csv_file2 = output_root / Path("load16_mte.csv")
+
+    df1 = pd.read_csv(csv_file1, sep=";")
+    df2 = pd.read_csv(csv_file2, sep=";")
+
+    df1["kb"] = df1["len"] * 64 // 1024
+    df2["kb"] = df2["len"] * 64 // 1024
+
+    grouped1 = df1.groupby("kb")["duration"].agg(["mean", "std"]).reset_index()
+    grouped2 = df2.groupby("kb")["duration"].agg(["mean", "std"]).reset_index()
+
+    merged = pd.merge(grouped1, grouped2, on="kb", suffixes=("_1", "_2"))
+
+    _, ax1 = plt.subplots(figsize=(10, 6))
+
+    ax1.errorbar(
+        merged["kb"],
+        merged["mean_1"],
+        yerr=merged["std_1"],
+        fmt="o-",
+        color="#a6bddb",
+        capsize=5,
+        label="MTE disabled",
+    )
+    ax1.errorbar(
+        merged["kb"],
+        merged["mean_2"],
+        yerr=merged["std_2"],
+        fmt="s-",
+        color="#fc9272",
+        capsize=5,
+        label="MTE enabled",
+    )
+
+    merged["lower_1"] = merged["mean_1"] - merged["std_1"]
+    merged["lower_2"] = merged["mean_2"] - merged["std_2"]
+    merged["upper_1"] = merged["mean_1"] + merged["std_1"]
+    merged["upper_2"] = merged["mean_2"] + merged["std_2"]
+
+    ax1.set_xscale("log", base=2)
+    ax1.set_yscale("log", base=2)
+
+    ax1.legend(loc="upper left")
+    ax1.grid(True, which="both", linestyle="--", linewidth=0.5)
+
+    xticks = grouped1["kb"].to_numpy()
+    xticks_filtered = [x for i, x in enumerate(xticks) if i % 2 == 0]
+    xtick_labels = [
+        f"{int(x/1024)} MiB" if x >= 1024 else f"{int(x)} KiB" for x in xticks_filtered
+    ]
+
+    plt.xticks(xticks_filtered, xtick_labels, rotation=45, ha="right")
+
+    plt.title("Lower is better ↓", color="navy")
+    plt.ylabel("Time (ns - logarithmic scale)")
+    plt.xlabel("Memory size (logarithmic scale)")
+
+    ax1.spines["top"].set_visible(False)
+    ax1.spines["right"].set_visible(False)
+
+    plt.tight_layout()
+
+    output = output_root / Path(f"result.{format}")
+    plt.savefig(output, format=format)