add plotter for submit method with logscale to show optimal

benchmarks/benchmark-plotters/plot-opt-submitmethod.py

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+import os
+import json
+import pandas as pd
+from pandas.core.ops import methods
+from typing import List
+import seaborn as sns
+import matplotlib.pyplot as plt
+
+runid = "Run ID"
+x_label = "Size of Submitted Task"
+y_label = "Throughput in GiB/s, LogScale"
+var_label = "Submission Type"
+sizes = ["1kib", "4kib", "1mib", "32mib"]
+sizes_nice = ["1 KiB", "4 KiB", "1 MiB", "32 MiB"]
+types = ["bs10", "bs50", "ms10", "ms50", "ssaw"]
+types_nice = ["Batch, Size 10", "Batch, Size 50", "Multi-Submit, Count 10", "Multi-Submit, Count 50", "Single Submit"]
+title = "Optimal Submission Method - Copy Operation tested Intra-Node on DDR"
+
+index = [runid, x_label, var_label]
+data = []
+
+def calc_throughput(size_bytes,time_microseconds):
+    time_seconds = time_microseconds * 1e-9
+    size_gib = size_bytes / (1024 ** 3)
+    throughput_gibs = size_gib / time_seconds
+    return throughput_gibs
+
+
+def index_from_element(value,array):
+    for (idx,val) in enumerate(array):
+        if val == value: return idx
+    return 0
+
+
+def load_and_process_submit_json(file_path):
+    with open(file_path, 'r') as file:
+        data = json.load(file)
+        return data["list"][0]["report"]["time"]
+
+
+# Function to plot the graph for the new benchmark
+def create_submit_dataset(file_paths, type_label):
+    times = []
+
+    type_index = index_from_element(type_label,types)
+    type_nice = types_nice[type_index]
+
+    idx = 0
+    for file_path in file_paths:
+        time = load_and_process_submit_json(file_path)
+        times.append(time["combined"])
+        idx = idx + 1
+
+    # Adjust time measurements based on type
+    # which can contain multiple submissions
+    if type_label in {"bs10", "ms10"}:
+        times = [[t / 10  for t in time] for time in times]
+    elif type_label in {"ms50", "bs50"}:
+        times = [[t / 50  for t in time] for time in times]
+
+    times[0] = [t / 1 for t in times[0]]
+    times[1] = [t / 4 for t in times[1]]
+    times[2] = [t / (1024) for t in times[2]]
+    times[3] = [t / (32*1024) for t in times[3]]
+
+    throughput = [[calc_throughput(1024,time) for time in t] for t in times]
+
+    idx = 0
+    for run_set in throughput:
+        run_idx = 0
+        for run in run_set:
+            data.append({ runid : run_idx, x_label: sizes_nice[idx], var_label : type_nice, y_label : throughput[idx][run_idx]})
+            run_idx = run_idx + 1
+        idx = idx + 1
+
+
+# Main function to iterate over files and create plots for the new benchmark
+def main():
+    folder_path = "benchmark-results/"  # Replace with the actual path to your folder
+
+    for type_label in types:
+        file_paths = [os.path.join(folder_path, f"submit-{type_label}-{size}-1e.json") for size in sizes]
+        create_submit_dataset(file_paths, type_label)
+
+    df = pd.DataFrame(data)
+    df.set_index(index, inplace=True)
+    df = df.sort_values(y_label)
+
+    sns.barplot(x=x_label, y=y_label, hue=var_label, data=df, palette="rocket", errorbar="sd").set(yscale="log")
+
+    plt.title(title)
+    plt.savefig(os.path.join(folder_path, "plot-opt-submitmethod.png"), bbox_inches='tight')
+    plt.show()
+
+if __name__ == "__main__":
+    main()