add benchmark plotting scripts using seaborn to display the results, the plots are also added

benchmarks/benchmark-plotters/plot-cost-mtsubmit.py

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+import os
+import json
+import pandas as pd
+from pandas.core.ops import methods
+import seaborn as sns
+import matplotlib.pyplot as plt
+
+x_label = "Copy Type"
+y_label = "Time in Microseconds"
+var_label = "Thread Counts"
+thread_counts = ["1t", "2t", "4t", "8t", "12t"]
+thread_counts_nice = ["1 Thread", "2 Threads", "4 Threads", "8 Threads", "12 Threads"]
+engine_counts = ["1e", "4e"]
+engine_counts_nice = ["1 Engine per Group", "4 Engines per Group"]
+
+data = {
+    x_label : thread_counts_nice,
+    engine_counts_nice[0] : [],
+    engine_counts_nice[1] : [],
+}
+
+
+def index_from_element(value,array):
+    for (idx,val) in enumerate(array):
+        if val == value: return idx
+    return 0
+
+
+# Function to load and process the JSON file for the multi-threaded benchmark
+def load_and_process_mt_json(file_path):
+    with open(file_path, 'r') as file:
+        data = json.load(file)
+
+        # Extracting count from JSON structure
+        count = data["count"]
+
+        # Extracting time from JSON structure for elements 0 to count
+        times = [data["list"][i]["report"]["time"]["combined_avg"] for i in range(count)]
+
+        # Calculating the average of times
+        average_time = sum(times) / count
+
+        return average_time
+
+
+# Function to plot the graph for the new benchmark
+def plot_mt_graph(file_paths, engine_label):
+    times = []
+
+    for file_path in file_paths:
+        # Load and process JSON file for the new benchmark
+        time_microseconds = load_and_process_mt_json(file_path)
+        times.append(time_microseconds)
+
+    engine_index = index_from_element(engine_label,engine_counts)
+    engine_nice = engine_counts_nice[engine_index]
+
+    data[engine_nice] = times
+
+
+# Main function to iterate over files and create plots for the new benchmark
+def main():
+    folder_path = "benchmark-results/mtsubmit-bench/"  # Replace with the actual path to your folder
+
+    for engine_label in engine_counts:
+        mt_file_paths = [os.path.join(folder_path, f"mtsubmit-{thread_count}-{engine_label}.json") for thread_count in thread_counts]
+        plot_mt_graph(mt_file_paths, engine_label)
+
+    df = pd.DataFrame(data)
+    dfm = pd.melt(df, id_vars=x_label, var_name=var_label, value_name=y_label)
+
+    sns.catplot(x=x_label, y=y_label, hue=var_label, data=dfm, kind='bar', height=5, aspect=1, palette="viridis")
+    plt.savefig(os.path.join(folder_path, "plot-cost-mtsubmit.png"))
+    plt.show()
+
+if __name__ == "__main__":
+    main()

benchmarks/benchmark-plotters/plot-perf-enginelocation.py

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+import os
+import json
+import pandas as pd
+from pandas.core.ops import methods
+import seaborn as sns
+import matplotlib.pyplot as plt
+
+x_label = "Copy Type"
+y_label = "Time in Microseconds"
+var_label = "Configuration"
+types = ["intersock-n0ton4", "internode-n0ton1"]
+types_nice = ["Inter-Socket Copy", "Inter-Node Copy"]
+copy_methods = ["dstcopy", "srccopy", "xcopy"]
+copy_methods_nice = [ "Engine on DST-Node", "Engine on SRC-Node", "Cross-Copy / Both Engines" ]
+
+data = {
+    x_label : types_nice,
+    copy_methods_nice[0] : [],
+    copy_methods_nice[1] : [],
+    copy_methods_nice[2] : []
+}
+
+
+def index_from_element(value,array):
+    for (idx,val) in enumerate(array):
+        if val == value: return idx
+    return 0
+
+
+# Function to load and process the JSON file for the new benchmark
+def load_and_process_copy_json(file_path, method_label):
+    with open(file_path, 'r') as file:
+        data = json.load(file)
+
+        # Extracting time from JSON structure
+        if method_label == "xcopy":
+            # For xcopy method, add times from two entries and divide by 4
+            time_entry1 = data["list"][0]["report"]["time"]["combined_avg"]
+            time_entry2 = data["list"][1]["report"]["time"]["combined_avg"]
+            time_microseconds = (time_entry1 + time_entry2) / 4
+        else:
+            # For other methods, use the time from the single entry
+            time_microseconds = data["list"][0]["report"]["time"]["combined_avg"]
+
+        return time_microseconds
+
+
+# Function to plot the graph for the new benchmark
+def plot_copy_graph(file_paths, method_label):
+    times = []
+
+    for file_path in file_paths:
+        # Load and process JSON file for the new benchmark
+        time_microseconds = load_and_process_copy_json(file_path, method_label)
+        times.append(time_microseconds)
+
+    method_index = index_from_element(method_label,copy_methods)
+    method_nice = copy_methods_nice[method_index]
+
+    data[method_nice] = times
+
+
+# Main function to iterate over files and create plots for the new benchmark
+def main():
+    folder_path = "benchmark-results/cross-copy-bench/"  # Replace with the actual path to your folder
+
+
+    for method_label in copy_methods:
+        copy_file_paths = [os.path.join(folder_path, f"{method_label}-{type_label}-1mib-4e.json") for type_label in types]
+        plot_copy_graph(copy_file_paths, method_label)
+
+    df = pd.DataFrame(data)
+    dfm = pd.melt(df, id_vars=x_label, var_name=var_label, value_name=y_label)
+
+    sns.catplot(x=x_label, y=y_label, hue=var_label, data=dfm, kind='bar', height=5, aspect=1, palette="viridis")
+    plt.savefig(os.path.join(folder_path, "plot-perf-enginelocation.png"))
+    plt.show()
+
+if __name__ == "__main__":
+    main()

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

@@ -0,0 +1,105 @@
+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
+
+x_label = "Size of Submitted Task"
+y_label = "Time to Copy 1 KiB in Microseconds"
+var_label = "Submission Type"
+sizes = ["1kib", "4kib", "1mib", "1gib"]
+sizes_nice = ["1 KiB", "4 KiB", "1 MiB", "1 GiB"]
+types = ["bs10", "bs50", "ms10", "ms50", "ssaw"]
+types_nice = ["Batch, Size 10", "Batch, Size 50", "Multi-Submit, Count 10", "Multi Submit, Count 50", "Single Submit"]
+
+data = {
+    x_label : sizes_nice,
+    types_nice[0] : [],
+    types_nice[1] : [],
+    types_nice[2] : [],
+    types_nice[3] : [],
+    types_nice[4] : []
+}
+
+stdev = {}
+
+
+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,s,t):
+    with open(file_path, 'r') as file:
+        data = json.load(file)
+        time_microseconds = data["list"][0]["report"]["time"]["combined_avg"]
+        if t not in stdev: stdev[t] = dict()
+        stdev[t][s] = data["list"][0]["report"]["time"]["combined_stdev"]
+        return time_microseconds
+
+
+def stdev_functor(values):
+    v = values[0]
+    sd = stdev[v]
+    return (v - sd, v + sd)
+
+
+# Function to plot the graph for the new benchmark
+def plot_submit_graph(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_microseconds = load_and_process_submit_json(file_path,sizes_nice[idx],type_nice)
+        times.append(time_microseconds)
+        idx = idx + 1
+
+    # Adjust time measurements based on type
+    # which can contain multiple submissions
+    if type_label in {"bs10", "ms10"}:
+        times = [time / 10 for time in times]
+    elif type_label in {"ms50", "bs50"}:
+        times = [time / 50 for time in times]
+
+    times[0] = times[0] / 1
+    times[1] = times[1] / 4
+    times[2] = times[2] / 1024
+    times[3] = times[3] / (1024 * 1024)
+
+    data[type_nice] = times
+
+
+# Main function to iterate over files and create plots for the new benchmark
+def main():
+    folder_path = "benchmark-results/submit-bench/"  # 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]
+        plot_submit_graph(file_paths, type_label)
+
+    df = pd.DataFrame(data)
+    dfm = pd.melt(df, id_vars=x_label, var_name=var_label, value_name=y_label)
+
+    error_values: List[float] = []
+    for index,row in dfm.iterrows():
+        s = dfm[x_label][index]
+        t = dfm[var_label][index]
+        error_values.append(stdev[t][s])
+
+    dfm["Stdev"] = error_values
+
+    print(dfm)
+
+    sns.catplot(x=x_label, y=y_label, hue=var_label, data=dfm, kind='bar', height=5, aspect=1, palette="viridis", errorbar=("ci", 100))
+    plt.title("Performance of Submission Methods - Copy Operatione tested Intra-Node on DDR")
+    plt.savefig(os.path.join(folder_path, "plot-perf-submitmethod.png"))
+    plt.show()
+
+if __name__ == "__main__":
+    main()