plot the throughput benchmark for source node 0 and destination nodess {8,11,12,15} only and as a bar plot for use in thesis

benchmarks/benchmark-plotters/plot-perf-peakthroughput-bar.py

@@ -0,0 +1,108 @@
+import os
+import json
+import pandas as pd
+from itertools import chain
+import seaborn as sns
+import matplotlib.pyplot as plt
+
+from common import calc_throughput
+
+result_path = "benchmark-results/"
+output_path = "benchmark-plots/"
+
+runid = "Run ID"
+x_label = "Destination Node"
+y_label = "Throughput"
+
+title_allnodes = \
+    """Copy Throughput in GiB/s tested for 1GiB Elements\n
+    Using all 8 DSA Chiplets available on the System"""
+title_smartnodes = \
+    """Copy Throughput in GiB/s tested for 1GiB Elements\n
+    Using Cross-Copy for Intersocket and all 4 Chiplets of Socket for Intrasocket"""
+title_difference = \
+    """Gain in Copy Throughput in GiB/s of All-DSA vs. Smart Assignment"""
+
+description_smartnodes = \
+    """Copy Throughput in GiB/s tested for 1GiB Elements\n
+    Nodes of {8...15} are HBM accessors for their counterparts (minus 8)\n
+    Using all 4 DSA Chiplets of a Socket for Intra-Socket Operation\n
+    And using only the Source and Destination Nodes DSA for Inter-Socket"""
+description_allnodes = \
+    """Copy Throughput in GiB/s tested for 1GiB Elements\n
+    Nodes of {8...15} are HBM accessors for their counterparts (minus 8)\n
+    Using all 8 DSA Chiplets available on the System"""
+
+index = [ runid, x_label, y_label]
+data = []
+
+
+# loads the measurements from a given file and processes them
+# so that they are normalized, meaning that the timings returned
+# are nanoseconds per element transfered
+def load_time_mesurements(file_path):
+    with open(file_path, 'r') as file:
+        data = json.load(file)
+        count = data["count"]
+        batch_size = data["list"][0]["task"]["batching"]["batch_size"] if data["list"][0]["task"]["batching"]["batch_size"] > 0 else 1
+        iterations = data["list"][0]["task"]["iterations"]
+
+        return {
+            "size": data["list"][0]["task"]["size"],
+            "total": sum([x / (iterations * batch_size * count * count) for x in list(chain([data["list"][i]["report"]["time"]["total"] for i in range(count)]))]),
+            "combined": [ x / (count * batch_size) for x in list(chain(*[data["list"][i]["report"]["time"]["combined"] for i in range(count)]))],
+            "submission": [ x / (count * batch_size) for x in list(chain(*[data["list"][i]["report"]["time"]["submission"] for i in range(count)]))],
+            "completion": [ x / (count * batch_size) for x in list(chain(*[data["list"][i]["report"]["time"]["completion"] for i in range(count)]))]
+        }
+
+
+# procceses a single file and appends the desired timings
+# to the global data-array, handles multiple runs with a runid
+# and ignores if the given file is not found as some
+# configurations may not be benchmarked
+def process_file_to_dataset(file_path, src_node, dst_node):
+    try:
+        file_data = load_time_mesurements(file_path)
+        time = [file_data["total"]]
+        run_idx = 0
+        for t in time:
+            data.append({ runid : run_idx, x_label : dst_node, y_label : calc_throughput(file_data["size"], t)})
+            run_idx = run_idx + 1
+    except FileNotFoundError:
+        return
+
+
+def plot_bar(table,title,node_config):
+    plt.figure(figsize=(8, 6))
+
+    sns.barplot(x=x_label, y=y_label, data=table, palette="rocket", errorbar=None)
+
+    plt.ylim(0, 100)
+
+    plt.savefig(os.path.join(output_path, f"plot-perf-{node_config}-throughput-selectbarplot.png"), bbox_inches='tight')
+    plt.show()
+
+
+# loops over all possible configuration combinations and calls
+# process_file_to_dataset for them in order to build a dataframe
+# which is then displayed and saved
+def main(node_config,title):
+    src_node = 0
+    for dst_node in {8,11,12,15}:
+        size = "512mib" if node_config == "allnodes" and src_node == dst_node and src_node >= 8 else "1gib" 
+        file = os.path.join(result_path, f"copy-n{src_node}ton{dst_node}-{size}-{node_config}-1e.json")
+        process_file_to_dataset(file, src_node, dst_node)
+
+    df = pd.DataFrame(data)
+
+    data.clear()
+    df.set_index(index, inplace=True)
+
+    plot_bar(df, title, node_config)
+
+    return df
+
+
+if __name__ == "__main__":
+    dall = main("allnodes", title_allnodes)
+    dsmart = main("smart", title_smartnodes)