diff --git a/benchmarks/benchmark-descriptors/peak-perf-brute-cpu/copy-n0ton11-1gib-allnodes-cpu.json b/benchmarks/benchmark-descriptors/peak-perf-brute-cpu/copy-n0ton11-1gib-allnodes-cpu.json index 4f26208..76c5545 100644 --- a/benchmarks/benchmark-descriptors/peak-perf-brute-cpu/copy-n0ton11-1gib-allnodes-cpu.json +++ b/benchmarks/benchmark-descriptors/peak-perf-brute-cpu/copy-n0ton11-1gib-allnodes-cpu.json @@ -1,5 +1,5 @@ { - "count": 48, + "count": 12, "list": [ { "affinity": { @@ -180,726 +180,6 @@ "iterations": 10, "size": 1073741824 } - }, - { - "affinity": { - "nnode_dst": 11, - "nnode_src": 0, - "node": 1 - }, - "task": { - "batching": { - "batch_size": 0, - "batch_submit": false - }, - "iterations": 10, - "size": 1073741824 - } - }, - { - "affinity": { - "nnode_dst": 11, - "nnode_src": 0, - "node": 1 - }, - "task": { - "batching": { - "batch_size": 0, - "batch_submit": false - }, - "iterations": 10, - "size": 1073741824 - } - }, - { - "affinity": { - "nnode_dst": 11, - 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b/benchmarks/benchmark-plotters/plot-perf-peakthroughput-cpu-bar.py new file mode 100644 index 0000000..744cdbc --- /dev/null +++ b/benchmarks/benchmark-plotters/plot-perf-peakthroughput-cpu-bar.py @@ -0,0 +1,110 @@ +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 in GiB/s" + +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["combined"] + run_idx = 0 + for t in time: + size = file_data["size"] + tp = calc_throughput(size, t) + data.append({ runid : run_idx, x_label : dst_node, y_label : tp}) + 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}-cpu-throughput-selectbarplot.pdf"), 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}-cpu-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) \ No newline at end of file