diff --git a/benchmarks/benchmark-plotters/common.py b/benchmarks/benchmark-plotters/common.py index eeb2f90..d6f55f5 100644 --- a/benchmarks/benchmark-plotters/common.py +++ b/benchmarks/benchmark-plotters/common.py @@ -23,7 +23,7 @@ def index_from_element(value,array): # loads the measurements from a given file -def load_time_mesurements(file_path) -> List[float64]: +def load_time_mesurements(file_path): with open(file_path, 'r') as file: data = json.load(file) count = data["count"] @@ -39,4 +39,9 @@ def load_time_mesurements(file_path) -> List[float64]: print("Runcount missmatch between tasks. Check the commend above, aborting for now.") os.abort() - return [ x / runcount_divisor for x in data["timings"]] \ No newline at end of file + return [ x / runcount_divisor for x in data["timings"]] + + +def get_task_count(file_path): + with open(file_path, 'r') as file: + return json.load(file)["count"] \ No newline at end of file diff --git a/benchmarks/benchmark-plotters/plot-cost-mtsubmit.py b/benchmarks/benchmark-plotters/plot-cost-mtsubmit.py index 3587d1e..d9cfe6e 100644 --- a/benchmarks/benchmark-plotters/plot-cost-mtsubmit.py +++ b/benchmarks/benchmark-plotters/plot-cost-mtsubmit.py @@ -1,11 +1,9 @@ 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, index_from_element +from common import calc_throughput, index_from_element, load_time_mesurements runid = "Run ID" x_label = "Thread Count" @@ -13,8 +11,8 @@ y_label = "Throughput in GiB/s" var_label = "Transfer Size" thread_counts = ["1t", "2t", "12t"] thread_counts_nice = ["1 Thread", "2 Threads", "12 Threads"] -engine_counts = ["1mib-1e", "1gib-1e"] -engine_counts_nice = ["1 MiB", "1 GiB"] +size_labels = ["1mib", "1gib"] +size_labels_nice = ["1 MiB", "1 GiB"] title = \ """Total Throughput showing cost of MT Submit\n @@ -34,47 +32,36 @@ 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"] - iterations = data["list"][0]["task"]["iterations"] +def get_timing(file_path,thread_count): + divisor = 0 - # work queue size is 120 which is split over all available threads - # therefore we divide the result by 120/n_threads to get the per-element speed + if thread_count == "1t": divisor = 1 + elif thread_count == "2t" : divisor = 2 + elif thread_count == "12t" : divisor = 12 - return { - "total" : sum([x / (iterations * 120) for x in list(chain([data["list"][i]["report"]["time"]["total"] for i in range(count)]))]), - "combined" : [x / 120 for x in list(chain(*[data["list"][i]["report"]["time"]["combined"] for i in range(count)]))], - "submission" : [x / 120 for x in list(chain(*[data["list"][i]["report"]["time"]["submission"] for i in range(count)]))], - "completion" : [x / 120 for x in list(chain(*[data["list"][i]["report"]["time"]["completion"] for i in range(count)]))] - } + return [ x / divisor for x in load_time_mesurements(file_path)] # 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, engine_label, thread_count): - engine_index = index_from_element(engine_label,engine_counts) - engine_nice = engine_counts_nice[engine_index] +def process_file_to_dataset(file_path, size_label, thread_count): + size_index = index_from_element(size_label,size_labels) + size_nice = size_labels_nice[size_index] threadc_index = index_from_element(thread_count, thread_counts) thread_count_nice = thread_counts_nice[threadc_index] data_size = 0 - if engine_label in ["1gib-1e", "1gib-4e"]: data_size = 1024*1024*1024 - elif engine_label in ["1mib-1e", "1mib-4e"]: data_size = 1024*1024 - else: data_size = 0 + if size_label == "1gib" : data_size = 1024*1024*1024 + elif size_label == "1mib" : data_size = 1024*1024 + + timing = get_timing(file_path, thread_count) + run_idx = 0 + for t in timing: + data.append({ runid : run_idx, x_label: thread_count_nice, var_label : size_nice, y_label : calc_throughput(data_size, t)}) + run_idx = run_idx + 1 - try: - time = load_time_mesurements(file_path)["combined"] - run_idx = 0 - for t in time: - data.append({ runid : run_idx, x_label: thread_count_nice, var_label : engine_nice, y_label : calc_throughput(data_size, t)}) - run_idx = run_idx + 1 - except FileNotFoundError: - return # loops over all possible configuration combinations and calls @@ -84,17 +71,19 @@ def main(): result_path = "benchmark-results/" output_path = "benchmark-plots/" - for engine_label in engine_counts: + for size in size_labels: for thread_count in thread_counts: - file = os.path.join(result_path, f"mtsubmit-{thread_count}-{engine_label}.json") - process_file_to_dataset(file, engine_label, thread_count) + file = os.path.join(result_path, f"mtsubmit-{thread_count}-{size}.json") + process_file_to_dataset(file, size, thread_count) df = pd.DataFrame(data) df.set_index(index, inplace=True) - sns.barplot(x=x_label, y=y_label, hue=var_label, data=df, palette="rocket", errorbar="sd") + plt.figure(figsize=(4, 4)) + plt.ylim(0, 30) + sns.barplot(x=x_label, y=y_label, hue=var_label, data=df, palette="mako", errorbar="sd") - plt.savefig(os.path.join(output_path, "plot-perf-mtsubmit.pdf"), bbox_inches='tight') + plt.savefig(os.path.join(output_path, "plot-mtsubmit.pdf"), bbox_inches='tight') plt.show() diff --git a/benchmarks/benchmark-plotters/plot-perf-peakthroughput-bar.py b/benchmarks/benchmark-plotters/plot-perf-peakthroughput-bar.py index 098825b..9cd74c7 100644 --- a/benchmarks/benchmark-plotters/plot-perf-peakthroughput-bar.py +++ b/benchmarks/benchmark-plotters/plot-perf-peakthroughput-bar.py @@ -1,11 +1,9 @@ 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, load_time_mesurements +from common import calc_throughput, load_time_mesurements, get_task_count result_path = "benchmark-results/" output_path = "benchmark-plots/" @@ -41,7 +39,8 @@ data = [] # so that they are normalized, meaning that the timings returned # are nanoseconds per element transfered def get_timing(file_path): - return load_time_mesurements(file_path) + divisor = get_task_count(file_path) + return [ x / divisor for x in load_time_mesurements(file_path)] # procceses a single file and appends the desired timings @@ -50,25 +49,23 @@ def get_timing(file_path): # configurations may not be benchmarked def process_file_to_dataset(file_path, src_node, dst_node): size = 1024*1024*1024 - try: - timing = get_timing(file_path) - run_idx = 0 - for t in timing: - 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 + + timing = get_timing(file_path) + run_idx = 0 + for t in timing: + tp = calc_throughput(size, t) + data.append({ runid : run_idx, x_label : dst_node, y_label : tp}) + run_idx = run_idx + 1 def plot_bar(table,title,node_config): - plt.figure(figsize=(8, 6)) + plt.figure(figsize=(2, 3)) - sns.barplot(x=x_label, y=y_label, data=table, palette="rocket") + sns.barplot(x=x_label, y=y_label, data=table, palette="mako", errorbar="sd") - plt.ylim(0, 100) + plt.ylim(0, 75) - plt.savefig(os.path.join(output_path, f"plot-perf-{node_config}-throughput-selectbarplot.png"), bbox_inches='tight') + plt.savefig(os.path.join(output_path, f"plot-{node_config}-throughput.pdf"), bbox_inches='tight') plt.show() @@ -78,7 +75,7 @@ def plot_bar(table,title,node_config): def main(node_config,title): src_node = 0 for dst_node in {8,11,12,15}: - file = os.path.join(result_path, f"copy-n{src_node}ton{dst_node}-1gib-{node_config}-1e.json") + file = os.path.join(result_path, f"copy-n{src_node}ton{dst_node}-1gib-{node_config}.json") process_file_to_dataset(file, src_node, dst_node) df = pd.DataFrame(data) diff --git a/benchmarks/benchmark-plotters/plot-perf-peakthroughput-cpu-bar.py b/benchmarks/benchmark-plotters/plot-perf-peakthroughput-cpu-bar.py index e4ff1bd..dec3dda 100644 --- a/benchmarks/benchmark-plotters/plot-perf-peakthroughput-cpu-bar.py +++ b/benchmarks/benchmark-plotters/plot-perf-peakthroughput-cpu-bar.py @@ -1,11 +1,9 @@ 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, load_time_mesurements +from common import calc_throughput, load_time_mesurements, get_task_count result_path = "benchmark-results/" output_path = "benchmark-plots/" @@ -41,7 +39,8 @@ data = [] # so that they are normalized, meaning that the timings returned # are nanoseconds per element transfered def get_timing(file_path): - return load_time_mesurements(file_path) + divisor = get_task_count(file_path) + return [ x / divisor for x in load_time_mesurements(file_path)] # procceses a single file and appends the desired timings @@ -50,25 +49,23 @@ def get_timing(file_path): # configurations may not be benchmarked def process_file_to_dataset(file_path, src_node, dst_node): size = 1024*1024*1024 - try: - timing = get_timing(file_path) - run_idx = 0 - for t in timing: - 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 + + timing = get_timing(file_path) + run_idx = 0 + for t in timing: + tp = calc_throughput(size, t) + data.append({ runid : run_idx, x_label : dst_node, y_label : tp}) + run_idx = run_idx + 1 def plot_bar(table,title,node_config): - plt.figure(figsize=(8, 6)) + plt.figure(figsize=(2, 3)) - sns.barplot(x=x_label, y=y_label, data=table, palette="rocket", errorbar=None) + sns.barplot(x=x_label, y=y_label, data=table, palette="mako", errorbar="sd") - plt.ylim(0, 100) + plt.ylim(0, 75) - plt.savefig(os.path.join(output_path, f"plot-perf-{node_config}-cpu-throughput-selectbarplot.pdf"), bbox_inches='tight') + plt.savefig(os.path.join(output_path, f"plot-{node_config}-cpu-throughput.pdf"), bbox_inches='tight') plt.show() @@ -78,7 +75,7 @@ def plot_bar(table,title,node_config): def main(node_config,title,ext): src_node = 0 for dst_node in {8,11,12,15}: - file = os.path.join(result_path, f"copy-n{src_node}ton{dst_node}-1gib-{node_config}-cpu-{ext}.json") + file = os.path.join(result_path, f"copy-n{src_node}ton{dst_node}-1gib-{node_config}{ext}.json") process_file_to_dataset(file, src_node, dst_node) df = pd.DataFrame(data) @@ -93,5 +90,5 @@ def main(node_config,title,ext): if __name__ == "__main__": - dall = main("allnodes", title_allnodes, "1e") - dbrt = main("allnodes", title_allnodes, "brute") \ No newline at end of file + dall = main("allnodes", title_allnodes, "-cpu") + dbrt = main("brute", title_allnodes, "-cpu") \ No newline at end of file diff --git a/benchmarks/benchmark-plotters/plot-perf-submitmethod.py b/benchmarks/benchmark-plotters/plot-perf-submitmethod.py index 05698c4..cb35b63 100644 --- a/benchmarks/benchmark-plotters/plot-perf-submitmethod.py +++ b/benchmarks/benchmark-plotters/plot-perf-submitmethod.py @@ -1,7 +1,4 @@ import os -import json -from numpy import float64, int64 -from typing import List import pandas as pd import seaborn as sns import matplotlib.pyplot as plt @@ -12,7 +9,7 @@ runid = "Run ID" x_label = "Size of Submitted Task" y_label = "Throughput in GiB/s" var_label = "Submission Type" -sizes = ["1kib", "4kib", "1mib", "128mib"] +sizes = ["1kib", "4kib", "1mib"] sizes_nice = ["1 KiB", "4 KiB", "1 MiB", "128 MiB"] types = ["bs10", "bs50", "ssaw"] types_nice = ["Batch, Size 10", "Batch, Size 50", "Single Submit"] @@ -35,7 +32,7 @@ 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 get_timing(file_path,type_label) -> List[float64]: +def get_timing(file_path,type_label): divisor = 0 if type_label == "bs10": divisor = 10 @@ -59,18 +56,13 @@ def process_file_to_dataset(file_path, type_label,size_label): if size_label == "1kib": data_size = 1024; elif size_label == "4kib": data_size = 4 * 1024; elif size_label == "1mib": data_size = 1024 * 1024; - elif size_label == "32mib": data_size = 32 * 1024 * 1024; - elif size_label == "1gib": data_size = 1024 * 1024 * 1024; - else: data_size = 0 + elif size_label == "128mib": data_size = 128 * 1024 * 1024; - try: - time = get_timing(file_path,type_label) - run_idx = 0 - for t in time: - data.append({ runid : run_idx, x_label: size_nice, var_label : type_nice, y_label : calc_throughput(data_size, t)}) - run_idx = run_idx + 1 - except FileNotFoundError: - return + time = get_timing(file_path,type_label) + run_idx = 0 + for t in time: + data.append({ runid : run_idx, x_label: size_nice, var_label : type_nice, y_label : calc_throughput(data_size, t)}) + run_idx = run_idx + 1 # loops over all possible configuration combinations and calls @@ -82,16 +74,17 @@ def main(): for type_label in types: for size in sizes: - file = os.path.join(result_path, f"submit-{type_label}-{size}-1e.json") + file = os.path.join(result_path, f"submit-{type_label}-{size}.json") process_file_to_dataset(file, type_label, size) 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") + plt.figure(figsize=(4, 4)) + sns.barplot(x=x_label, y=y_label, hue=var_label, data=df, palette="mako", errorbar="sd") - plt.savefig(os.path.join(output_path, "plot-opt-submitmethod.pdf"), bbox_inches='tight') + plt.savefig(os.path.join(output_path, "plot-submitmethod.pdf"), bbox_inches='tight') plt.show() if __name__ == "__main__":