finish adapting plotters to new result style, add division by thread count to the throughput plotters, adjust figure sizes to be small (larger font when scaled up in latex)

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"]
@@ -40,3 +40,8 @@ def load_time_mesurements(file_path) -> List[float64]:
                     os.abort()
 
         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"]

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)
+def get_timing(file_path,thread_count):
+    divisor = 0
 
-        count = data["count"]
-        iterations = data["list"][0]["task"]["iterations"]
+    if thread_count == "1t": divisor = 1
+    elif thread_count == "2t" : divisor = 2
+    elif thread_count == "12t" : divisor = 12
 
-        # 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
-
-        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
 
-    try:
-        time = load_time_mesurements(file_path)["combined"]
+    timing = get_timing(file_path, thread_count)
     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)})
+    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
-    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()
 
 

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
 
 
 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)

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
 
 
 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")
+    dall = main("allnodes", title_allnodes, "-cpu")
+    dbrt = main("brute", title_allnodes, "-cpu")

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
 
 
 # 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__":