fix bugs that were introduced by changes to the plotter scripts

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

@@ -7,13 +7,13 @@ import matplotlib.pyplot as plt
 
 runid = "Run ID"
 x_label = "Thread Count"
-y_label = "Throughput in GiB/s LogScale"
+y_label = "Throughput in GiB/s"
 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 = ["1mib-1e", "1mib-4e", "1gib-1e", "1gib-4e"]
 engine_counts_nice = ["1 E/WQ and Tasksize 1 MiB", "4 E/WQ and Tasksize 1 MiB", "1 E/WQ and Tasksize 1 GiB", "4 E/WQ and Tasksize 1 GiB"]
-title = "Per-Thread Throughput - 120 Copy Operations split on Threads Intra-Node on DDR with Size 1 MiB"
+title = "Per-Thread Throughput - 120 Copy Operations split on Threads Intra-Node on DDR"
 
 index = [runid, x_label, var_label]
 data = []
@@ -42,7 +42,7 @@ def load_time_mesurements(file_path):
         # therefore we divide the result by 120/n_threads to get the per-element speed
 
         return {
-            "total" : sum([x / (iterations * (120 / count)) for x in list(chain(*[data["list"][i]["report"]["time"]["total"] for i in range(count)]))]),
+            "total" : sum([x / (iterations * 120) for x in list(chain([data["list"][i]["report"]["time"]["total"] for i in range(count)]))]),
             "combined" : [x / (120 / count) for x in list(chain(*[data["list"][i]["report"]["time"]["combined"] for i in range(count)]))],
             "submission" : [x / (120 / count) for x in list(chain(*[data["list"][i]["report"]["time"]["submission"] for i in range(count)]))],
             "completion" : [x / (120 / count) for x in list(chain(*[data["list"][i]["report"]["time"]["completion"] for i in range(count)]))]
@@ -61,7 +61,7 @@ def process_file_to_dataset(file_path, engine_label, thread_count):
         data_size = 1024*1024
 
     try:
-        time = load_time_mesurements(file_path)["total"]
+        time = [load_time_mesurements(file_path)["total"]]
         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)})

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

@@ -12,7 +12,7 @@ var_label = "Configuration"
 types = ["intersock-n0ton4-1mib", "internode-n0ton1-1mib", "intersock-n0ton4-1gib", "internode-n0ton1-1gib"]
 types_nice = ["Inter-Socket Copy 1MiB", "Inter-Node Copy 1MiB", "Inter-Socket Copy 1GiB", "Inter-Node Copy 1GiB"]
 copy_methods = ["dstcopy", "srccopy", "xcopy", "srcoutsidercopy", "dstoutsidercopy", "sockoutsidercopy", "nodeoutsidercopy"]
-copy_methods_nice = [ "Engine on DST-Node", "Engine on SRC-Node", "Cross-Copy / Both Engines", "Engine on SRC-Socket, not SRC-Node", "Engine on DST-Socket, not DST-Node", "Engine on different Socket", "Engine on same Socket but neither SRC nor DST Node"]
+copy_methods_nice = [ "Engine on DST-Node", "Engine on SRC-Node", "Cross-Copy / Both Engines", "Engine on SRC-Socket, not SRC-Node", "Engine on DST-Socket, not DST-Node", "Engine on different Socket", "Engine on same Socket"]
 title = "Performance of Engine Location - Copy Operation on DDR with 1 Engine per WQ"
 
 index = [runid, x_label, var_label]
@@ -45,14 +45,19 @@ def load_time_mesurements(file_path,method_label):
             time1 = data["list"][1]["report"]["time"]
 
             return {
-                "total": max(time0["total"],time1["total"]),
+                "total": max(time0["total"],time1["total"]) / iterations,
                 "combined" : [max(x,y) for x,y in zip(time0["combined"], time1["combined"])],
                 "submission" : [max(x,y) for x,y in zip(time0["completion"], time1["completion"])],
-                "completion" : [max(x,y) for x,y in zip(time0["submission"], time1["submission"])]
+                "submission" : [max(x,y) for x,y in zip(time0["completion"], time1["completion"])],
             }
 
         else:
-            return data["list"][0]["report"]["time"]
+            return {
+                "total": data["list"][0]["report"]["time"]["total"] / iterations,
+                "combined": data["list"][0]["report"]["time"]["combined"],
+                "submission": data["list"][0]["report"]["time"]["submission"],
+                "completion": data["list"][0]["report"]["time"]["completion"]
+            }
 
 def create_copy_dataset(file_path, method_label, type_label):
     method_index = index_from_element(method_label,copy_methods)
@@ -61,14 +66,14 @@ def create_copy_dataset(file_path, method_label, type_label):
     type_nice = types_nice[type_index]
     data_size = 0
 
-    if type_label in ["internode-n0ton1-1mib", "intersock-n0ton4-1mib"]:
-        data_size = 1024 * 1024
-    else:
-        data_size = 1024*1024*1024
+
+    if type_label in ["internode-n0ton1-1gib", "intersock-n0ton4-1gib"]: data_size = 1024*1024*1024
+    elif type_label in ["internode-n0ton1-1mib", "intersock-n0ton4-1mib"]: data_size = 1024 * 1024
+    else: data_size = 0
 
     try:
-        time = load_time_mesurements(file_path,method_label)["total"]
         run_idx = 0
+        time = [load_time_mesurements(file_path,method_label)["total"]]
         for t in time:
             data.append({ runid : run_idx, x_label: type_nice, var_label : method_nice, y_label : calc_throughput(data_size, t)})
             run_idx = run_idx + 1

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

@@ -12,6 +12,7 @@ y_label = "Source Node"
 v_label = "Throughput"
 title = "Copy Throughput for 1GiB Elements running on SRC Node"
 
+index = [ runid, x_label, y_label]
 data = []
 
 
@@ -38,10 +39,10 @@ def load_time_mesurements(file_path):
         iterations = data["list"][0]["task"]["iterations"]
 
         return {
-            "total": data["list"][0]["report"]["total"] / iterations,
-            "combined": data["list"][0]["report"]["combined"],
-            "submission": data["list"][0]["report"]["submission"],
-            "completion": data["list"][0]["report"]["completion"]
+            "total": data["list"][0]["report"]["time"]["total"] / iterations,
+            "combined": data["list"][0]["report"]["time"]["combined"],
+            "submission": data["list"][0]["report"]["time"]["submission"],
+            "completion": data["list"][0]["report"]["time"]["completion"]
         }
 
 
@@ -49,7 +50,7 @@ def process_file_to_dataset(file_path, src_node, dst_node):
     data_size = 1024*1024*1024
 
     try:
-        time = load_time_mesurements(file_path)["total"]
+        time = [load_time_mesurements(file_path)["total"]]
         run_idx = 0
         for t in time:
             data.append({ runid : run_idx, x_label : dst_node, y_label : src_node, v_label: calc_throughput(data_size, t)})
@@ -67,9 +68,10 @@ def main():
             process_file_to_dataset(file, src_node, dst_node)
 
     df = pd.DataFrame(data)
-    data_pivot = df.pivot_table(index=y_label, columns=x_label, values=v_label, aggfunc=mean_without_outliers)
+    df.set_index(index, inplace=True)
+    data_pivot = df.pivot_table(index=y_label, columns=x_label, values=v_label)
 
-    sns.heatmap(data_pivot, annot=True, palette="rocket", fmt=".0f")
+    sns.heatmap(data_pivot, annot=True, cmap="rocket_r", fmt=".0f")
 
     plt.title(title)
     plt.savefig(os.path.join(folder_path, "plot-perf-peakthroughput.png"), bbox_inches='tight')

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

@@ -47,10 +47,10 @@ def load_time_mesurements(file_path,type_label):
         else: divisor = 1
 
         return {
-            "total": data["list"][0]["report"]["total"] / (iterations * divisor),
-            "combined": [ x / divisor for x in data["list"][0]["report"]["combined"]],
-            "submission": [ x / divisor for x in data["list"][0]["report"]["submission"]],
-            "completion": [ x / divisor for x in data["list"][0]["report"]["completion"]]
+            "total": data["list"][0]["report"]["time"]["total"] / (iterations * divisor),
+            "combined": [ x / divisor for x in data["list"][0]["report"]["time"]["combined"]],
+            "submission": [ x / divisor for x in data["list"][0]["report"]["time"]["submission"]],
+            "completion": [ x / divisor for x in data["list"][0]["report"]["time"]["completion"]]
         }
 
 
@@ -69,7 +69,7 @@ def process_file_to_dataset(file_path, type_label,size_label):
     else: data_size = 0
 
     try:
-        time = load_time_mesurements(file_path,type_label)["total"]
+        time = [load_time_mesurements(file_path,type_label)["total"]]
         run_idx = 0
         for t in time:
             data.append({ runid : run_idx, x_label: type_nice, var_label : size_nice, y_label : calc_throughput(data_size, t)})