Added data,plots and plotting scripts

results-and-plotting/python/scripts/plot_all.py

@@ -0,0 +1,69 @@
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+import seaborn as sns
+from scipy.optimize import curve_fit
+import matplotlib.cm as cm
+
+
+def max_transfer_size(msg_size, np_procs, benchmark_type):
+    if benchmark_type == 'Allgather':
+        return (np_procs-72)*msg_size
+    elif benchmark_type == 'Scatter':
+        return (np_procs-72)*msg_size  # ?
+    elif benchmark_type == 'Alltoall':
+        return 72*(np_procs-72)*msg_size
+    elif benchmark_type == 'Bcast':
+        return msg_size
+    elif benchmark_type == 'Gather':
+        return (np_procs)*msg_size  # ?
+    elif benchmark_type == 'Reduce_scatter':
+        return 0.25*(np_procs-72)*(1/72)*msg_size  # ?
+    elif benchmark_type == 'Allreduce':
+        return 0.25*(np_procs-72)*(1/72)*msg_size
+    elif benchmark_type == 'Reduce':
+        return 0.25*(np_procs-72)*(1/72)*msg_size
+
+
+data_file = "data/data-multi-defand100cflag.csv"
+df_multinode = pd.read_csv(data_file, delimiter=',')
+df_multinode_offdef = df_multinode[df_multinode['off_cache_flag'] == 100]
+benchmarks = df_multinode_offdef['benchmark_type'].unique().tolist()
+benchmarks = [x for x in benchmarks if x[-1] != 'v']
+print(benchmarks)
+df_multinode_offdef = df_multinode_offdef[df_multinode_offdef['benchmark_type'].isin(
+    benchmarks)][df_multinode_offdef['msg_size_bytes'] > 1000]
+
+df_multinode_offdef["max_transfer"] = df_multinode_offdef.apply(
+    lambda row: max_transfer_size(
+        msg_size=row["msg_size_bytes"],
+        np_procs=row["proc_num"],
+        benchmark_type=row["benchmark_type"]
+    ),
+    axis=1
+)
+
+df_multinode_offdef["bytes/usec"] = df_multinode_offdef["max_transfer"] / \
+    df_multinode_offdef["t_avg_usec"]
+df_multinode_offdef = df_multinode_offdef[df_multinode_offdef['benchmark_type']!='Allgather'][df_multinode_offdef['benchmark_type']!='Alltoall']
+df_multinode_offdef = df_multinode_offdef[['benchmark_type','msg_size_bytes','t_avg_usec','proc_num']]
+
+plt.figure(figsize=(16, 9))
+sns.barplot(
+    data=df_multinode_offdef,
+    x="benchmark_type",
+    y="t_avg_usec",
+    dodge=True,
+    hue=df_multinode_offdef["msg_size_bytes"].astype(str),
+)
+# plt.yscale("log")
+plt.title("Average Time (usec) per Benchmark Type and Message Size")
+plt.ylabel("Average Time (usec)")
+plt.xlabel("Benchmark Type")
+plt.xticks(rotation=45)
+plt.legend(title="Message Size (bytes)")
+plt.tight_layout()
+# plt.show()
+plt.savefig("./plots/benchmark_avg_time_barplot.png", dpi=300)
+plt.close()
+