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prosenjitdhole merged 1 commit into from
Dec 12, 2025
Merged

RCCL Test intregration : changes for creating final plots #46

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146 changes: 67 additions & 79 deletions scripts/tracelens_single_config/create_final_plots.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2,7 +2,7 @@
import matplotlib.pyplot as plt
from pathlib import Path
import seaborn as sns

import numpy as np

def plot_improvement_chart(df, output_path):
fig, ax = plt.subplots(figsize=(10, 6))
Expand Down Expand Up @@ -33,26 +33,28 @@ def plot_improvement_chart(df, output_path):
plt.close()


def plot_abs_time_comparison(df, output_path):
def plot_abs_time_comparison(df, output_path, labels):

fig, ax = plt.subplots(figsize=(10, 6))

# Set up bar positions
x = range(len(df))
x = np.arange(len(df))
width = 0.35

colors = ["#3498db", "#e67e22"]
values = []
for label in labels:
values.append(df[label])
# Create bars for Baseline and Test
bars1 = ax.bar(
[i - width / 2 for i in x],
df["Baseline"],
width,
label="Baseline",
color="#3498db",
)
bars2 = ax.bar(
[i + width / 2 for i in x], df["Test"], width, label="Test", color="#e67e22"
)

for i,val in enumerate(values):
offset = (i - len(labels) / 2 + 0.5) * width
bars = ax.bar(
x + offset,
val,
width,
label=labels[i],
color=colors[i],
)
# Add horizontal grid lines only
ax.xaxis.grid(True, linestyle="--", alpha=0.7, color="gray")
ax.set_axisbelow(True)
Expand All @@ -78,41 +80,31 @@ def plot_abs_time_comparison(df, output_path):
plt.close()


def create_summary_charts(excel_path, output_path):
def create_summary_charts(excel_path, output_path, labels):

# Read the Summary_Dashboard sheet
df = pd.read_excel(excel_path, sheet_name="Summary_Dashboard")

plot_improvement_chart(df, output_path)
plot_abs_time_comparison(df, output_path)
plot_abs_time_comparison(df, output_path, labels)
# Create the horizontal bar chart


def plot_gpu_type_by_rank(total_time_df, output_path, title):
def plot_gpu_type_by_rank(total_time_df, output_path, title, labels):
# Create the line plot
fig, ax = plt.subplots(figsize=(12, 6))

# Plot baseline total_time by rank
ax.plot(
total_time_df["rank"],
total_time_df["baseline_time_ms"],
marker="o",
linewidth=2,
markersize=8,
color="#3498db",
label="Baseline",
)

# Plot Test (test) total_time by rank
ax.plot(
total_time_df["rank"],
total_time_df["test_time_ms"],
marker="s",
linewidth=2,
markersize=8,
color="#e67e22",
label="Test",
)
colors = ["#3498db", "#e67e22"]
markers = ["o", "s"]
for i, label in enumerate(labels):
ax.plot(
total_time_df["rank"],
total_time_df[label + "_time_ms"],
marker=markers[i],
linewidth=2,
markersize=8,
color=colors[i],
label=label,
)

# Add horizontal grid lines only
ax.yaxis.grid(True, linestyle="--", alpha=0.7, color="gray")
Expand All @@ -129,14 +121,14 @@ def plot_gpu_type_by_rank(total_time_df, output_path, title):
plt.close()


def create_gpu_time_accross_all_ranks(excel_path, output_path):
def create_gpu_time_accross_all_ranks(excel_path, output_path, labels):
# Read the GPU_ByRank_Cmp sheet
df = pd.read_excel(excel_path, sheet_name="GPU_ByRank_Cmp")

# Filter for total_time rows only
for type in ["total_time", "computation_time", "total_comm_time", "idle_time"]:
total_time_df = df[df["type"] == type]
plot_gpu_type_by_rank(total_time_df, output_path / f"{type}_by_rank.png", type)
plot_gpu_type_by_rank(total_time_df, output_path / f"{type}_by_rank.png", type, labels)


def plot_gpu_time_change_percentage_summaryby_rank(df, ax):
Expand Down Expand Up @@ -178,60 +170,50 @@ def create_gpu_time_change_percentage_summaryby_rank(excel_path, output_path):
plt.close()


def create_nccl_charts(excel_path, output_path):
def create_nccl_charts(excel_path, output_path, labels):
# Read the NCCL_Charst sheet
df = pd.read_excel(excel_path, sheet_name="NCCL_ImplSync_Cmp")
df = pd.read_excel(excel_path, sheet_name="NCCL_ImplicitSyncCmp")
df["label"] = df["Collective name"] + "\n" + df["In msg nelems"].astype(str)
x = range(len(df))

x = np.arange(len(df))
colors = ["#3498db", "#e67e22"]
plot_item = {
"NCCL Communication Latency": {
"x_label": "Collective Operation (Message Size)",
"y_label": "Communication Latency (ms)",
"y_col_names": ["baseline_comm_latency_mean", "test_comm_latency_mean"],
"y_col": "comm_latency_mean"
},
"NCCL Algorithm Bandwidth": {
"x_label": "Collective Operation (Message Size)",
"y_label": "Algorithm Bandwidth (GB/s)",
"y_col_names": [
"baseline_algo bw (GB/s)_mean",
"test_algo bw (GB/s)_mean",
],
"y_col": "algo bw (GB/s)_mean"
},
"NCCL Bus Bandwidth": {
"x_label": "Collective Operation (Message Size)",
"y_label": "Bus Bandwidth (GB/s)",
"y_col_names": [
"baseline_bus bw (GB/s)_mean",
"test_bus bw (GB/s)_mean",
],
"y_col": "bus bw (GB/s)_mean"
},
"NCCL Total Communication Latency": {
"x_label": "Collective Operation (Message Size)",
"y_label": "Total Communication Latency (ms)",
"y_col_names": [
"baseline_Total comm latency (ms)",
"test_Total comm latency (ms)",
],
"y_col": "Total comm latency (ms)"
},
}
for item in plot_item.keys():
fig, ax = plt.subplots(figsize=(14, 6))
width = 0.35
bars1 = ax.bar(
[i - width / 2 for i in x],
df[plot_item[item]["y_col_names"][0]],
width,
label="Baseline",
color="#3498db",
)
bars2 = ax.bar(
[i + width / 2 for i in x],
df[plot_item[item]["y_col_names"][1]],
width,
label="Test",
color="#e67e22",
)
for i, label in enumerate(labels):
col_name = f"{label}_{plot_item[item]['y_col']}"
print(f"col_name: {col_name} {df.columns}")
if(col_name in df.columns):
values = df[col_name].values
offset = (i - len(labels) / 2 + 0.5) * width
bar = ax.bar(
x + offset,
values,
width,
label=label,
color=colors[i],
)
ax.yaxis.grid(True, linestyle="--", alpha=0.7, color="gray")
ax.set_axisbelow(True)
ax.set_xticks(x)
Expand Down Expand Up @@ -309,6 +291,11 @@ def create_gpu_time_heatmap(excel_path, output_path):
plt.savefig(output_path / "gpu_time_heatmap.png", dpi=150)
plt.show()

def get_labels(excel_path):
# Read the Summary_Dashboard sheet
df = pd.read_excel(excel_path, sheet_name="Summary_Dashboard")
cols = df.columns.tolist()
return [cols[1], cols[2]] # for now just for two, later will be generalized for more than two

def main():
import argparse
Expand All @@ -317,7 +304,7 @@ def main():
description="Generate improvement chart from generated reports"
)
parser.add_argument(
"--report-path",
"--input",
type=Path,
default="~/aorta/aorta_single_config/aorta/expt_compare/final_analysis_report.xlsx",
help="Path to the input Excel file (should have Summary_Dashboard sheet)",
Expand All @@ -330,17 +317,18 @@ def main():
)

args = parser.parse_args()
output_path = args.output if args.output else args.report_path.parent / "plots"
output_path = args.output if args.output else args.input.parent / "plots"
output_path.mkdir(exist_ok=True, parents=True)
create_summary_charts(args.report_path, output_path)
labels = get_labels(args.input)
create_summary_charts(args.input, output_path, labels)
print(f"Summary charts saved to: {args.output}")
create_gpu_time_heatmap(args.report_path, output_path)
create_gpu_time_heatmap(args.input, output_path)
print(f"GPU time heatmap saved to: {output_path}")
create_gpu_time_accross_all_ranks(args.report_path, output_path)
create_gpu_time_accross_all_ranks(args.input, output_path, labels)
print(f"GPU time across all runs saved to: {output_path}")
create_gpu_time_change_percentage_summaryby_rank(args.report_path, output_path)
create_gpu_time_change_percentage_summaryby_rank(args.input, output_path)
print(f"GPU time change percentage summary by rank saved to: {output_path}")
create_nccl_charts(args.report_path, output_path)
create_nccl_charts(args.input, output_path, labels)
print(f"NCCL communication charts saved to: {output_path}")


Expand Down