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research/paper_plot/gpu_under.py

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+#!/usr/bin/env python3
+# -*- coding:utf-8 -*-
+# Motto: Were It to Benefit My Country, I Would Lay Down My Life!
+# \file: /gpu_utilization_plot.py
+# \brief: Plots GPU throughput vs. batch size to show utilization with equally spaced x-axis.
+# Author: AI Assistant
+
+import numpy as np
+import pandas as pd # Using pandas for data structuring, similar to example
+from matplotlib import pyplot as plt
+
+# Apply styling similar to the example script
+plt.rcParams["font.family"] = "Helvetica"
+plt.rcParams["ytick.direction"] = "in"
+plt.rcParams["xtick.direction"] = "in"
+# plt.rcParams["hatch.linewidth"] = 1.5 # Not used for line plots
+plt.rcParams["font.weight"] = "bold"
+plt.rcParams["axes.labelweight"] = "bold"
+plt.rcParams["text.usetex"] = True # Enables LaTeX for text rendering
+
+# New Benchmark data (4th set)
+data = {
+    'batch_size': [1, 4, 8, 10, 16, 20, 32, 40, 64, 128, 256,],
+    'avg_time_s': [
+        0.0031, 0.0057, 0.0100, 0.0114, 0.0186, 0.0234,
+        0.0359, 0.0422, 0.0626, 0.1259, 0.2454,
+    ],
+    'throughput_seq_s': [
+        318.10, 696.77, 798.95, 874.70, 859.58, 855.19,
+        890.80, 946.93, 1022.75, 1017.03, 1043.17,
+    ]
+}
+benchmark_df = pd.DataFrame(data)
+
+# Create the plot
+# Increased width slightly for more x-axis labels
+fig, ax = plt.subplots()
+fig.set_size_inches(8, 5)
+
+# Generate equally spaced x-coordinates (indices)
+x_indices = np.arange(len(benchmark_df))
+
+# Plotting throughput vs. batch size (using indices for x-axis)
+ax.plot(
+    x_indices, # Use equally spaced indices for plotting
+    benchmark_df['throughput_seq_s'],
+    marker='o',       # Add markers to data points
+    linestyle='-',
+    color="#63B8B6",  # A color inspired by the example's 'edgecolors'
+    linewidth=2,
+    markersize=6,
+    # label="Model Throughput" # Label for legend if needed, but not showing legend by default
+)
+
+# Setting labels for axes
+ax.set_xlabel("Batch Size", fontsize=14)
+ax.set_ylabel("Throughput (sequences/second)", fontsize=14)
+
+# Customizing Y-axis for the new data range:
+# Start Y from 0 to include the anomalous low point and show full scale.
+y_min_val = 200
+# Round up y_max_val to the nearest 100, as max throughput > 1000
+y_max_val = np.ceil(benchmark_df['throughput_seq_s'].max() / 100) * 100
+ax.set_ylim((y_min_val, y_max_val))
+# Set y-ticks every 100 units, ensuring the top tick is included.
+ax.set_yticks(np.arange(y_min_val, y_max_val + 1, 100))
+
+# Customizing X-axis for equally spaced ticks:
+# Set tick positions to the indices
+ax.set_xticks(x_indices)
+# Set tick labels to the actual batch_size values
+ax.set_xticklabels(benchmark_df['batch_size'])
+ax.tick_params(axis='x', rotation=45, labelsize=10) # Rotate X-axis labels, fontsize 10
+ax.tick_params(axis='y', labelsize=12)
+
+
+# Add a light grid for better readability, common in academic plots
+ax.grid(True, linestyle=':', linewidth=0.5, color='grey', alpha=0.7, zorder=0)
+
+# Remove title (as requested)
+# ax.set_title("GPU Throughput vs. Batch Size", fontsize=16) # Title would go here
+
+# Optional: Add a legend if you have multiple lines or want to label the single line
+# ax.legend(
+#     loc="center right", # Location might need adjustment due to data shape
+#     edgecolor="black",
+#     facecolor="white",
+#     framealpha=1.0,
+#     shadow=False,
+#     fancybox=False,
+#     prop={"weight": "bold", "size": 10}
+# ).set_zorder(100)
+
+# Adjust layout to prevent labels from being cut off
+plt.tight_layout()
+
+# Save the figure
+output_filename = "./paper_plot/figures/gpu_throughput_vs_batch_size_equispaced.pdf"
+plt.savefig(output_filename, bbox_inches="tight", dpi=300)
+print(f"Plot saved to {output_filename}")
+
+# Display the plot (optional, depending on environment)
+plt.show()
+
+# %%
+# This is just to mimic the '%%' cell structure from the example.
+# No actual code needed here for this script.