281 lines
9.8 KiB
Python
Executable File
281 lines
9.8 KiB
Python
Executable File
#!/usr/bin/env python3
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"""
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MPI-OpenMP矩阵乘法实验数据分析脚本
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用于读取实验数据并生成性能分析图表
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"""
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import pandas as pd
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import matplotlib.pyplot as plt
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import numpy as np
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import seaborn as sns
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# 设置中文字体和样式
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plt.rcParams['font.sans-serif'] = ['SimHei', 'DejaVu Sans']
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plt.rcParams['axes.unicode_minus'] = False
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sns.set_style("whitegrid")
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def load_data(filename='experiment_results.csv'):
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"""加载实验数据"""
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df = pd.read_csv(filename)
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return df
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def load_serial_data(filename='serial_results.csv'):
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"""加载串行基准数据"""
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df = pd.read_csv(filename)
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return df
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def plot_experiment1(df):
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"""绘制实验一：MPI进程数扩展性"""
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exp1 = df[df['Experiment'] == 'Exp1'].copy()
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if exp1.empty:
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print("警告：没有找到实验一的数据")
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return
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fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 6))
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# 绘制加速比
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for size in exp1['M'].unique():
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data = exp1[exp1['M'] == size].sort_values('MPI_Processes')
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ax1.plot(data['MPI_Processes'], data['Speedup'],
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marker='o', label=f'{size}×{size}', linewidth=2)
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ax1.set_xlabel('MPI进程数', fontsize=12)
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ax1.set_ylabel('加速比', fontsize=12)
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ax1.set_title('实验一：MPI进程数扩展性（OpenMP=1）', fontsize=14)
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ax1.legend(fontsize=10)
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ax1.grid(True, alpha=0.3)
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ax1.plot([1, 16], [1, 16], 'k--', alpha=0.3, label='理想线性加速比')
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# 绘制效率
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for size in exp1['M'].unique():
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data = exp1[exp1['M'] == size].sort_values('MPI_Processes')
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ax2.plot(data['MPI_Processes'], data['Efficiency'] * 100,
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marker='s', label=f'{size}×{size}', linewidth=2)
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ax2.set_xlabel('MPI进程数', fontsize=12)
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ax2.set_ylabel('效率 (%)', fontsize=12)
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ax2.set_title('实验一：并行效率', fontsize=14)
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ax2.legend(fontsize=10)
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ax2.grid(True, alpha=0.3)
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ax2.axhline(y=100, color='k', linestyle='--', alpha=0.3, label='理想效率100%')
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plt.tight_layout()
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plt.savefig('exp1_mpi_scaling.png', dpi=300, bbox_inches='tight')
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print("已保存: exp1_mpi_scaling.png")
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plt.close()
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def plot_experiment2(df):
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"""绘制实验二：MPI-OpenMP混合并行扩展性"""
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exp2 = df[df['Experiment'] == 'Exp2'].copy()
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if exp2.empty:
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print("警告：没有找到实验二的数据")
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return
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exp2['Total_Processors'] = exp2['MPI_Processes'] * exp2['OpenMP_Threads']
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fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 6))
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# 绘制加速比（按OpenMP线程数分组）
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for nthreads in exp2['OpenMP_Threads'].unique():
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data = exp2[exp2['OpenMP_Threads'] == nthreads].copy()
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# 对相同总处理器数的数据取平均
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avg_data = data.groupby('Total_Processors').agg({
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'Speedup': 'mean',
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'Efficiency': 'mean'
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}).reset_index()
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ax1.plot(avg_data['Total_Processors'], avg_data['Speedup'],
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marker='o', label=f'OpenMP={nthreads}', linewidth=2)
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ax1.set_xlabel('总处理器数', fontsize=12)
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ax1.set_ylabel('加速比', fontsize=12)
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ax1.set_title('实验二：混合并行扩展性', fontsize=14)
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ax1.legend(fontsize=10)
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ax1.grid(True, alpha=0.3)
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# 绘制效率
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for nthreads in exp2['OpenMP_Threads'].unique():
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data = exp2[exp2['OpenMP_Threads'] == nthreads].copy()
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avg_data = data.groupby('Total_Processors').agg({
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'Speedup': 'mean',
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'Efficiency': 'mean'
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}).reset_index()
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ax2.plot(avg_data['Total_Processors'], avg_data['Efficiency'] * 100,
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marker='s', label=f'OpenMP={nthreads}', linewidth=2)
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ax2.set_xlabel('总处理器数', fontsize=12)
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ax2.set_ylabel('效率 (%)', fontsize=12)
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ax2.set_title('实验二：并行效率', fontsize=14)
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ax2.legend(fontsize=10)
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ax2.grid(True, alpha=0.3)
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ax2.axhline(y=100, color='k', linestyle='--', alpha=0.3)
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plt.tight_layout()
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plt.savefig('exp2_hybrid_scaling.png', dpi=300, bbox_inches='tight')
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print("已保存: exp2_hybrid_scaling.png")
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plt.close()
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def plot_experiment3(df):
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"""绘制实验三：MPI/OpenMP组合优化"""
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exp3 = df[df['Experiment'] == 'Exp3'].copy()
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if exp3.empty:
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print("警告：没有找到实验三的数据")
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return
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exp3['Total_Processors'] = exp3['MPI_Processes'] * exp3['OpenMP_Threads']
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fig, ax = plt.subplots(figsize=(12, 6))
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# 绘制效率热图
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for size in exp3['M'].unique():
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data = exp3[exp3['M'] == size]
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ax.plot(data['MPI_Processes'], data['Efficiency'] * 100,
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marker='o', label=f'{size}×{size}', linewidth=2, markersize=8)
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ax.set_xlabel('MPI进程数', fontsize=12)
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ax.set_ylabel('效率 (%)', fontsize=12)
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ax.set_title('实验三：不同MPI/OpenMP组合的效率（总处理器数=16）', fontsize=14)
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ax.legend(fontsize=10)
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ax.grid(True, alpha=0.3)
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ax.axhline(y=100, color='k', linestyle='--', alpha=0.3)
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# 添加x轴标签显示OpenMP线程数
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ax2 = ax.twiny()
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ax2.set_xlim(ax.get_xlim())
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ax2.set_xlabel('OpenMP线程数', fontsize=12)
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ax2.set_xticks([1, 2, 4, 8, 16])
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ax2.set_xticklabels([16, 8, 4, 2, 1])
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plt.tight_layout()
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plt.savefig('exp3_mpi_openmp_combo.png', dpi=300, bbox_inches='tight')
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print("已保存: exp3_mpi_openmp_combo.png")
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plt.close()
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def plot_efficiency_heatmap(df):
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"""绘制效率热图"""
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exp2 = df[df['Experiment'] == 'Exp2'].copy()
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if exp2.empty:
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print("警告：没有找到实验二的数据")
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return
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# 选择一个中等规模的矩阵尺寸
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sizes = sorted(exp2['M'].unique())
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if len(sizes) > 2:
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target_size = sizes[len(sizes)//2]
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else:
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target_size = sizes[0] if sizes else 1024
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data = exp2[exp2['M'] == target_size].copy()
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if data.empty:
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print("警告：没有足够的数据绘制热图")
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return
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# 创建数据透视表
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pivot_data = data.pivot_table(
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values='Efficiency',
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index='MPI_Processes',
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columns='OpenMP_Threads',
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aggfunc='mean'
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) * 100
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fig, ax = plt.subplots(figsize=(10, 8))
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sns.heatmap(pivot_data, annot=True, fmt='.1f', cmap='YlOrRd',
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cbar_kws={'label': '效率 (%)'}, ax=ax)
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ax.set_title(f'并行效率热图（矩阵尺寸: {target_size}×{target_size}）', fontsize=14)
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ax.set_xlabel('OpenMP线程数', fontsize=12)
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ax.set_ylabel('MPI进程数', fontsize=12)
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plt.tight_layout()
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plt.savefig('efficiency_heatmap.png', dpi=300, bbox_inches='tight')
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print("已保存: efficiency_heatmap.png")
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plt.close()
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def print_summary(df):
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"""打印实验结果摘要"""
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print("\n" + "="*80)
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print("实验结果摘要")
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print("="*80)
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# 实验一摘要
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exp1 = df[df['Experiment'] == 'Exp1']
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if not exp1.empty:
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print("\n实验一：MPI进程数扩展性（OpenMP=1）")
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print("-" * 80)
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for size in sorted(exp1['M'].unique()):
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data = exp1[exp1['M'] == size]
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max_speedup = data['Speedup'].max()
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max_eff = data['Efficiency'].max()
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best_np = data.loc[data['Speedup'].idxmax(), 'MPI_Processes']
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print(f"矩阵 {size}×{size}: 最大加速比={max_speedup:.2f} (NP={best_np}), "
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f"最高效率={max_eff*100:.1f}%")
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# 实验二摘要
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exp2 = df[df['Experiment'] == 'Exp2']
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if not exp2.empty:
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exp2['Total_Processors'] = exp2['MPI_Processes'] * exp2['OpenMP_Threads']
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print("\n实验二：混合并行扩展性")
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print("-" * 80)
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for nthreads in sorted(exp2['OpenMP_Threads'].unique()):
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data = exp2[exp2['OpenMP_Threads'] == nthreads]
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max_speedup = data['Speedup'].max()
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max_eff = data['Efficiency'].max()
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best_total = data.loc[data['Speedup'].idxmax(), 'Total_Processors']
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print(f"OpenMP={nthreads}: 最大加速比={max_speedup:.2f} "
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f"(总处理器={best_total}), 最高效率={max_eff*100:.1f}%")
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# 实验三摘要
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exp3 = df[df['Experiment'] == 'Exp3']
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if not exp3.empty:
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print("\n实验三：MPI/OpenMP组合优化（总处理器=16）")
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print("-" * 80)
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for size in sorted(exp3['M'].unique()):
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data = exp3[exp3['M'] == size]
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max_eff = data['Efficiency'].max()
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best_config = data.loc[data['Efficiency'].idxmax()]
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print(f"矩阵 {size}×{size}: 最高效率={max_eff*100:.1f}% "
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f"(MPI={best_config['MPI_Processes']}, "
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f"OpenMP={best_config['OpenMP_Threads']})")
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print("\n" + "="*80)
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def main():
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"""主函数"""
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import sys
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filename = sys.argv[1] if len(sys.argv) > 1 else 'experiment_results.csv'
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print(f"加载数据文件: {filename}")
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try:
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df = load_data(filename)
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print(f"数据加载成功，共 {len(df)} 条记录")
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except FileNotFoundError:
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print(f"错误：找不到文件 {filename}")
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print("请先运行 ./run_experiments.sh 生成实验数据")
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return
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# 打印摘要
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print_summary(df)
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# 生成图表
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print("\n生成性能分析图表...")
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plot_experiment1(df)
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plot_experiment2(df)
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plot_experiment3(df)
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plot_efficiency_heatmap(df)
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print("\n所有图表已生成完成！")
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print("\n建议：")
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print("1. 查看 exp1_mpi_scaling.png 了解MPI扩展性")
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print("2. 查看 exp2_hybrid_scaling.png 了解混合并行性能")
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print("3. 查看 exp3_mpi_openmp_combo.png 了解MPI/OpenMP组合优化")
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print("4. 查看 efficiency_heatmap.png 了解不同配置的效率分布")
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if __name__ == '__main__':
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main()
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