hpc-lab-code/work

MPI+OpenMP Hybrid Parallel Matrix Multiplication Experiments

Overview

This document summarizes the experimental analysis of MPI+OpenMP hybrid parallel matrix multiplication performance.

Generated Files

Analysis Scripts

• analyze_mpi_openmp.py - Python script for data analysis and visualization

Figures (All labels in English)

1. experiment1_analysis.png - Experiment 1: Varying MPI Processes (OpenMP threads=1)

   • Execution Time vs MPI Processes
   • Speedup vs MPI Processes
   • Parallel Efficiency vs MPI Processes
   • Parallel Efficiency Heatmap

2. experiment2_analysis.png - Experiment 2: Varying Both MPI and OpenMP

   • Efficiency Comparison (Total Processes=16)
   • Best Configuration Efficiency vs Matrix Size
   • MPI Process Impact on Efficiency
   • Speedup Comparison for Different Configurations

3. experiment3_analysis.png - Experiment 3: Optimization Results

   • Execution Time Comparison (Before/After)
   • Efficiency Comparison (Before/After)
   • Optimization Effect for Different Matrix Sizes
   • Best Configuration Efficiency Comparison

Data Files

• experiment_results.csv - Complete experimental data
• serial_results.csv - Serial baseline performance

Reports (in Chinese)

• MPI_OpenMP实验分析报告.md - Detailed analysis report
• 实验总结.md - Summary of key findings

Key Findings

Experiment 1: MPI Process Scaling

• Optimal configuration: 6 MPI processes
• Efficiency: 75%-89% for 1-6 processes
• Performance bottleneck: Communication overhead increases significantly beyond 6 processes

Experiment 2: MPI+OpenMP Configuration

• Optimal configuration: 4×4 (4 MPI processes × 4 OpenMP threads)
• Superlinear speedup: Achieved for large matrices (4096×4096) with 107% efficiency
• Key insight: Balance between node-level (MPI) and node-internal (OpenMP) parallelism is crucial

Experiment 3: Optimization Results

• Performance improvement: 1.1-2.3x speedup
• Optimization techniques:
  • Loop tiling (64×64 blocks)
  • Loop unrolling
  • Memory access optimization
• Best result: 4×4 configuration achieves 107% efficiency for 4096×4096 matrix

Recommendations

Configuration Selection

• Small matrices (<1024): 2×2 or 4×2 configuration
• Medium matrices (1024-2048): 4×4 configuration
• Large matrices (>2048): 4×4 or 8×2 configuration

Avoid

• 1×N configurations (too few MPI processes)
• N×1 configurations (too few OpenMP threads)
• Excessive total processes (>48)

Running the Analysis

cd /home/yly/dev/hpc-lab-code/work
python3 analyze_mpi_openmp.py

Requirements

• Python 3.x
• pandas
• matplotlib
• numpy

Notes

• All figures have been regenerated with English labels
• Font: DejaVu Sans (supports all characters)
• Resolution: 300 DPI for publication quality