Matrix Multiplication Optimization Project

A compact yet powerful demonstration of matrix multiplication optimizations using cache blocking, memory alignment, loop unrolling, and multi-threading (OpenMP).

Highlights

• Naive vs. Optimized
  Compare a simple triple-nested loop (matmul_naive.c) against optimized approaches (cache-blocked, aligned, unrolled).

• Multi-threading
  All methods support OpenMP for parallel execution and improved CPU utilization.

• Analysis
  Profiling with Intel VTune plus custom scripts yields metrics on:

  • Execution Time
  • Speedup
  • L1/LLC Cache Miss Rates
  • CPU Utilization

Directory Overview

• src/

  • Core implementations (matmul_naive.c, matmul_blocked.c, etc.)
  • test_matmul.c for validation and performance checks

• logs/

  • Recorded performance data (cache miss rates, CPU usage)

• graphs/

  • Plots illustrating key performance metrics (shown below)

• scripts/

  • Automation and visualization scripts (e.g., cache_analysis_draw.py, compare_threading.py)

• report/

  • Methodology, results, and conclusions in a concise PDF/Markdown document

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Detailed Graphs

Below are all of the generated PNGs, separated by metric and matrix size.

1) CPU Utilization

Matrix Size = 1024

Matrix Size = 2048

Matrix Size = 4096

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2) Execution Time

Matrix Size = 1024

Matrix Size = 2048

Matrix Size = 4096

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3) L1-dcache Miss Percentage

Matrix Size = 1024

Matrix Size = 2048

Matrix Size = 4096

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4) LLC-load Miss Percentage

Matrix Size = 1024

Matrix Size = 2048

Matrix Size = 4096

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5) Speedup

Matrix Size = 1024

Matrix Size = 2048

Matrix Size = 4096

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Conclusion

By integrating cache blocking, memory alignment, loop unrolling, and multi-threading, we significantly reduce cache misses and boost CPU utilization. Check out the logs for raw data, graphs for visual insights, and the report folder for a comprehensive discussion of these results.