commit ae258223cace9faeac544a034f7c1a9f86285b49 Author: Carlos Gutierrez Date: Sat Dec 6 21:47:42 2025 -0500 Initial commit: Matrix alignment prototype for HPC performance demonstration - Add C implementation demonstrating memory alignment effects (matrix_alignment_prototype.c) - Include cache-blocked matrix multiplication with AVX SIMD optimizations - Add automated benchmarking framework (run_all_tests.sh, run_benchmark_sizes.sh) - Add Python visualization scripts (generate_plots.py) - Include Makefile for building with AVX support - Add benchmark results and generated plots - Add README with build and usage instructions - Configure .gitignore for C/Python project files diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..56f9f1f --- /dev/null +++ b/.gitignore @@ -0,0 +1,237 @@ +# Project-specific ignores +old/ +*.log +test.log +instructions.md +context.txt +theory_for_report.txt + +# Compiled executables +matrix_alignment_prototype +alignment_demo_prototype +hpc_alignment_demo + +# LaTeX report files (this repo focuses on the C prototype only) +HPC_Optimization_Report.tex +references.bib +*.aux +*.bbl +*.blg +*.fdb_latexmk +*.fls +*.out +*.pdf +*.synctex.gz +*.toc +*.lof +*.lot +*.idx +*.ilg +*.ind +*.auxlock + +### C++ ### +# Prerequisites +*.d + +# Compiled Object files +*.slo +*.lo +*.o +*.obj + +# Precompiled Headers +*.gch +*.pch + +# Compiled Dynamic libraries +*.so +*.dylib +*.dll + +# Fortran module files +*.mod +*.smod + +# Compiled Static libraries +*.lai +*.la +*.a +*.lib + +# Executables +*.exe +*.out +*.app + +### Python ### +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ +cover/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +.pybuilder/ +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +# For a library or package, you might want to ignore these files since the code is +# intended to run in multiple environments; otherwise, check them in: +# .python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# poetry +# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control. +# This is especially recommended for binary packages to ensure reproducibility, and is more +# commonly ignored for libraries. +# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control +#poetry.lock + +# pdm +# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control. +#pdm.lock +# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it +# in version control. +# https://pdm.fming.dev/#use-with-ide +.pdm.toml + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ + +# pytype static type analyzer +.pytype/ + +# Cython debug symbols +cython_debug/ + +# PyCharm +# JetBrains specific template is maintained in a separate JetBrains.gitignore that can +# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore +# and can be added to the global gitignore or merged into this file. For a more nuclear +# option (not recommended) you can uncomment the following to ignore the entire idea folder. +#.idea/ + +### Python Patch ### +# Poetry local configuration file - https://python-poetry.org/docs/configuration/#local-configuration +poetry.toml + +# ruff +.ruff_cache/ + +# LSP config files +pyrightconfig.json + diff --git a/Makefile b/Makefile new file mode 100644 index 0000000..a8d3336 --- /dev/null +++ b/Makefile @@ -0,0 +1,30 @@ +# Makefile for HPC Matrix Alignment Prototype +# Demonstrates memory alignment optimization impact + +CC = gcc +CFLAGS = -Wall -Wextra -O3 -march=native -std=c11 -ffast-math -mavx -mfma +LDFLAGS = -lm +TARGET = matrix_alignment_prototype +SOURCE = matrix_alignment_prototype.c + +# Default target +all: $(TARGET) + +# Build the prototype +$(TARGET): $(SOURCE) + $(CC) $(CFLAGS) -o $(TARGET) $(SOURCE) $(LDFLAGS) + +# Run the prototype +run: $(TARGET) + ./$(TARGET) + +# Clean build artifacts +clean: + rm -f $(TARGET) *.o + +# Debug build +debug: CFLAGS += -g -DDEBUG +debug: $(TARGET) + +.PHONY: all run clean debug + diff --git a/README.md b/README.md new file mode 100644 index 0000000..68f5412 --- /dev/null +++ b/README.md @@ -0,0 +1,143 @@ +# Matrix Alignment Prototype - HPC Performance Demonstration + +This repository contains a standalone C implementation demonstrating memory alignment effects on high-performance computing performance. The prototype investigates performance variability issues related to memory alignment, specifically examining patterns described in OpenBLAS Issue #3879. + +## Project Overview + +This repository focuses on the practical implementation and benchmarking framework: + +- **C Prototype**: Custom implementation demonstrating cache-blocked matrix multiplication with AVX SIMD optimizations +- **Memory Alignment Comparison**: Compares 64-byte cache-line aligned vs 16-byte aligned memory access patterns +- **Benchmarking Framework**: Automated scripts for performance testing and visualization +- **Performance Analysis**: Tools for measuring and visualizing alignment effects across different matrix sizes + +## Project Structure + +```text +. +├── matrix_alignment_prototype.c # C implementation demonstrating alignment effects +├── Makefile # Build configuration for C prototype +├── generate_plots.py # Python script for performance visualization +├── run_benchmark_sizes.sh # Automated benchmarking script +├── run_all_tests.sh # Complete test suite orchestrator +├── benchmark_results.csv # Collected performance data (generated) +├── requirements.txt # Python dependencies +└── assets/ # Generated plots and figures +``` + +## Building and Running + +### Prerequisites + +- GCC compiler with AVX support +- Python 3 with matplotlib and numpy +- LaTeX distribution (for report compilation) +- Make utility + +### Compiling the C Prototype + +```bash +make +``` + +This will compile `matrix_alignment_prototype.c` with AVX optimizations enabled. + +### Running Benchmarks + +Run the complete test suite: + +```bash +./run_all_tests.sh +``` + +Or run benchmarks for specific matrix sizes: + +```bash +./run_benchmark_sizes.sh +``` + +For CSV output: + +```bash +./matrix_alignment_prototype -s 1024 --csv +``` + +### Generating Visualizations + +After running benchmarks, generate plots: + +```bash +python3 generate_plots.py +``` + +Plots will be saved in the `assets/` directory. + +## Key Features + +### Memory Alignment Demonstration + +The prototype demonstrates: + +- **Aligned version**: Uses 64-byte cache-line aligned memory with `_mm256_load_ps` (aligned SIMD loads) +- **Misaligned version**: Uses 16-byte aligned memory with `_mm256_loadu_ps` (unaligned SIMD loads) +- **Cache-blocked algorithm**: Implements tiled matrix multiplication for optimal cache utilization +- **Performance variability analysis**: Measures and visualizes alignment effects across different matrix sizes + +### Benchmarking Framework + +The automated framework includes: + +- Multiple matrix size testing (512, 1024, 1500, 2048) +- CSV data collection for reproducibility +- Python visualization generating multiple analysis plots +- Execution time, speedup ratio, variability, and GFLOPS metrics + +## Results + +The implementation demonstrates performance variability patterns consistent with OpenBLAS Issue #3879, showing: + +- Peak variability of 6.6% at matrix size 512 +- Size-dependent performance differences +- Architecture-sensitive alignment effects +- Reduced variability on modern hardware (Zen 3) compared to older architectures (Zen 2) + +## Technical Details + +### Memory Alignment Implementation + +The prototype demonstrates two memory allocation strategies: + +- **Aligned allocation**: Uses `posix_memalign()` to allocate memory aligned to 64-byte cache-line boundaries +- **Misaligned allocation**: Simulates C++ default 16-byte alignment by offsetting pointers from cache-line boundaries + +### SIMD Optimizations + +When compiled with AVX support, the implementation uses: + +- `_mm256_load_ps()`: Aligned SIMD loads (faster, requires 32-byte alignment) +- `_mm256_loadu_ps()`: Unaligned SIMD loads (slower, works with any alignment) + +### Cache-Blocked Algorithm + +The matrix multiplication uses a tiled (cache-blocked) approach: + +- Tile size: 64x64 elements (256 bytes for floats) +- Maximizes cache line utilization +- Reduces memory bandwidth requirements +- Enables better spatial locality + +## Background + +This implementation was developed to investigate performance variability patterns described in OpenBLAS Issue #3879, which reported up to 2x performance differences depending on memory alignment. The prototype demonstrates that: + +- Performance variability is size-dependent and architecture-sensitive +- Modern CPUs (Zen 3) show reduced alignment sensitivity compared to older architectures (Zen 2) +- Proper cache-line alignment reduces performance unpredictability + +## Related Work + +This prototype is based on analysis of OpenBLAS Issue #3879, which documented performance variability in matrix multiplication operations due to memory alignment. The implementation demonstrates similar variability patterns while providing a standalone, reproducible example. + +## License + +This project is for educational and research purposes. 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Run benchmark first.") + sys.exit(1) + except Exception as e: + print(f"Error reading {filename}: {e}") + sys.exit(1) + + return sizes, aligned_times, misaligned_times, speedups + + +def create_plots(sizes, aligned_times, misaligned_times, speedups): + """Create multiple plots showing benchmark results""" + + # Set style (fallback if seaborn not available) + try: + plt.style.use("seaborn-v0_8-darkgrid") + except: + try: + plt.style.use("seaborn-darkgrid") + except: + plt.style.use("default") + fig = plt.figure(figsize=(15, 10)) + + # Plot 1: Execution Time Comparison + ax1 = plt.subplot(2, 2, 1) + ax1.plot( + sizes, + aligned_times, + "o-", + label="64-byte Aligned (Optimized)", + linewidth=2, + markersize=8, + color="#2ecc71", + ) + ax1.plot( + sizes, + misaligned_times, + "s-", + label="16-byte Aligned (Non-optimized)", + linewidth=2, + markersize=8, + color="#e74c3c", + ) + ax1.set_xlabel("Matrix Size (N x N)", fontsize=12, fontweight="bold") + ax1.set_ylabel("Execution Time (seconds)", fontsize=12, fontweight="bold") + ax1.set_title("Execution Time vs Matrix Size", fontsize=14, fontweight="bold") + ax1.legend(fontsize=10) + ax1.grid(True, alpha=0.3) + ax1.set_xscale("linear") + ax1.set_yscale("log") + + # Plot 2: Speedup Ratio + ax2 = plt.subplot(2, 2, 2) + colors = ["#e74c3c" if s < 1.0 else "#2ecc71" for s in speedups] + bars = ax2.bar( + range(len(sizes)), speedups, color=colors, alpha=0.7, edgecolor="black" + ) + ax2.axhline( + y=1.0, color="black", linestyle="--", linewidth=1.5, label="No difference" + ) + ax2.set_xlabel("Matrix Size (N x N)", fontsize=12, fontweight="bold") + ax2.set_ylabel("Speedup Ratio (Misaligned/Aligned)", fontsize=12, fontweight="bold") + ax2.set_title("Performance Ratio by Matrix Size", fontsize=14, fontweight="bold") + ax2.set_xticks(range(len(sizes))) + ax2.set_xticklabels(sizes) + ax2.legend(fontsize=10) + ax2.grid(True, alpha=0.3, axis="y") + + # Add value labels on bars + for i, (bar, speedup) in enumerate(zip(bars, speedups)): + height = bar.get_height() + ax2.text( + bar.get_x() + bar.get_width() / 2.0, + height, + f"{speedup:.2f}x", + ha="center", + va="bottom" if speedup > 1 else "top", + fontsize=9, + ) + + # Plot 3: Performance Variability (as percentage) + ax3 = plt.subplot(2, 2, 3) + variability = [(abs(1 - s) * 100) for s in speedups] + ax3.plot(sizes, variability, "o-", linewidth=2, markersize=8, color="#3498db") + ax3.fill_between(sizes, variability, alpha=0.3, color="#3498db") + ax3.set_xlabel("Matrix Size (N x N)", fontsize=12, fontweight="bold") + ax3.set_ylabel("Performance Variability (%)", fontsize=12, fontweight="bold") + ax3.set_title( + "Performance Variability (Issue #3879)", fontsize=14, fontweight="bold" + ) + ax3.grid(True, alpha=0.3) + + # Add annotations + for size, var in zip(sizes, variability): + ax3.annotate( + f"{var:.1f}%", + (size, var), + textcoords="offset points", + xytext=(0, 10), + ha="center", + fontsize=9, + ) + + # Plot 4: GFLOPS Comparison + ax4 = plt.subplot(2, 2, 4) + gflops_aligned = [(2.0 * s**3) / (t * 1e9) for s, t in zip(sizes, aligned_times)] + gflops_misaligned = [ + (2.0 * s**3) / (t * 1e9) for s, t in zip(sizes, misaligned_times) + ] + + x = np.arange(len(sizes)) + width = 0.35 + ax4.bar( + x - width / 2, + gflops_aligned, + width, + label="64-byte Aligned", + color="#2ecc71", + alpha=0.8, + edgecolor="black", + ) + ax4.bar( + x + width / 2, + gflops_misaligned, + width, + label="16-byte Aligned", + color="#e74c3c", + alpha=0.8, + edgecolor="black", + ) + ax4.set_xlabel("Matrix Size (N x N)", fontsize=12, fontweight="bold") + ax4.set_ylabel("Performance (GFLOPS)", fontsize=12, fontweight="bold") + ax4.set_title("Computational Throughput Comparison", fontsize=14, fontweight="bold") + ax4.set_xticks(x) + ax4.set_xticklabels(sizes) + ax4.legend(fontsize=10) + ax4.grid(True, alpha=0.3, axis="y") + + plt.tight_layout() + + # Save figure + output_file = "alignment_benchmark_results.png" + plt.savefig(output_file, dpi=300, bbox_inches="tight") + print(f"✓ Plot saved to: {output_file}") + + # Also save individual plots + for i, (ax, title) in enumerate( + zip( + [ax1, ax2, ax3, ax4], ["execution_time", "speedup", "variability", "gflops"] + ), + 1, + ): + fig_single = plt.figure(figsize=(8, 6)) + ax_new = fig_single.add_subplot(111) + + # Copy plot content + if i == 1: + ax_new.plot( + sizes, + aligned_times, + "o-", + label="64-byte Aligned (Optimized)", + linewidth=2, + markersize=8, + color="#2ecc71", + ) + ax_new.plot( + sizes, + misaligned_times, + "s-", + label="16-byte Aligned (Non-optimized)", + linewidth=2, + markersize=8, + color="#e74c3c", + ) + ax_new.set_yscale("log") + elif i == 2: + colors = ["#e74c3c" if s < 1.0 else "#2ecc71" for s in speedups] + bars = ax_new.bar( + range(len(sizes)), speedups, color=colors, alpha=0.7, edgecolor="black" + ) + ax_new.axhline(y=1.0, color="black", linestyle="--", linewidth=1.5) + ax_new.set_xticks(range(len(sizes))) + ax_new.set_xticklabels(sizes) + for j, (bar, speedup) in enumerate(zip(bars, speedups)): + height = bar.get_height() + ax_new.text( + bar.get_x() + bar.get_width() / 2.0, + height, + f"{speedup:.2f}x", + ha="center", + va="bottom" if speedup > 1 else "top", + fontsize=9, + ) + elif i == 3: + variability = [(abs(1 - s) * 100) for s in speedups] + ax_new.plot( + sizes, variability, "o-", linewidth=2, markersize=8, color="#3498db" + ) + ax_new.fill_between(sizes, variability, alpha=0.3, color="#3498db") + for size, var in zip(sizes, variability): + ax_new.annotate( + f"{var:.1f}%", + (size, var), + textcoords="offset points", + xytext=(0, 10), + ha="center", + fontsize=9, + ) + elif i == 4: + x = np.arange(len(sizes)) + width = 0.35 + ax_new.bar( + x - width / 2, + gflops_aligned, + width, + label="64-byte Aligned", + color="#2ecc71", + alpha=0.8, + edgecolor="black", + ) + ax_new.bar( + x + width / 2, + gflops_misaligned, + width, + label="16-byte Aligned", + color="#e74c3c", + alpha=0.8, + edgecolor="black", + ) + ax_new.set_xticks(x) + ax_new.set_xticklabels(sizes) + + ax_new.set_xlabel(ax.get_xlabel(), fontsize=12, fontweight="bold") + ax_new.set_ylabel(ax.get_ylabel(), fontsize=12, fontweight="bold") + ax_new.set_title(ax.get_title(), fontsize=14, fontweight="bold") + ax_new.legend(fontsize=10) + ax_new.grid(True, alpha=0.3) + + plt.tight_layout() + filename = f"alignment_benchmark_{title}.png" + plt.savefig(filename, dpi=300, bbox_inches="tight") + print(f"✓ Individual plot saved to: {filename}") + plt.close(fig_single) + + plt.close(fig) + print("\nAll plots generated successfully!") + + +def main(): + """Main function""" + print("=" * 50) + print("HPC Alignment Benchmark - Plot Generation") + print("=" * 50) + print() + + sizes, aligned_times, misaligned_times, speedups = read_results() + + print(f"Loaded {len(sizes)} data points") + print(f"Matrix sizes: {sizes}") + print() + + create_plots(sizes, aligned_times, misaligned_times, speedups) + + +if __name__ == "__main__": + main() diff --git a/matrix_alignment_prototype.c b/matrix_alignment_prototype.c new file mode 100644 index 0000000..13ed78c --- /dev/null +++ b/matrix_alignment_prototype.c @@ -0,0 +1,540 @@ +/** + * HPC Data Structure Optimization Prototype + * Demonstrates Memory Alignment Impact on Matrix Multiplication Performance + * + * Based on OpenBLAS Issue #3879: Performance Variability in Matrix + * Multiplication + * + * This prototype demonstrates: + * 1. Non-optimized version: 16-byte alignment (C++ default) + * 2. Optimized version: 64-byte cache-line alignment + * + * Expected Results (based on empirical study): + * - Performance variability up to 2x difference + * - Cache misses reduction with proper alignment + * - Improved SIMD vectorization efficiency + */ + +#include +#include +#include +#include +#include +#include + +#ifdef __AVX__ +#include +#define USE_SIMD 1 +#elif defined(__SSE__) +#include +#include +#define USE_SIMD 1 +#else +#define USE_SIMD 0 +#endif + +#define CACHE_LINE_SIZE 64 +#define MATRIX_SIZE 1024 // Default size, can be overridden via command line +#define BLOCK_SIZE 64 // Cache block size for tiled algorithm +#define NUM_ITERATIONS 10 +#define WARMUP_ITERATIONS 3 +#define CSV_OUTPUT 0 // Set to 1 for CSV output mode + +/** + * High-resolution timing function + */ +static inline double get_time(void) { + struct timespec ts; + clock_gettime(CLOCK_MONOTONIC, &ts); + return ts.tv_sec + ts.tv_nsec * 1e-9; +} + +/** + * Allocate memory with specific alignment + * Returns pointer aligned to 'alignment' bytes + */ +void *aligned_malloc(size_t size, size_t alignment) { + void *ptr; + if (posix_memalign(&ptr, alignment, size) != 0) { + return NULL; + } + return ptr; +} + +/** + * Allocate memory with intentional misalignment (16-byte aligned, not + * cache-line aligned) Simulates C++ default alignment behavior Uses 32-byte + * offset to ensure clear misalignment from cache lines + */ +void *misaligned_malloc(size_t size) { + // Allocate extra space to allow offsetting + void *raw = malloc(size + CACHE_LINE_SIZE + sizeof(void *)); + if (!raw) + return NULL; + + // Offset by 32 bytes to ensure 16-byte alignment but clear 64-byte + // misalignment This creates a more significant misalignment that better + // demonstrates the issue + uintptr_t addr = (uintptr_t)raw; + uintptr_t aligned = + (addr + 32) & + ~(16 - 1); // 16-byte aligned, but offset by 32 from cache line + + // Store original pointer for free() before the aligned address + *((void **)((char *)aligned - sizeof(void *))) = raw; + + return (void *)aligned; +} + +/** + * Free misaligned memory + */ +void misaligned_free(void *ptr) { + if (ptr) { + void *raw = *((void **)((char *)ptr - sizeof(void *))); + free(raw); + } +} + +/** + * SIMD-optimized matrix multiplication using AVX intrinsics + * This version uses aligned loads when data is properly aligned, + * demonstrating the performance benefit of cache-line alignment + * C = A * B + * + * Uses cache-blocked approach with SIMD for inner loops + */ +#if USE_SIMD && defined(__AVX__) +void matrix_multiply_simd_aligned(const float *restrict A, const float *restrict B, + float *restrict C, int matrix_dimension) { + // Initialize C to zero + for (int element_idx = 0; element_idx < matrix_dimension * matrix_dimension; element_idx++) { + C[element_idx] = 0.0f; + } + + const int simd_width = 8; // AVX processes 8 floats at a time + const int tile_size = 64; // Cache-friendly tile size + + // Cache-blocked matrix multiplication with SIMD + for (int tile_row_start = 0; tile_row_start < matrix_dimension; tile_row_start += tile_size) { + for (int tile_col_start = 0; tile_col_start < matrix_dimension; tile_col_start += tile_size) { + for (int tile_k_start = 0; tile_k_start < matrix_dimension; tile_k_start += tile_size) { + int tile_row_end = (tile_row_start + tile_size < matrix_dimension) ? tile_row_start + tile_size : matrix_dimension; + int tile_col_end = (tile_col_start + tile_size < matrix_dimension) ? tile_col_start + tile_size : matrix_dimension; + int tile_k_end = (tile_k_start + tile_size < matrix_dimension) ? tile_k_start + tile_size : matrix_dimension; + + for (int row_idx = tile_row_start; row_idx < tile_row_end; row_idx++) { + const float *A_row = &A[row_idx * matrix_dimension]; + for (int col_idx = tile_col_start; col_idx < tile_col_end; col_idx++) { + __m256 sum_vec = _mm256_setzero_ps(); + int k_idx = tile_k_start; + + // Process 8 elements at a time with ALIGNED loads (faster) + // When base pointer is cache-line aligned and we process in chunks, + // we can often use aligned loads + for (; k_idx + simd_width <= tile_k_end; k_idx += simd_width) { + // Check alignment - if aligned, use faster load + uintptr_t a_address = (uintptr_t)(A_row + k_idx); + if (a_address % 32 == 0) { + __m256 a_vec = _mm256_load_ps(&A_row[k_idx]); // Aligned load (faster) + // Gather B elements (column access - not ideal but demonstrates alignment) + float b_values[8] __attribute__((aligned(32))); + for (int simd_element_idx = 0; simd_element_idx < 8; simd_element_idx++) { + b_values[simd_element_idx] = B[(k_idx + simd_element_idx) * matrix_dimension + col_idx]; + } + __m256 b_vec = _mm256_load_ps(b_values); + __m256 product = _mm256_mul_ps(a_vec, b_vec); + sum_vec = _mm256_add_ps(sum_vec, product); + } else { + __m256 a_vec = _mm256_loadu_ps(&A_row[k_idx]); // Fallback to unaligned + float b_values[8]; + for (int simd_element_idx = 0; simd_element_idx < 8; simd_element_idx++) { + b_values[simd_element_idx] = B[(k_idx + simd_element_idx) * matrix_dimension + col_idx]; + } + __m256 b_vec = _mm256_loadu_ps(b_values); + __m256 product = _mm256_mul_ps(a_vec, b_vec); + sum_vec = _mm256_add_ps(sum_vec, product); + } + } + + // Horizontal sum + float sum_array[8] __attribute__((aligned(32))); + _mm256_store_ps(sum_array, sum_vec); + float sum = sum_array[0] + sum_array[1] + sum_array[2] + sum_array[3] + + sum_array[4] + sum_array[5] + sum_array[6] + sum_array[7]; + + // Handle remainder + for (; k_idx < tile_k_end; k_idx++) { + sum += A_row[k_idx] * B[k_idx * matrix_dimension + col_idx]; + } + + C[row_idx * matrix_dimension + col_idx] += sum; + } + } + } + } + } +} + +/** + * SIMD-optimized matrix multiplication using unaligned loads + * This simulates the performance penalty when data is not cache-line aligned + * C = A * B + */ +void matrix_multiply_simd_misaligned(const float *restrict A, const float *restrict B, + float *restrict C, int matrix_dimension) { + // Initialize C to zero + for (int element_idx = 0; element_idx < matrix_dimension * matrix_dimension; element_idx++) { + C[element_idx] = 0.0f; + } + + const int simd_width = 8; + const int tile_size = 64; + + // Cache-blocked matrix multiplication with SIMD using unaligned loads + for (int tile_row_start = 0; tile_row_start < matrix_dimension; tile_row_start += tile_size) { + for (int tile_col_start = 0; tile_col_start < matrix_dimension; tile_col_start += tile_size) { + for (int tile_k_start = 0; tile_k_start < matrix_dimension; tile_k_start += tile_size) { + int tile_row_end = (tile_row_start + tile_size < matrix_dimension) ? tile_row_start + tile_size : matrix_dimension; + int tile_col_end = (tile_col_start + tile_size < matrix_dimension) ? tile_col_start + tile_size : matrix_dimension; + int tile_k_end = (tile_k_start + tile_size < matrix_dimension) ? tile_k_start + tile_size : matrix_dimension; + + for (int row_idx = tile_row_start; row_idx < tile_row_end; row_idx++) { + const float *A_row = &A[row_idx * matrix_dimension]; + for (int col_idx = tile_col_start; col_idx < tile_col_end; col_idx++) { + __m256 sum_vec = _mm256_setzero_ps(); + int k_idx = tile_k_start; + + // Always use UNALIGNED loads (slower) - simulates misaligned data + for (; k_idx + simd_width <= tile_k_end; k_idx += simd_width) { + __m256 a_vec = _mm256_loadu_ps(&A_row[k_idx]); // Unaligned load (slower) + float b_values[8]; + for (int simd_element_idx = 0; simd_element_idx < 8; simd_element_idx++) { + b_values[simd_element_idx] = B[(k_idx + simd_element_idx) * matrix_dimension + col_idx]; + } + __m256 b_vec = _mm256_loadu_ps(b_values); // Unaligned load (slower) + __m256 product = _mm256_mul_ps(a_vec, b_vec); + sum_vec = _mm256_add_ps(sum_vec, product); + } + + // Horizontal sum + float sum_array[8] __attribute__((aligned(32))); + _mm256_store_ps(sum_array, sum_vec); + float sum = sum_array[0] + sum_array[1] + sum_array[2] + sum_array[3] + + sum_array[4] + sum_array[5] + sum_array[6] + sum_array[7]; + + // Handle remainder + for (; k_idx < tile_k_end; k_idx++) { + sum += A_row[k_idx] * B[k_idx * matrix_dimension + col_idx]; + } + + C[row_idx * matrix_dimension + col_idx] += sum; + } + } + } + } + } +} +#endif + +/** + * Cache-blocked (tiled) matrix multiplication: C = A * B + * Fallback for non-SIMD systems or when SIMD is disabled + */ +void matrix_multiply_blocked(const float *restrict A, const float *restrict B, + float *restrict C, int matrix_dimension) { + // Initialize C to zero + for (int element_idx = 0; element_idx < matrix_dimension * matrix_dimension; element_idx++) { + C[element_idx] = 0.0f; + } + + // Blocked matrix multiplication + for (int tile_row_start = 0; tile_row_start < matrix_dimension; tile_row_start += BLOCK_SIZE) { + for (int tile_col_start = 0; tile_col_start < matrix_dimension; tile_col_start += BLOCK_SIZE) { + for (int tile_k_start = 0; tile_k_start < matrix_dimension; tile_k_start += BLOCK_SIZE) { + // Process block + int tile_row_end = (tile_row_start + BLOCK_SIZE < matrix_dimension) ? tile_row_start + BLOCK_SIZE : matrix_dimension; + int tile_col_end = (tile_col_start + BLOCK_SIZE < matrix_dimension) ? tile_col_start + BLOCK_SIZE : matrix_dimension; + int tile_k_end = (tile_k_start + BLOCK_SIZE < matrix_dimension) ? tile_k_start + BLOCK_SIZE : matrix_dimension; + + for (int row_idx = tile_row_start; row_idx < tile_row_end; row_idx++) { + for (int col_idx = tile_col_start; col_idx < tile_col_end; col_idx++) { + float sum = C[row_idx * matrix_dimension + col_idx]; + // Inner loop with better cache locality + for (int k_idx = tile_k_start; k_idx < tile_k_end; k_idx++) { + sum += A[row_idx * matrix_dimension + k_idx] * B[k_idx * matrix_dimension + col_idx]; + } + C[row_idx * matrix_dimension + col_idx] = sum; + } + } + } + } + } +} + +/** + * Simple naive matrix multiplication for comparison + * This has poor cache locality and doesn't benefit much from alignment + */ +void matrix_multiply_naive(const float *restrict A, const float *restrict B, + float *restrict C, int matrix_dimension) { + for (int row_idx = 0; row_idx < matrix_dimension; row_idx++) { + for (int col_idx = 0; col_idx < matrix_dimension; col_idx++) { + float sum = 0.0f; + for (int k_idx = 0; k_idx < matrix_dimension; k_idx++) { + sum += A[row_idx * matrix_dimension + k_idx] * B[k_idx * matrix_dimension + col_idx]; + } + C[row_idx * matrix_dimension + col_idx] = sum; + } + } +} + +/** + * Initialize matrix with random values + */ +void init_matrix(float *matrix, int matrix_dimension) { + for (int element_idx = 0; element_idx < matrix_dimension * matrix_dimension; element_idx++) { + matrix[element_idx] = (float)rand() / RAND_MAX; + } +} + +/** + * Check memory alignment + */ +int check_alignment(const void *ptr, size_t alignment) { + return ((uintptr_t)ptr % alignment) == 0; +} + +/** + * Benchmark matrix multiplication with different alignments + * Uses SIMD-optimized algorithms when available + */ +double benchmark_matrix_multiply(float *A, float *B, float *C, int matrix_dimension, + int iterations, int use_aligned_simd) { + double total_time = 0.0; + + for (int iteration_idx = 0; iteration_idx < iterations; iteration_idx++) { + init_matrix(A, matrix_dimension); + init_matrix(B, matrix_dimension); + memset(C, 0, matrix_dimension * matrix_dimension * sizeof(float)); + + double start = get_time(); +#if USE_SIMD && defined(__AVX__) + if (use_aligned_simd) { + matrix_multiply_simd_aligned(A, B, C, matrix_dimension); + } else { + matrix_multiply_simd_misaligned(A, B, C, matrix_dimension); + } +#else + (void)use_aligned_simd; // Suppress unused parameter warning when SIMD not available + matrix_multiply_blocked(A, B, C, matrix_dimension); +#endif + double end = get_time(); + + total_time += (end - start); + } + + return total_time / iterations; +} + +int main(int argc, char *argv[]) { + int matrix_size = MATRIX_SIZE; + int csv_mode = 0; + + // Parse command line arguments + for (int arg_idx = 1; arg_idx < argc; arg_idx++) { + if (strcmp(argv[arg_idx], "-s") == 0 && arg_idx + 1 < argc) { + matrix_size = atoi(argv[arg_idx + 1]); + arg_idx++; + } else if (strcmp(argv[arg_idx], "--csv") == 0 || strcmp(argv[arg_idx], "-c") == 0) { + csv_mode = 1; + } else if (strcmp(argv[arg_idx], "-h") == 0 || strcmp(argv[arg_idx], "--help") == 0) { + printf("Usage: %s [-s SIZE] [--csv]\n", argv[0]); + printf(" -s SIZE Matrix size (default: %d)\n", MATRIX_SIZE); + printf(" --csv, -c Output results in CSV format\n"); + printf(" -h, --help Show this help message\n"); + return 0; + } + } + + if (!csv_mode) { + printf("========================================\n"); + printf("HPC Data Structure Optimization Prototype\n"); + printf("Memory Alignment Impact Demonstration\n"); + printf("========================================\n\n"); + } + + if (!csv_mode) { + printf("Matrix Size: %d x %d\n", matrix_size, matrix_size); + printf("Cache Line Size: %d bytes\n", CACHE_LINE_SIZE); + printf("Iterations: %d (after %d warmup)\n\n", NUM_ITERATIONS, + WARMUP_ITERATIONS); + } + + // Allocate matrices with cache-line alignment (64-byte) + float *A_aligned = (float *)aligned_malloc( + matrix_size * matrix_size * sizeof(float), CACHE_LINE_SIZE); + float *B_aligned = (float *)aligned_malloc( + matrix_size * matrix_size * sizeof(float), CACHE_LINE_SIZE); + float *C_aligned = (float *)aligned_malloc( + matrix_size * matrix_size * sizeof(float), CACHE_LINE_SIZE); + + // Allocate matrices with misalignment (16-byte, not cache-line aligned) + float *A_misaligned = + (float *)misaligned_malloc(matrix_size * matrix_size * sizeof(float)); + float *B_misaligned = + (float *)misaligned_malloc(matrix_size * matrix_size * sizeof(float)); + float *C_misaligned = + (float *)misaligned_malloc(matrix_size * matrix_size * sizeof(float)); + + if (!A_aligned || !B_aligned || !C_aligned || !A_misaligned || + !B_misaligned || !C_misaligned) { + fprintf(stderr, "Error: Memory allocation failed\n"); + return 1; + } + + if (!csv_mode) { + // Verify alignments + printf("Memory Alignment Verification:\n"); + printf(" A_aligned: %s (address: %p)\n", + check_alignment(A_aligned, CACHE_LINE_SIZE) ? "64-byte aligned" + : "NOT aligned", + (void *)A_aligned); + printf(" A_misaligned: %s (address: %p)\n", + check_alignment(A_misaligned, CACHE_LINE_SIZE) ? "64-byte aligned" + : "16-byte aligned", + (void *)A_misaligned); + printf(" Alignment offset: %zu bytes\n\n", + (uintptr_t)A_misaligned % CACHE_LINE_SIZE); + +#if USE_SIMD && defined(__AVX__) + printf("Using AVX SIMD-optimized algorithm with alignment-sensitive loads\n"); + printf("Aligned version uses _mm256_load_ps (fast aligned loads)\n"); + printf("Misaligned version uses _mm256_loadu_ps (slower unaligned loads)\n\n"); +#else + printf("Using cache-blocked (tiled) algorithm for better alignment " + "demonstration\n"); + printf("Block size: %d (designed to fit in cache)\n\n", BLOCK_SIZE); + printf("Note: SIMD not available. Recompile with -mavx for better alignment demonstration.\n\n"); +#endif + } + + // Warmup runs + if (!csv_mode) { + printf("Warming up...\n"); + } + benchmark_matrix_multiply(A_aligned, B_aligned, C_aligned, matrix_size, + WARMUP_ITERATIONS, 1); + benchmark_matrix_multiply(A_misaligned, B_misaligned, C_misaligned, + matrix_size, WARMUP_ITERATIONS, 0); + if (!csv_mode) { + printf("Warmup complete.\n\n"); + } + + // Benchmark optimized version (cache-line aligned) with SIMD aligned loads + if (!csv_mode) { + printf("Benchmarking OPTIMIZED version (64-byte cache-line aligned, " + "SIMD aligned loads)...\n"); + } + double time_aligned = benchmark_matrix_multiply( + A_aligned, B_aligned, C_aligned, matrix_size, NUM_ITERATIONS, 1); + if (!csv_mode) { + printf(" Average time: %.6f seconds\n", time_aligned); + printf(" Performance: %.2f GFLOPS\n\n", + (2.0 * matrix_size * matrix_size * matrix_size) / + (time_aligned * 1e9)); + } + + // Benchmark non-optimized version (misaligned) with SIMD unaligned loads + if (!csv_mode) { + printf("Benchmarking NON-OPTIMIZED version (16-byte aligned, " + "SIMD unaligned loads)...\n"); + } + double time_misaligned = benchmark_matrix_multiply( + A_misaligned, B_misaligned, C_misaligned, matrix_size, NUM_ITERATIONS, 0); + if (!csv_mode) { + printf(" Average time: %.6f seconds\n", time_misaligned); + printf(" Performance: %.2f GFLOPS\n\n", + (2.0 * matrix_size * matrix_size * matrix_size) / + (time_misaligned * 1e9)); + } + + // Calculate performance difference + double speedup = time_misaligned / time_aligned; + double slowdown = time_aligned / time_misaligned; + + // CSV output mode + if (csv_mode) { + printf("%d,%.6f,%.6f,%.4f\n", matrix_size, time_aligned, time_misaligned, speedup); + return 0; + } + + printf("========================================\n"); + printf("Results Summary:\n"); + printf("========================================\n"); + printf("Optimized (64-byte aligned): %.6f sec\n", time_aligned); + printf("Non-optimized (misaligned): %.6f sec\n", time_misaligned); + printf("Performance difference: %.2fx\n", speedup); + + // Interpret results based on Issue #3879 pattern + if (speedup > 1.05) { + printf("\n[OK] Optimized version is %.2fx FASTER\n", speedup); + printf(" This demonstrates the alignment benefit.\n"); + } else if (speedup < 0.95) { + printf("\n[WARNING] Non-optimized version is %.2fx FASTER\n", slowdown); + printf(" This matches the VARIABILITY pattern from OpenBLAS Issue #3879:\n"); + printf(" - Performance varies by matrix size due to cache interactions\n"); + printf(" - At some sizes, misalignment can appear faster due to:\n"); + printf(" * Cache line boundary effects\n"); + printf(" * Memory access pattern interactions\n"); + printf(" * CPU prefetcher behavior variations\n"); + } else { + printf("\n[~] Performance difference is minimal (< 5%%)\n"); + printf(" This demonstrates the VARIABILITY pattern from Issue #3879.\n"); + } + + printf("\n Key Insight from Issue #3879:\n"); + printf(" - Performance VARIABILITY (not consistent speedup) is the issue\n"); + printf(" - Different matrix sizes show different alignment sensitivity\n"); + printf(" - This unpredictability is problematic for HPC applications\n"); + + printf("\n========================================\n"); + printf("HPC Context & Empirical Study Alignment:\n"); + printf("========================================\n"); + printf("According to the empirical study on HPC performance bugs:\n"); + printf("- Memory alignment issues account for significant performance " + "variability\n"); + printf("- Cache-line alignment (64-byte) enables efficient SIMD " + "vectorization\n"); + printf("- Proper alignment reduces cache misses through better spatial " + "locality\n"); + printf("- Performance VARIABILITY (not consistent speedup) is the key issue\n"); + printf("\nOpenBLAS Issue #3879 Pattern:\n"); + printf("- At N=512: Misaligned faster (cache effects)\n"); + printf("- At N=1024: Misaligned faster (cache effects)\n"); + printf("- At N=1500: Aligned faster (alignment benefit)\n"); + printf("- At N=2048: Misaligned faster (cache effects)\n"); + printf("\nThis prototype demonstrates:\n"); +#if USE_SIMD && defined(__AVX__) + printf("- SIMD operations with aligned vs unaligned loads\n"); + printf("- How alignment affects AVX vectorization performance\n"); +#else + printf("- Cache-blocked matrix operations\n"); + printf("- How cache-line alignment affects memory access patterns\n"); +#endif + printf("- Performance VARIABILITY pattern matching Issue #3879\n"); + printf("\nThe variability (not consistent speedup) is the critical finding:\n"); + printf(" - Unpredictable performance makes optimization difficult\n"); + printf(" - Cache interactions cause size-dependent behavior\n"); + printf(" - Proper alignment reduces this variability\n"); + + // Cleanup + free(A_aligned); + free(B_aligned); + free(C_aligned); + misaligned_free(A_misaligned); + misaligned_free(B_misaligned); + misaligned_free(C_misaligned); + + return 0; +} diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..608bf99 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,3 @@ +matplotlib>=3.5.0 +numpy>=1.21.0 + diff --git a/run_all_tests.sh b/run_all_tests.sh new file mode 100755 index 0000000..7551ed8 --- /dev/null +++ b/run_all_tests.sh @@ -0,0 +1,83 @@ +#!/bin/bash +# Complete test suite for HPC Alignment Benchmark +# Based on OpenBLAS Issue #3879 and Step.md setup instructions + +set -e # Exit on error + +echo "==========================================" +echo "HPC Alignment Benchmark - Complete Test Suite" +echo "Based on OpenBLAS Issue #3879" +echo "==========================================" +echo "" + +# Check if we're in the right directory +if [ ! -f "matrix_alignment_prototype.c" ]; then + echo "Error: matrix_alignment_prototype.c not found" + echo "Please run this script from the project directory" + exit 1 +fi + +# Step 1: Build the prototype +echo "Step 1: Building prototype..." +echo "----------------------------------------" +make clean +make +if [ $? -ne 0 ]; then + echo "Error: Build failed" + exit 1 +fi +echo "✓ Build successful" +echo "" + +# Step 2: Run benchmarks for all sizes +echo "Step 2: Running benchmarks for multiple sizes..." +echo "----------------------------------------" +./run_benchmark_sizes.sh +if [ $? -ne 0 ]; then + echo "Error: Benchmark failed" + exit 1 +fi +echo "" + +# Step 3: Display results +echo "Step 3: Benchmark Results Summary" +echo "----------------------------------------" +if [ -f "benchmark_results.csv" ]; then + echo "" + echo "Results:" + column -t -s',' benchmark_results.csv + echo "" +else + echo "Warning: benchmark_results.csv not found" +fi + +# Step 4: Check for plots +echo "Step 4: Generated Files" +echo "----------------------------------------" +if [ -f "alignment_benchmark_results.png" ]; then + echo "✓ Main plot: alignment_benchmark_results.png" +fi +if [ -f "alignment_benchmark_execution_time.png" ]; then + echo "✓ Execution time plot: alignment_benchmark_execution_time.png" +fi +if [ -f "alignment_benchmark_speedup.png" ]; then + echo "✓ Speedup plot: alignment_benchmark_speedup.png" +fi +if [ -f "alignment_benchmark_variability.png" ]; then + echo "✓ Variability plot: alignment_benchmark_variability.png" +fi +if [ -f "alignment_benchmark_gflops.png" ]; then + echo "✓ GFLOPS plot: alignment_benchmark_gflops.png" +fi + +echo "" +echo "==========================================" +echo "Test suite complete!" +echo "==========================================" +echo "" +echo "Next steps:" +echo "1. Review benchmark_results.csv for detailed data" +echo "2. Check generated PNG plots for visualizations" +echo "3. Compare results with OpenBLAS Issue #3879 findings" +echo "" + diff --git a/run_benchmark_sizes.sh b/run_benchmark_sizes.sh new file mode 100755 index 0000000..2278efc --- /dev/null +++ b/run_benchmark_sizes.sh @@ -0,0 +1,61 @@ +#!/bin/bash +# Benchmark script to test multiple matrix sizes +# Based on OpenBLAS Issue #3879 performance variability analysis + +# Matrix sizes to test (from before the fix issue 3879.md) +SIZES=(512 1024 1500 2048) + +# Output file for results +RESULTS_FILE="benchmark_results.csv" + +echo "==========================================" +echo "HPC Alignment Benchmark - Multiple Sizes" +echo "==========================================" +echo "" + +# Create results file with header +echo "Matrix_Size,Aligned_Time,Misaligned_Time,Speedup" > "$RESULTS_FILE" + +# Build the prototype if needed +if [ ! -f "matrix_alignment_prototype" ]; then + echo "Building prototype..." + make clean + make + if [ $? -ne 0 ]; then + echo "Error: Build failed" + exit 1 + fi +fi + +# Run benchmarks for each size +for SIZE in "${SIZES[@]}"; do + echo "Testing matrix size: ${SIZE}x${SIZE}" + echo "----------------------------------------" + + # Run the benchmark with specific size in CSV mode + ./matrix_alignment_prototype -s "$SIZE" --csv >> "$RESULTS_FILE" + + if [ $? -ne 0 ]; then + echo "Error: Benchmark failed for size $SIZE" + exit 1 + fi + + echo "" +done + +echo "==========================================" +echo "Benchmark complete!" +echo "Results saved to: $RESULTS_FILE" +echo "==========================================" + +# Generate plots +if command -v python3 &> /dev/null; then + echo "" + echo "Generating plots..." + python3 generate_plots.py +else + echo "" + echo "Python3 not found. Skipping plot generation." + echo "Install Python3 and matplotlib to generate plots." +fi +