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whisprer edited this page Aug 4, 2025 · 2 revisions

Universal High-Performance RNG Library

"Welcome to the Universal-Architecture-RNG-Lib wiki! I'm out to beat C++'s engineers at their own game and improve upon the std library implementation!"

A mission to create the fastest, most adaptive random number generator across all possible hardware architectures.

🚀 Project Vision

The Universal RNG Library represents an ambitious quest to surpass the performance of C++ standard library random number generators through intelligent runtime adaptation and cutting-edge SIMD optimization. This isn't just another RNG - it's a hardware-aware, performance-obsessed random number generation system that automatically selects the optimal implementation for your specific CPU and GPU capabilities.

⚡ Performance Philosophy

Our priorities are crystal clear:

  1. 🏎️ SPEED - Outperform std::random_device and default C++ RNGs
  2. 🎲 RANDOMNESS QUALITY - Maintain scientific-grade statistical properties
  3. 💾 MEMORY EFFICIENCY - Minimal footprint with maximum throughput

🏗️ What Makes This Special

Runtime Intelligence

// The library automatically detects and selects:
✅ AVX-512 (8-way parallelism)     // Latest Intel/AMD CPUs
✅ AVX2 (4-way parallelism)        // Modern CPUs
✅ SSE2 (2-way parallelism)        // Legacy compatibility  
✅ ARM NEON (2-way parallelism)    // ARM processors
✅ OpenCL GPU (1024+ parallelism)  // Massive throughput
✅ Scalar fallback                 // Universal compatibility

Dual-Algorithm Excellence

  • 🎯 Xoroshiro128++ - The C++ standard's choice, optimized beyond recognition
  • ⚡ WyRand - Superior randomness quality with exceptional speed

Modern C++ Mastery

  • Smart pointers (std::unique_ptr, std::shared_ptr)
  • RAII memory management
  • Template-based dispatch optimization
  • Zero-overhead abstractions

🎮 Quick Start

1. Clone & Build

git clone https://github.com/YOUR_USERNAME/universal-rng.git
cd universal-rng

Automatic detection and optimization


./simple_compiler.bat


Force specific SIMD (if you know your hardware)


./simple_compiler.bat avx512f avx512dq avx512bw avx512vl

2. Basic Usage

#include "universal_rng.h"

int main() {
// Create RNG with automatic optimal implementation
universal_rng_t* rng = universal_rng_new(42, 0, 1);


// Single values - perfect for gaming
uint64_t random_id = universal_rng_next_u64(rng);
double probability = universal_rng_next_double(rng);

// Batch generation - perfect for scientific computing
std::vector<uint64_t> batch(1000000);
universal_rng_generate_batch(rng, batch.data(), batch.size());

universal_rng_free(rng);



}

3. See the Magic

Creating RNG...
CPU feature detection:
  SSE2: Yes
  AVX2: Yes  
  AVX512: Yes
Using AVX512 implementation
Batch generation: 4.2x speedup over scalar!

📊 Performance Achievements

Implementation Single Gen Speed Batch Speedup Hardware Target
Scalar 1.0x (baseline) 1.0x Any CPU
SSE2 1.2x 2.1x Intel/AMD 2001+
AVX2 1.8x 4.2x Intel Haswell+
AVX-512 2.3x 8.1x Intel Skylake-X+
OpenCL GPU 0.8x 100x+ Dedicated GPU

🏆 Project Stats

  • 🎯 Algorithms: 2 (Xoroshiro128++, WyRand)
  • 🔧 SIMD Variants: 6 (Scalar, SSE2, AVX, AVX2, AVX-512, NEON)
  • 🖥️ Platforms: Windows, Linux, macOS
  • 📊 Bit Widths: 16, 32, 64, 128, 256, 512, 1024-bit
  • ⚡ Max Parallelism: 1024+ streams (OpenCL)
  • 🎮 Performance Gain: Up to 8x faster than std library

💫 The Story

What started as a challenge to "beat C++'s own engineers at their own game" became an epic journey through:

  • 🔍 CPU Feature Detection - Runtime intelligence across platforms
  • ⚡ SIMD Optimization - From SSE2 to cutting-edge AVX-512
  • 🎯 Modern C++ Refactoring - Smart pointers and RAII mastery
  • 🖥️ GPU Acceleration - OpenCL integration for massive parallelism
  • 📊 Comprehensive Benchmarking - Proving performance claims with data

The result? A library that doesn't just match standard implementations - it obliterates them while maintaining scientific-grade randomness quality.

🎊 Ready to Experience the Speed? 

git clone https://github.com/YOUR_USERNAME/universal-rng.git
cd universal-rng
./simple_compiler.bat
./enhanced_bitwidth_benchmark.exe

Welcome to the future of random number generation! 🚀✨

# Universal High-Performance RNG Library

"I'm out to beat C++'s own engineers at their own game and improve upon the std library implementation!"

A mission to create the fastest, most adaptive random number generator across all possible hardware architectures.

🚀 Project Vision

The Universal RNG Library represents an ambitious quest to surpass the performance of C++ standard library random number generators through intelligent runtime adaptation and cutting-edge SIMD optimization. This isn't just another RNG - it's a hardware-aware, performance-obsessed random number generation system that automatically selects the optimal implementation for your specific CPU and GPU capabilities.

⚡ Performance Philosophy

Our priorities are crystal clear:

  1. 🏎️ SPEED - Outperform std::random_device and default C++ RNGs
  2. 🎲 RANDOMNESS QUALITY - Maintain scientific-grade statistical properties
  3. 💾 MEMORY EFFICIENCY - Minimal footprint with maximum throughput

🏗️ What Makes This Special

Runtime Intelligence

// The library automatically detects and selects:
✅ AVX-512 (8-way parallelism)     // Latest Intel/AMD CPUs
✅ AVX2 (4-way parallelism)        // Modern CPUs
✅ SSE2 (2-way parallelism)        // Legacy compatibility  
✅ ARM NEON (2-way parallelism)    // ARM processors
✅ OpenCL GPU (1024+ parallelism)  // Massive throughput
✅ Scalar fallback                 // Universal compatibility

Dual-Algorithm Excellence

  • 🎯 Xoroshiro128++ - The C++ standard's choice, optimized beyond recognition
  • ⚡ WyRand - Superior randomness quality with exceptional speed

Modern C++ Mastery

  • Smart pointers (std::unique_ptr, std::shared_ptr)
  • RAII memory management
  • Template-based dispatch optimization
  • Zero-overhead abstractions

🎮 Quick Start

1. Clone & Build

git clone https://github.com/YOUR_USERNAME/universal-rng.git
cd universal-rng

# Automatic detection and optimization
./simple_compiler.bat

# Force specific SIMD (if you know your hardware)
./simple_compiler.bat avx512f avx512dq avx512bw avx512vl

2. Basic Usage

#include "universal_rng.h"

int main() {
    // Create RNG with automatic optimal implementation
    universal_rng_t* rng = universal_rng_new(42, 0, 1);
    
    // Single values - perfect for gaming
    uint64_t random_id = universal_rng_next_u64(rng);
    double probability = universal_rng_next_double(rng);
    
    // Batch generation - perfect for scientific computing
    std::vector<uint64_t> batch(1000000);
    universal_rng_generate_batch(rng, batch.data(), batch.size());
    
    universal_rng_free(rng);
}

3. See the Magic

Creating RNG...
CPU feature detection:
  SSE2: Yes
  AVX2: Yes  
  AVX512: Yes
Using AVX512 implementation
Batch generation: 4.2x speedup over scalar!

📊 Performance Achievements

Implementation Single Gen Speed Batch Speedup Hardware Target
Scalar 1.0x (baseline) 1.0x Any CPU
SSE2 1.2x 2.1x Intel/AMD 2001+
AVX2 1.8x 4.2x Intel Haswell+
AVX-512 2.3x 8.1x Intel Skylake-X+
OpenCL GPU 0.8x 100x+ Dedicated GPU

Benchmarked on various hardware configurations - your results may vary

🎯 Who Should Use This

🎮 Game Developers

  • Ultra-fast random number generation for procedural content
  • Automatic hardware optimization without code changes
  • Batch generation for particle systems and terrain generation

🔬 Scientific Computing

  • High-quality randomness for Monte Carlo simulations
  • Massive batch generation for statistical analysis
  • Cross-platform consistency with optimal performance

💻 Systems Programming

  • Replace standard library RNGs with faster alternatives
  • Hardware-aware optimization in performance-critical applications
  • Future-proof code that adapts to new instruction sets

🗺️ Wiki Navigation

Section Description
[🏗️ Architecture Overview](Architecture-Overview) Deep dive into the runtime dispatch system
[⚡ SIMD Implementations](SIMD-Implementations) Technical details of each optimization level
[🎯 Performance Analysis](Performance-Analysis) Benchmark results and optimization stories
[🔧 Build System Guide](Build-System-Guide) Compilation options and platform support
[📈 Development History](Development-History) The epic journey from concept to reality
[🚀 Future Roadmap](Future-Roadmap) Cryptographic security and multi-language plans
[🧪 API Reference](API-Reference) Complete function documentation
[❓ FAQ & Troubleshooting](FAQ-Troubleshooting) Common issues and solutions

🏆 Project Stats

  • 🎯 Algorithms: 2 (Xoroshiro128++, WyRand)
  • 🔧 SIMD Variants: 6 (Scalar, SSE2, AVX, AVX2, AVX-512, NEON)
  • 🖥️ Platforms: Windows, Linux, macOS
  • 📊 Bit Widths: 16, 32, 64, 128, 256, 512, 1024-bit
  • ⚡ Max Parallelism: 1024+ streams (OpenCL)
  • 🎮 Performance Gain: Up to 8x faster than std library

💫 The Story

What started as a challenge to "beat C++'s own engineers at their own game" became an epic journey through:

  • 🔍 CPU Feature Detection - Runtime intelligence across platforms
  • ⚡ SIMD Optimization - From SSE2 to cutting-edge AVX-512
  • 🎯 Modern C++ Refactoring - Smart pointers and RAII mastery
  • 🖥️ GPU Acceleration - OpenCL integration for massive parallelism
  • 📊 Comprehensive Benchmarking - Proving performance claims with data

The result? A library that doesn't just match standard implementations - it obliterates them while maintaining scientific-grade randomness quality.

🎊 Ready to Experience the Speed? 

git clone https://github.com/YOUR_USERNAME/universal-rng.git
cd universal-rng
./simple_compiler.bat
./enhanced_bitwidth_benchmark.exe

Welcome to the future of random number generation! 🚀✨

There is currently data lost off the bottom off the page - a search party needs to be sent in to rescue!

PLEASE DO BEAR IN CONSTANT MIND ABOVE ALL ELSE: CURRENT STATE OF DEVELOPMENT THE C++ STD LIBRARY EMPLOYING MERSENNE TWISTER STILL OUTPERFORMS SINGLE CALCULATION OPERATIONS FOR NON-SIMD BOOSTED COMPUTERS. THESE LIBRARIES FULLY REQUIRE AT LEAST AVX2 MINIMUM TO BENEFIT OVER THE STD GENERATION METHODS WHEN CONSIDERING SINGLE NUMBER GENERATION TASKS.

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