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ROADMAP Universal RNG Library
whisprer edited this page Aug 4, 2025
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The Universal RNG Library aims to be the fastest, most comprehensive, and most portable random number generation library available, providing optimal performance across all modern computing platforms while maintaining exceptional statistical quality.
- Core Architecture: Universal generator interface with runtime SIMD detection
- Algorithm Implementations: Xoroshiro128++, WyRand, MT19937-64 with scalar and AVX2 variants
- Multi-bit Width Support: 16, 32, 64, 128, 256, 512, and 1024-bit generators
- Batch Generation: High-performance SIMD-optimized batch processing
- Cross-Platform Build: Windows (MSVC/MinGW), Linux (GCC/Clang), macOS support
- C API: Complete C language bindings for cross-language compatibility
- Benchmarking Suite: Comprehensive performance measurement framework
- 4.6x AVX2 speedup in batch mode (128-bit width)
- 1355 M ops/sec peak throughput (64-bit Xoroshiro128++)
- Consistent 3-4x improvements across 64-256 bit ranges
Priority: P0 - Must Fix
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Single-mode optimization: Eliminate 30-70% performance penalty
- Template-based dispatch replacing function pointers
- Aggressive compiler optimization integration
- Target: Match reference implementation speed
- Memory copy elimination: Remove unnecessary copies in batch mode
- AVX-512 detection fix: Resolve build system conflicts
Priority: P1 - High Impact
- Loop unrolling optimization: 4x unroll factor for AVX2 kernels
- Register allocation improvements: Minimize SIMD register pressure
- Cache-conscious batch processing: Prefetch and streaming stores
- Profile-guided optimization: PGO integration in build system
Single-mode performance: 0% regression vs reference implementations
AVX2 batch speedup: 4.5x+ (from current 4.2x)
Memory bandwidth efficiency: 90%+ of theoretical maximum
- AVX-512 implementations: 6-8x speedup targets for supported CPUs
- ARM NEON optimization: Native performance for ARM64 platforms
- Apple Silicon support: M1/M2 optimized implementations
- RISC-V basic support: Future-proofing for emerging architectures
- CMake improvements: Better cross-compilation and feature detection
- Package manager integration: vcpkg, Conan, and system package support
- Continuous integration: Automated cross-platform testing
- Static analysis integration: Clang-tidy, PVS-Studio integration
- Microcontroller support: Arduino, ESP32, and STM32 compatibility
- Memory-constrained builds: Minimal footprint configurations
- Fixed-point implementations: Integer-only variants for resource-limited systems
Priority: High Demand
- ChaCha20-based PRNG: 200-300 M ops/sec target with crypto security
- AES-CTR generator: Hardware-accelerated with AES-NI support
- Secure seeding framework: Integration with system entropy sources
- FIPS compliance: Documentation and testing for regulated environments
- PCG family: Configurable, high-quality generators
- xoshiro256++: Extended precision variant
- Lehmer128: Ultra-simple, ultra-fast generator
- Domain-specific: Optimized for floating-point, Gaussian distributions
- TestU01 integration: Automated BigCrush testing
- PractRand integration: Long-term statistical quality validation
- Custom test suites: Application-specific quality metrics
- Quality reporting: Automated statistical quality reports
Priority: High Performance Computing
- CUDA implementation: NVIDIA GPU acceleration
- OpenCL support: Cross-vendor GPU compatibility
- ROCm integration: AMD GPU optimization
- Bulk generation: 10-100x speedup for massive parallel workloads
- AVX-512 optimization: Full utilization of 512-bit vectors
- Variable-width vectors: Adaptive to available SIMD width
- ARM SVE support: Scalable vector extensions for future ARM CPUs
- Auto-vectorization: Compiler-assisted optimization
- Infinite streams: Memory-efficient continuous generation
- Parallel streams: Independent, non-overlapping sequences
- Stream synchronization: Coordinated parallel generation
- Checkpoint/restore: State serialization for long computations
Priority: Ecosystem Growth
- Rust bindings: Complete Rust API with zero-cost abstractions
- Python package: High-performance NumPy integration
- JavaScript/WebAssembly: Browser and Node.js support
- Go bindings: Native Go integration
- Java/JNI: Enterprise Java compatibility
- Package managers: npm, PyPI, crates.io, Maven Central
- Container images: Docker containers for development
- Cloud deployment: AWS Lambda, Google Cloud Functions optimization
- Documentation hub: Comprehensive online documentation
Single-mode: Match or exceed all reference implementations
AVX2 batch: 5x+ speedup consistently
AVX-512 batch: 8x+ speedup on supported hardware
GPU acceleration: 50-100x speedup for bulk generation
Memory efficiency: <1% overhead vs theoretical minimum
- Quantum-resistant PRNGs: Future-proofing against quantum computing
- Neural network-based: ML-enhanced randomness quality
- Hardware entropy integration: True random number incorporation
- Adaptive algorithms: Self-tuning based on usage patterns
- Compiler-as-a-service: JIT compilation for optimal code generation
- Hardware-specific tuning: Per-CPU-model optimization
- Memory compression: Compressed state representations
- Distributed generation: Network-distributed random streams
- WebGPU support: Browser-based GPU acceleration
- FPGA implementations: Custom hardware acceleration
- Optical computing: Future optical processor support
- DNA storage: Biological computing integration
Current: 200-300 M ops/sec (underperforming)
v0.2.0: 800+ M ops/sec (match reference)
v0.3.0: 1000+ M ops/sec (optimized templates)
v1.0.0: 1200+ M ops/sec (perfect optimization)
Current: 1355 M ops/sec peak (AVX2)
v0.2.0: 1500+ M ops/sec (optimization)
v0.3.0: 3000+ M ops/sec (AVX-512)
v0.5.0: 10000+ M ops/sec (GPU acceleration)
v1.0.0: 50000+ M ops/sec (optimized GPU)
Current: Good cache utilization
v0.2.0: Optimal memory alignment
v0.3.0: Zero-copy batch processing
v1.0.0: Theoretical minimum memory usage
- Developer outreach: Conference presentations and workshops
- Academic partnerships: Research collaboration with universities
- Industry adoption: Enterprise use case development
- Open source contributions: Welcoming external contributors
- Video tutorials: YouTube channel with implementation guides
- Interactive demos: Web-based performance demonstrations
- Academic papers: Peer-reviewed research publications
- Workshop materials: University course integration
- Industry adoption: Use in major scientific computing frameworks
- Academic citations: Research paper references and validation
- Performance leadership: Fastest RNG library benchmarks
- Quality certification: Independent statistical validation
- Performance: Consistent leadership in speed benchmarks
- Quality: Pass all major statistical test suites
- Portability: Support for 95%+ of target platforms
- Adoption: 1000+ GitHub stars, 100+ contributors
- Scientific computing: Adoption in major simulation frameworks
- Gaming industry: Integration in AAA game engines
- Financial modeling: Use in quantitative trading systems
- Academic research: Citations in peer-reviewed papers
- Algorithm advances: Novel PRNG algorithm contributions
- Performance breakthroughs: New optimization techniques
- Platform pioneering: First-to-market on new architectures
- Standard influence: Impact on future RNG standards
- Every architecture: ARM, x86, RISC-V, GPU, FPGA, quantum
- Every language: Native bindings for all major programming languages
- Every scale: Embedded microcontrollers to supercomputer clusters
- Every application: Gaming, finance, science, AI, cryptography
- De facto standard: The go-to library for high-performance random generation
- Reference implementation: Used as benchmark for other libraries
- Academic adoption: Standard tool in computational science curricula
- Commercial licensing: Enterprise support and custom optimizations
- Algorithm innovation: Pioneer new PRNG techniques and optimizations
- Performance boundaries: Push theoretical limits of generation speed
- Quality standards: Define new statistical testing methodologies
- Platform adoption: First library on emerging computing platforms
Problem: Current 30-70% performance penalty vs reference implementations Solution Approach:
- Template metaprogramming for compile-time dispatch
- Aggressive inlining and loop unrolling
- Compiler-specific optimization pragmas
- Profile-guided optimization integration
Problem: Detection and build issues prevent AVX-512 deployment Solution Approach:
- Modular SIMD detection framework
- Runtime capability testing
- Fallback mechanism design
- Cross-compiler compatibility matrix
Problem: Higher bit-widths hit memory bandwidth limits Solution Approach:
- Streaming store optimizations
- Cache-conscious data structures
- Prefetch instruction integration
- NUMA-aware memory allocation
Problem: NEON implementations lag behind AVX2 performance Solution Approach:
- ARM-specific algorithm optimizations
- Apple Silicon custom tuning
- SVE future-proofing
- ARM Cortex-A series targeting
Problem: Memory and power limitations on embedded platforms Solution Approach:
- Minimal state generators
- Power-aware algorithms
- Flash memory optimizations
- Real-time deterministic guarantees
- Single-mode optimization: Template dispatch implementation
- AVX-512 support: Build system fixes and implementations
- ARM NEON: Performance optimization for ARM platforms
- Statistical testing: TestU01 and PractRand integration
- Documentation: API examples and performance guides
- GPU acceleration: CUDA and OpenCL implementations
- Cryptographic algorithms: ChaCha20 and AES-CTR generators
- Language bindings: Python, Rust, and JavaScript APIs
- Package management: Distribution system integration
- Cross-compilation: Embedded system support
- Novel algorithms: New PRNG designs and optimizations
- Hardware integration: FPGA and custom silicon support
- Quantum resistance: Post-quantum cryptography preparation
- Machine learning: AI-enhanced randomness generation
- Distributed systems: Network-coordinated generation
Current: Core maintainer + community contributors
v0.2.0: +1 Performance optimization specialist
v0.3.0: +1 Platform/architecture expert
v0.4.0: +1 Cryptography/security specialist
v0.5.0: +1 GPU computing expert
v1.0.0: +2 Language binding developers
- CI/CD expansion: Multi-platform automated testing
- Performance monitoring: Continuous benchmark tracking
- Documentation hosting: Comprehensive online documentation
- Package repositories: Multi-language distribution infrastructure
- Community support: Discord/forum/issue management systems
- SIMD expertise: AVX-512, NEON, SVE optimization knowledge
- Cryptography: Secure PRNG design and analysis
- GPU programming: CUDA, OpenCL, and compute shader expertise
- Language ecosystems: Python C extensions, Rust FFI, WebAssembly
- Performance analysis: Profiling, benchmarking, and optimization
The Universal RNG Library roadmap represents an ambitious but achievable vision for revolutionizing random number generation across the computing landscape. With a focus on performance, quality, and universality, each release builds toward the ultimate goal of becoming the definitive solution for high-performance random number generation.
- Performance first: Never compromise on speed for features
- Quality assurance: Rigorous statistical testing at every stage
- Community driven: Welcome contributions and feedback
- Platform agnostic: Support every relevant computing platform
- Future ready: Anticipate and prepare for emerging technologies
- Contributors: Join us in building the fastest RNG library
- Users: Integrate and provide feedback on performance
- Researchers: Collaborate on algorithm development
- Industry: Adopt and help drive real-world requirements
- Students: Learn cutting-edge optimization techniques
The future of random number generation is fast, universal, and open source. Let's build it together!
Roadmap version 1.0 | Updated August 2025 | Next review: Q4 2025
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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.