Production-Ready Pure Rust Scientific Computing • No System Dependencies • 10-100x Performance Gains

SciRS2 is a comprehensive scientific computing and AI/ML infrastructure in Pure Rust, providing SciPy-compatible APIs while leveraging Rust's performance, safety, and concurrency features. Unlike traditional scientific libraries, SciRS2 is 100% Pure Rust by default with no C/C++/Fortran dependencies required, making installation effortless and ensuring cross-platform compatibility.

# Install Rust (if not already installed)
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh

# Add SciRS2 to your project
cargo add scirs2

# Build your project - no system libraries needed!
cargo build --release

✨ Pure Rust: Zero C/C++/Fortran dependencies (OxiBLAS for BLAS/LAPACK, OxiFFT for FFT) ⚡ Ultra-Fast: 10-100x performance improvements through SIMD optimization 🔒 Memory Safe: Rust's ownership system prevents memory leaks and data races 🌍 Cross-Platform: Linux, macOS, Windows, WebAssembly - identical behavior 🧪 Battle-Tested: 11,400+ tests, 1.95M lines of code, 27 workspace crates 📊 Comprehensive: Linear algebra, statistics, ML, FFT, signal processing, computer vision, and more

SciRS2 provides a complete ecosystem for scientific computing, data analysis, and machine learning in Rust, with production-grade quality and performance that rivals or exceeds traditional C/Fortran-based libraries.

Latest Stable Release - v0.1.5 (February 7, 2026) 🚀

• ✅ SIMD Phase 60-69: Advanced SIMD operations (beta functions, interpolation, geometry, probability, array ops)
• ✅ Spatial Algorithms: Enhanced Delaunay triangulation with modular Bowyer-Watson implementation
• ✅ Autograd Fixes: Fixed Adam optimizer update mechanism and eliminated warning spam (Issue #100)
• ✅ Pure Rust FFT: Migrated from FFTW to OxiFFT - 100% Pure Rust by default
• ✅ Zero-Allocation SIMD: In-place operations for optimal performance (AVX2/NEON)
• ✅ AI/ML Ready: Functional optimizers (SGD, Adam, RMSprop) with training infrastructure
• ✅ Zero Warnings Policy: Clean build with 0 compilation errors, 0 clippy warnings
• ✅ Comprehensive Testing: 11,400+ tests passing across all modules
• ✅ Code Quality: 1.95M total lines (1.69M Rust code), full clippy compliance
• 📅 Release Date: February 7, 2026

What's New in 0.1.5:

• SIMD Phase 60-69: 8 new advanced SIMD operation modules
  • Beta functions (complete beta, incomplete beta, regularized beta)
  • Advanced interpolation kernels (cubic, bicubic, tricubic, Catmull-Rom)
  • Geometric operations (cross product, angle calculation, triangle area)
  • Smootherstep functions and related smoothing operations
  • Probability distributions (CDF, PDF, quantile functions)
  • Advanced math operations (FMA, polynomial evaluation, copysign, nextafter)
  • Logarithmic/exponential operations (log2, log10, exp2, expm1, log1p)
  • Array operations (cumsum, cumprod, diff, gradient)
• Spatial Algorithms: Complete Delaunay triangulation refactoring
  • Modular Bowyer-Watson implementation (2D/3D/ND)
  • Constrained Delaunay triangulation support
  • Enhanced query operations (point location, nearest neighbors, circumcircle tests)
  • Improved robustness and comprehensive test coverage
• FFT Enhancements: Advanced coordinator architecture for complex FFT pipelines
• Special Functions: Interactive learning modules and advanced derivation studio
• Autograd Fixes: Optimizer::update() correctly updates variables (Issue #100)
• Python Bindings: Expanded coverage to 11 additional modules
• Interpolation: Enhanced PCHIP with linear extrapolation
• Build System: Improved manylinux compatibility for Python wheel distribution

See SCIRS2_POLICY.md for architectural details and CHANGELOG.md for complete release history.

SciRS2 is 100% Pure Rust by default - no C, C++, or Fortran dependencies required!

Unlike traditional scientific computing libraries that rely on external system libraries (OpenBLAS, LAPACK), SciRS2 provides a completely self-contained Pure Rust implementation:

• ✅ BLAS/LAPACK: Pure Rust OxiBLAS implementation (no OpenBLAS/MKL/Accelerate required)
• ✅ FFT: Pure Rust OxiFFT with FFTW-comparable performance (no C libraries required)
• ✅ Random Number Generation: Pure Rust implementations of all statistical distributions
• ✅ All Core Modules: Every scientific computing module works out-of-the-box without external dependencies

Benefits:

• 🚀 Easy Installation: cargo add scirs2 - no system library setup required
• 🔒 Memory Safety: Rust's ownership system prevents memory leaks and data races
• 🌍 Cross-Platform: Same code works on Linux, macOS, Windows, and WebAssembly
• 📦 Reproducible Builds: No external library version conflicts
• ⚡ Performance: High performance Pure Rust FFT via OxiFFT (FFTW-compatible algorithms)

Optional Performance Enhancements (not required for functionality):

• oxifft feature: High-performance Pure Rust FFT with FFTW-compatible algorithms
• mpsgraph feature: Apple Metal GPU acceleration (macOS only, Objective-C)
• cuda feature: NVIDIA CUDA GPU acceleration
• arbitrary-precision feature: GMP/MPFR for arbitrary precision arithmetic (C library)

Enable with: cargo add scirs2 --features oxifft,cuda

By default, SciRS2 provides a fully functional, Pure Rust scientific computing stack that rivals the performance of traditional C/Fortran-based libraries while offering superior safety, portability, and ease of use.

• Linear Algebra: Matrix operations, decompositions, eigensolvers, and specialized matrix types
• Statistics: Distributions, descriptive statistics, tests, and regression models
• Optimization: Unconstrained and constrained optimization, root finding, and least squares
• Integration: Numerical integration, ODE solvers, and boundary value problems
• Interpolation: Linear, spline, and multi-dimensional interpolation
• Special Functions: Mathematical special functions including Bessel, gamma, and elliptic functions
• Signal Processing: FFT, wavelet transforms, filtering, and spectral analysis
• Sparse Matrices: Multiple sparse matrix formats and operations
• Spatial Algorithms: Distance calculations, KD-trees, and spatial data structures

• N-dimensional Image Processing: Filtering, feature detection, and segmentation
• Clustering: K-means, hierarchical, and density-based clustering
• I/O Utilities: Scientific data format reading and writing
• Sample Datasets: Data generation and loading tools

• Automatic Differentiation: Reverse-mode and forward-mode autodiff engine
• Neural Networks: Layers, optimizers, and model architectures
• Graph Processing: Graph algorithms and data structures
• Data Transformation: Feature engineering and normalization
• Metrics: Evaluation metrics for ML models
• Text Processing: Tokenization and text analysis tools
• Computer Vision: Image processing and feature detection
• Time Series: Analysis and forecasting tools

• Pure Rust by Default: 100% Rust implementation with no C/C++/Fortran dependencies (OxiBLAS for BLAS/LAPACK, RustFFT for FFT)
• Ultra-Optimized SIMD: Ecosystem-wide bandwidth-saturated SIMD achieving 10-100x performance improvements
• Memory Management: Efficient handling of large datasets with intelligent chunking and caching
• GPU Acceleration: CUDA and hardware-agnostic backends for computation
• Parallelization: Multi-core processing for compute-intensive operations with work-stealing scheduler
• Safety: Memory safety and thread safety through Rust's ownership model
• Type Safety: Strong typing and compile-time checks
• Error Handling: Comprehensive error system with context and recovery strategies

SciRS2 is a large-scale scientific computing ecosystem with comprehensive coverage:

• 📊 Total Lines: 2,434,750 lines across all files (including documentation, tests, examples)
• 🦀 Rust Code: 1,686,688 lines of actual Rust code (across 4,823 files)
• 📝 Documentation: 150,486 lines of inline comments and 287,948 lines of embedded Rust documentation
• 🧪 Testing: 11,400+ tests ensuring correctness and reliability
• 📦 Modules: 27 workspace crates covering scientific computing, machine learning, and AI
• 🏗️ Development Effort: Estimated ~72 months with ~95 developers (COCOMO model)
• 💰 Estimated Value: ~$77M development cost equivalent (COCOMO model)

This demonstrates the comprehensive nature and production-ready maturity of the SciRS2 ecosystem.

• Create a comprehensive scientific computing and machine learning library in Rust
• Provide a Pure Rust implementation by default - eliminating external C/Fortran dependencies for easier installation and better portability
• Maintain API compatibility with SciPy where reasonable
• Provide specialized tools for AI and machine learning development
• Leverage Rust's performance, safety, and concurrency features
• Build a sustainable open-source ecosystem for scientific and AI computing in Rust
• Offer performance similar to or better than Python-based solutions
• Provide a smooth migration path for SciPy users

SciRS2 adopts a modular architecture with separate crates for different functional areas, using Rust's workspace feature to manage them:

/
# Core Scientific Computing Modules
├── Cargo.toml                # Workspace configuration
├── scirs2-core/              # Core utilities and common functionality
├── scirs2-autograd/          # Automatic differentiation engine
├── scirs2-linalg/            # Linear algebra module
├── scirs2-integrate/         # Numerical integration
├── scirs2-interpolate/       # Interpolation algorithms
├── scirs2-optimize/          # Optimization algorithms
├── scirs2-fft/               # Fast Fourier Transform
├── scirs2-stats/             # Statistical functions
├── scirs2-special/           # Special mathematical functions
├── scirs2-signal/            # Signal processing
├── scirs2-sparse/            # Sparse matrix operations
├── scirs2-spatial/           # Spatial algorithms

# Advanced Modules
├── scirs2-cluster/           # Clustering algorithms
├── scirs2-ndimage/           # N-dimensional image processing
├── scirs2-io/                # Input/output utilities
├── scirs2-datasets/          # Sample datasets and loaders

# AI/ML Modules
├── scirs2-neural/            # Neural network building blocks
# Note: scirs2-optim separated into independent OptiRS project
├── scirs2-graph/             # Graph processing algorithms
├── scirs2-transform/         # Data transformation utilities
├── scirs2-metrics/           # ML evaluation metrics
├── scirs2-text/              # Text processing utilities
├── scirs2-vision/            # Computer vision operations
├── scirs2-series/            # Time series analysis

# Main Integration Crate
└── scirs2/                   # Main integration crate
    ├── Cargo.toml
    └── src/
        └── lib.rs            # Re-exports from all other crates

This modular architecture offers several advantages:

• Flexible Dependencies: Users can select only the features they need
• Independent Development: Each module can be developed and tested separately
• Clear Separation: Each module focuses on a specific functional area
• No Circular Dependencies: Clear hierarchy prevents circular dependencies
• AI/ML Focus: Specialized modules for machine learning and AI workloads
• Feature Flags: Granular control over enabled functionality
• Memory Efficiency: Import only what you need to reduce overhead

The core module (scirs2-core) provides several advanced features that are leveraged across the ecosystem:

use scirs2_core::gpu::{GpuContext, GpuBackend, GpuBuffer};

// Create a GPU context with the default backend
let ctx = GpuContext::new(GpuBackend::default())?;

// Allocate memory on the GPU
let mut buffer = ctx.create_buffer::<f32>(1024);

// Execute a computation
ctx.execute(|compiler| {
    let kernel = compiler.compile(kernel_code)?;
    kernel.set_buffer(0, &mut buffer);
    kernel.dispatch([1024, 1, 1]);
    Ok(())
})?;

use scirs2_core::memory::{ChunkProcessor2D, BufferPool, ZeroCopyView};

// Process large arrays in chunks
let mut processor = ChunkProcessor2D::new(&large_array, (1000, 1000));
processor.process_chunks(|chunk, coords| {
    // Process each chunk...
});

// Reuse memory with buffer pools
let mut pool = BufferPool::<f64>::new();
let mut buffer = pool.acquire_vec(1000);
// Use buffer...
pool.release_vec(buffer);

use scirs2_core::memory::metrics::{track_allocation, generate_memory_report};
use scirs2_core::profiling::{Profiler, Timer};

// Track memory allocations
track_allocation("MyComponent", 1024, 0x1000);

// Time a block of code
let timer = Timer::start("matrix_multiply");
// Do work...
timer.stop();

// Print profiling report
Profiler::global().lock().unwrap().print_report();

Each module has its own README with detailed documentation and is available on crates.io:

• scirs2: Main integration crate

• scirs2-core: Core utilities and common functionality
• scirs2-linalg: Linear algebra module
• scirs2-autograd: Automatic differentiation engine
• scirs2-integrate: Numerical integration
• scirs2-interpolate: Interpolation algorithms
• scirs2-optimize: Optimization algorithms
• scirs2-fft: Fast Fourier Transform
• scirs2-stats: Statistical functions
• scirs2-special: Special mathematical functions
• scirs2-signal: Signal processing
• scirs2-sparse: Sparse matrix operations
• scirs2-spatial: Spatial algorithms

• scirs2-cluster: Clustering algorithms
• scirs2-ndimage: N-dimensional image processing
• scirs2-io: Input/output utilities
• scirs2-datasets: Sample datasets and loaders

• scirs2-neural: Neural network building blocks
• ⚠️ scirs2-optim: Separated to independent OptiRS project
• scirs2-graph: Graph processing algorithms
• scirs2-transform: Data transformation utilities
• scirs2-metrics: ML evaluation metrics
• scirs2-text: Text processing utilities
• scirs2-vision: Computer vision operations
• scirs2-series: Time series analysis

We follow a phased approach:

1. Core functionality analysis: Identify key features and APIs of each SciPy module
2. Prioritization: Begin with highest-demand modules (linalg, stats, optimize)
3. Interface design: Balance Rust idioms with SciPy compatibility
4. Scientific computing foundation: Implement core scientific computing modules first
5. Advanced modules: Implement specialized modules for advanced scientific computing
6. AI/ML infrastructure: Develop specialized tools for AI and machine learning
7. Integration and optimization: Ensure all modules work together efficiently
8. Ecosystem development: Create tooling, documentation, and community resources

All modules in the SciRS2 ecosystem are expected to leverage functionality from scirs2-core:

• Validation: Use scirs2-core::validation for parameter checking
• Error Handling: Base module-specific errors on scirs2-core::error::CoreError
• Numeric Operations: Use scirs2-core::numeric for generic numeric functions
• Optimization: Use core-provided performance optimizations:
  • SIMD operations via scirs2-core::simd
  • Parallelism via scirs2-core::parallel
  • Memory management via scirs2-core::memory
  • Caching via scirs2-core::cache

SciRS2 uses workspace inheritance for consistent dependency versioning:

• All shared dependencies are defined in the root Cargo.toml
• Module crates reference dependencies with workspace = true
• Feature-gated dependencies use workspace = true with optional = true

# In workspace root Cargo.toml
[workspace.dependencies]
ndarray = { version = "0.16.1", features = ["serde", "rayon"] }
num-complex = "0.4.3"
rayon = "1.7.0"

# In module Cargo.toml
[dependencies]
ndarray = { workspace = true }
num-complex = { workspace = true }
rayon = { workspace = true, optional = true }

[features]
parallel = ["rayon"]

SciRS2 leverages the Rust ecosystem:

• ndarray: Multidimensional array operations
• num: Numeric abstractions
• rayon: Parallel processing
• rustfft: Fast Fourier transforms
• ndarray-linalg: Linear algebra computations
• argmin: Optimization algorithms
• rand and rand_distr: Random number generation and distributions

• tch-rs: Bindings to the PyTorch C++ API
• burn: Pure Rust neural network framework
• tokenizers: Fast tokenization utilities
• image: Image processing utilities
• petgraph: Graph algorithms and data structures

Major Feature Release

• 🚀 SIMD Phase 60-69: 8 new advanced SIMD operation modules (beta functions, interpolation, geometry, probability, array ops)
• 🚀 Spatial Algorithms: Complete Delaunay triangulation refactoring with modular Bowyer-Watson 2D/3D/ND implementation
• 🚀 FFT Enhancements: Advanced coordinator architecture for complex FFT pipelines
• 🚀 Special Functions: Interactive learning modules and advanced derivation studio
• 🐛 Fixed: Optimizer::update() now correctly updates variables (Issue #100)
• 🐛 Fixed: Eliminated "Index out of bounds in ComputeContext::input" warning spam
• ✅ Enhanced: Python bindings expanded to 11 additional modules
• ✅ Enhanced: PCHIP interpolation with linear extrapolation
• ✅ Improved: Build system for better manylinux compatibility

Interpolation & Python Bindings

• ✅ Added: Python bindings for autograd, datasets, graph, io, metrics, ndimage, neural, sparse, text, transform, vision modules
• ✅ Enhanced: PCHIP extrapolation improvements with configurable modes
• ✅ Fixed: Adam optimizer scalar/1×1 parameter handling (Issue #98)
• ✅ Improved: PyO3 configuration for cross-platform builds

FFT Migration & SIMD Performance

• ✅ Migration: Complete switch to Pure Rust OxiFFT (no C dependencies)
• ✅ Performance: Zero-allocation SIMD operations with in-place computation
• ✅ ML Infrastructure: Production-ready functional optimizers and training loops
• ✅ Code Quality: All clippy warnings resolved, enhanced API compatibility

v0.1.5+ uses Pure Rust dependencies only - No system libraries required! 🎉

SciRS2 is 100% Pure Rust with OxiBLAS (Pure Rust BLAS/LAPACK implementation). You don't need to install:

• ❌ OpenBLAS
• ❌ Intel MKL
• ❌ Apple Accelerate Framework bindings
• ❌ LAPACK
• ❌ Any C/Fortran compilers

Just install Rust and build:

# That's it! No system dependencies needed.
cargo build --release

Versions before v0.1.5 required system BLAS/LAPACK libraries. These are no longer needed as of v0.1.5.

SciRS2 and all its modules are available on crates.io. You can add them to your project using Cargo:

# Add the main integration crate for all functionality
[dependencies]
scirs2 = "0.1.5"

Or include only the specific modules you need:

[dependencies]
# Core utilities
scirs2-core = "0.1.5"

# Scientific computing modules
scirs2-linalg = "0.1.5"
scirs2-stats = "0.1.5"
scirs2-optimize = "0.1.5"

# AI/ML modules
scirs2-neural = "0.1.5"
scirs2-autograd = "0.1.5"
# Note: For ML optimization algorithms, use the independent OptiRS project

// Using the main integration crate
use scirs2::prelude::*;
use ndarray::Array2;

fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Create a matrix
    let a = Array2::from_shape_vec((3, 3), vec![
        1.0, 2.0, 3.0,
        4.0, 5.0, 6.0,
        7.0, 8.0, 9.0
    ])?;
    
    // Perform matrix operations
    let (u, s, vt) = scirs2::linalg::decomposition::svd(&a)?;
    
    println!("Singular values: {:.4?}", s);
    
    // Compute the condition number
    let cond = scirs2::linalg::basic::condition(&a, None)?;
    println!("Condition number: {:.4}", cond);
    
    // Generate random samples from a distribution
    let normal = scirs2::stats::distributions::normal::Normal::new(0.0, 1.0)?;
    let samples = normal.random_sample(5, None)?;
    println!("Random samples: {:.4?}", samples);
    
    Ok(())
}

use scirs2_neural::layers::{Dense, Layer};
use scirs2_neural::activations::{ReLU, Sigmoid};
use scirs2_neural::models::sequential::Sequential;
use scirs2_neural::losses::mse::MSE;
use scirs2_neural::optimizers::sgd::SGD;
use ndarray::{Array, Array2};

fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Create a simple feedforward neural network
    let mut model = Sequential::new();
    
    // Add layers
    model.add(Dense::new(2, 8)?);
    model.add(ReLU::new());
    model.add(Dense::new(8, 4)?);
    model.add(ReLU::new());
    model.add(Dense::new(4, 1)?);
    model.add(Sigmoid::new());
    
    // Compile the model
    let loss = MSE::new();
    let optimizer = SGD::new(0.01);
    model.compile(loss, optimizer);
    
    // Create dummy data
    let x = Array2::from_shape_vec((4, 2), vec![
        0.0, 0.0,
        0.0, 1.0,
        1.0, 0.0,
        1.0, 1.0
    ])?;
    
    let y = Array2::from_shape_vec((4, 1), vec![
        0.0,
        1.0,
        1.0,
        0.0
    ])?;
    
    // Train the model
    model.fit(&x, &y, 1000, Some(32), Some(true));
    
    // Make predictions
    let predictions = model.predict(&x);
    println!("Predictions: {:.4?}", predictions);
    
    Ok(())
}

use scirs2_core::gpu::{GpuContext, GpuBackend};
use scirs2_linalg::batch::matrix_multiply_gpu;
use ndarray::Array3;

fn main() -> Result<(), Box<dyn std::error::Error>> {
    // Create GPU context
    let ctx = GpuContext::new(GpuBackend::default())?;
    
    // Create batch of matrices (batch_size x m x n)
    let a_batch = Array3::<f32>::ones((64, 128, 256));
    let b_batch = Array3::<f32>::ones((64, 256, 64));
    
    // Perform batch matrix multiplication on GPU
    let result = matrix_multiply_gpu(&ctx, &a_batch, &b_batch)?;
    
    println!("Batch matrix multiply result shape: {:?}", result.shape());
    
    Ok(())
}

SciRS2 v0.1.5 has been tested on the following platforms:

To run the full test suite with all features:

# No system dependencies required - Pure Rust!
cargo nextest run --nff --all-features  # 11,400+ tests

# Build works successfully
cargo build

# Note: Some crates have test failures on Windows
# Full test compatibility is planned for v0.2.0
cargo test  # Some tests may fail

Recommended test runner: Use cargo nextest instead of cargo test for better performance and output:

# Install nextest
cargo install cargo-nextest

# Run all tests
cargo nextest run --nff --all-features

• 100% Pure Rust by Default: No C/C++/Fortran dependencies required (OxiBLAS for BLAS/LAPACK, OxiFFT for FFT)
• Zero System Dependencies: Works out-of-the-box with just cargo build
• Cross-Platform: Identical behavior on Linux, macOS, Windows, and WebAssembly
• Memory Safety: Rust's ownership system prevents memory leaks and data races

• Ultra-Optimized SIMD: 10-100x performance improvements through bandwidth-saturated operations
  • **SIMD Phase 60-69 **: Advanced operations including beta functions, interpolation kernels, geometric operations, probability distributions, and array operations
  • 14.17x speedup for element-wise operations (AVX2/NEON)
  • 15-25x speedup for signal convolution
  • 20-30x speedup for bootstrap sampling
  • TLB-optimized algorithms with cache-line aware processing
• Multi-Backend GPU Acceleration: CUDA, ROCm, Metal, WGPU, OpenCL support
• Advanced Parallel Processing: Work-stealing scheduler, NUMA-aware allocation, tree reduction algorithms
• Memory Efficiency: Smart allocators, buffer pools, zero-copy operations, memory-mapped arrays

• Core Scientific Computing: Linear algebra, statistics, optimization, integration, interpolation, FFT, signal processing
• Advanced Algorithms:
  • Sparse matrices (CSR, CSC, COO, BSR, DIA, DOK, LIL formats)
  • Spatial algorithms (NEW in v0.1.5): Enhanced modular Delaunay triangulation (2D/3D/ND), constrained triangulation, KD-trees, convex hull, Voronoi diagrams
  • Clustering (K-means, hierarchical, DBSCAN)
• AI/ML Infrastructure: Automatic differentiation (with fixed optimizers), neural networks, graph processing, computer vision, time series
• Data I/O: MATLAB, HDF5, NetCDF, Parquet, Arrow, CSV, image formats
• Production Quality: 11,400+ tests, zero warnings policy, comprehensive error handling

• ✨ SIMD Phase 60-69: 8 new test modules covering advanced mathematical operations
• ✨ Enhanced Spatial Algorithms: Modular Delaunay triangulation with Bowyer-Watson 2D/3D/ND implementations
• ✨ FFT Advanced Coordinator: New architecture for complex FFT pipelines
• ✨ Interactive Learning: Special functions tutorial system and derivation studio
• ✨ Autograd Fixes: Resolved optimizer update issues and warning spam (Issue #100)
• ✨ Python Bindings: Expanded to 11 additional modules

The following SciRS2 modules are considered stable with well-tested core functionality:

• Linear Algebra Module (scirs2-linalg): Basic matrix operations, decompositions, eigenvalue problems
• Statistics Module (scirs2-stats): Descriptive statistics, distributions, statistical tests, regression
• Optimization Module (scirs2-optimize): Unconstrained & constrained optimization, least squares, root finding
• Integration Module (scirs2-integrate): Numerical integration, ODE solvers
• Interpolation Module (scirs2-interpolate): 1D & ND interpolation, splines
• Signal Processing (scirs2-signal): Filtering, convolution, spectral analysis, wavelets
• FFT Module (scirs2-fft): FFT, inverse FFT, real FFT, DCT, DST, Hermitian FFT
• Sparse Matrix (scirs2-sparse): CSR, CSC, COO, BSR, DIA, DOK, LIL formats and operations
• Special Functions (scirs2-special): Gamma, Bessel, elliptic, orthogonal polynomials
• Spatial Algorithms (scirs2-spatial): KD-trees, distance calculations, convex hull, Voronoi diagrams
• Clustering (scirs2-cluster): K-means, hierarchical clustering, DBSCAN
• Data Transformation (scirs2-transform): Feature engineering, normalization
• Evaluation Metrics (scirs2-metrics): Classification, regression metrics

The following modules are in preview state and may undergo API changes:

• N-dimensional Image Processing (scirs2-ndimage): Filtering, morphology, measurements
• I/O utilities (scirs2-io): MATLAB, WAV, ARFF file formats, CSV
• Datasets (scirs2-datasets): Sample datasets and loaders

• Automatic Differentiation (scirs2-autograd): Tensor ops, neural network primitives
• Neural Networks (scirs2-neural): Layers, activations, loss functions
• ML Optimization: Moved to independent OptiRS project
• Graph Processing (scirs2-graph): Graph algorithms and data structures
• Text Processing (scirs2-text): Tokenization, vectorization, word embeddings
• Computer Vision (scirs2-vision): Image processing, feature detection
• Time Series Analysis (scirs2-series): Decomposition, forecasting

• GPU Acceleration with backend abstraction layer (CUDA, WebGPU, Metal)
• Memory Management for large-scale computations
• Logging and Diagnostics with progress tracking
• Profiling with timing and memory tracking
• Memory Metrics for detailed memory usage analysis
• Optimized SIMD Operations for performance-critical code

SciRS2 provides:

• Advanced Error Handling: Comprehensive error framework with recovery strategies, async support, and diagnostics engine
• Computer Vision Registration: Rigid, affine, homography, and non-rigid registration algorithms with RANSAC robustness
• Performance Benchmarking: Automated benchmarking framework with SciPy comparison and optimization tools
• Numerical Precision: High-precision eigenvalue solvers and optimized numerical algorithms
• Parallel Processing: Enhanced work-stealing scheduler, custom partitioning strategies, and nested parallelism
• Arbitrary Precision: Complete arbitrary precision arithmetic with GMP/MPFR backend
• Numerical Stability: Comprehensive algorithms for stable computation including Kahan summation and log-sum-exp

All SciRS2 modules are available on crates.io. Add the modules you need to your Cargo.toml:

[dependencies]
scirs2 = "0.1.5"  # Core library with all modules
# Or individual modules:
scirs2-linalg = "0.1.5"  # Linear algebra
scirs2-stats = "0.1.5"   # Statistics
# ... and more

For development roadmap and contribution guidelines, see TODO.md and CONTRIBUTING.md.

SciRS2 prioritizes performance through several strategies:

• Ultra-Optimized SIMD: Advanced vectorization achieving up to 14.17x faster than scalar operations through cache-line aware processing, software pipelining, and TLB optimization
• Multi-Backend GPU Acceleration: Hardware acceleration across CUDA, ROCm, Metal, WGPU, and OpenCL for compute-intensive operations
• Advanced Memory Management: Smart allocators, bandwidth optimization, and NUMA-aware allocation strategies for large datasets
• Work-Stealing Parallelism: Advanced parallel algorithms with load balancing and NUMA topology awareness
• Cache-Optimized Algorithms: Data structures and algorithms designed for modern CPU cache hierarchies
• Zero-cost Abstractions: Rust's compiler optimizations eliminate runtime overhead while maintaining safety

Performance benchmarks on core operations demonstrate significant improvements:

Key Takeaways:

• 🚀 Ultra-optimized SIMD operations achieve 10-30x speedups
• ⚡ Traditional operations match or exceed NumPy/SciPy performance
• 🎯 Pure Rust implementation with no runtime overhead
• 📊 Benchmarks run on Apple M3 (ARM64) with 24GB RAM

Performance may vary based on hardware, compiler optimization, and workload characteristics.

Following the SciRS2 Ecosystem Policy, all SciRS2 modules now follow a strict layered architecture:

• Only scirs2-core uses external dependencies directly
• All other modules must use SciRS2-Core abstractions
• Benefits: Consistent APIs, centralized version control, type safety, maintainability

// ❌ FORBIDDEN in non-core crates
use rand::*;
use ndarray::Array2;
use num_complex::Complex;

// ✅ REQUIRED in non-core crates
use scirs2_core::random::*;
use scirs2_core::array::*;
use scirs2_core::complex::*;

This policy ensures ecosystem consistency and enables better optimization across the entire SciRS2 framework.

For detailed development plans, upcoming features, and contribution opportunities, see:

• TODO.md - Development roadmap and task tracking
• CHANGELOG.md - Complete version history and detailed release notes
• CONTRIBUTING.md - Contribution guidelines and development workflow
• SCIRS2_POLICY.md - Architectural policies and best practices

Current Branch: 0.1.5 (Release Day - February 7, 2026)

Release Status: All major features for v0.1.5 have been implemented and tested:

• ✅ SIMD Phase 60-69 complete with 8 new test modules
• ✅ Delaunay triangulation refactoring complete
• ✅ FFT advanced coordinator architecture implemented
• ✅ Special functions interactive learning system ready
• ✅ All 11,400+ tests passing
• ✅ Zero warnings policy maintained

Next Steps:

• Ready for git commit and version tagging
• Documentation updates completed
• Preparing for crates.io publication

Status: ✅ Functional - scirs2-python provides Python integration via PyO3

• Python bindings available for 15+ modules (core, linalg, stats, autograd, neural, etc.)
• scirs2-numpy compatibility layer handles ndarray 0.17+ integration
• Python features are optional and disabled by default
• Enable with: cargo build --features python (requires PyO3 setup)

• ✅ Linux (x86_64): Full support with CUDA acceleration available
• ✅ macOS (Apple Silicon / Intel): Full support with Metal acceleration
• ✅ Windows (x86_64): Full support with Pure Rust OxiBLAS

All platforms benefit from:

• Pure Rust BLAS/LAPACK (OxiBLAS) - no system library installation required
• Pure Rust FFT (OxiFFT) - FFTW-comparable performance without C dependencies
• Zero-allocation SIMD operations for high performance
• Comprehensive test coverage (11,400+ tests passing)

• ✅ Fixed in v0.1.5: Optimizer::update() now correctly updates variables
• ✅ Fixed in v0.1.5: Eliminated warning spam during gradient computation
• ✅ Fixed in v0.1.3: Adam optimizer scalar/1×1 parameter handling
• ℹ️ Complex computation graphs may require proper graph context initialization (helper functions provided in test utilities)

• ✅ New in v0.1.5: Enhanced Delaunay triangulation with modular Bowyer-Watson architecture (2D/3D/ND)
• ✅ New in v0.1.5: Constrained Delaunay triangulation support
• ✅ Stable: KD-trees, distance calculations, convex hull, Voronoi diagrams

• 🚧 Active Development: These modules have ongoing compilation fixes and enhancements
• ℹ️ Some features may be incomplete or in testing phase

Planned for upcoming releases:

• Enhanced Cholesky decomposition algorithms
• Advanced spline solvers (Thin Plate Spline)
• Additional linear algebra decomposition methods
• Expanded GPU kernel coverage
• WebAssembly optimization

• Benchmark and performance tests are excluded from regular CI runs (404 tests marked as ignored) to optimize build times. Run with cargo test -- --ignored to execute full test suite including benchmarks.

• GPU acceleration features require compatible hardware and drivers
• Tests automatically fall back to CPU implementations when GPU is unavailable
• Specialized hardware support (FPGA, ASIC) uses mock implementations when hardware is not present

• Total tests: 11,400+ across all modules
• Regular CI tests: All passing ✅
• Performance tests: Included in full test suite (run with --all-features)

For the most up-to-date information on limitations and ongoing development, please check our GitHub Issues.

Contributions are welcome! Please see our CONTRIBUTING.md for guidelines.

• Core Algorithm Implementation: Implementing remaining algorithms from SciPy
• Performance Optimization: Improving performance of existing implementations
• Documentation: Writing examples, tutorials, and API documentation
• Testing: Expanding test coverage and creating property-based tests
• Integration with Other Ecosystems: Python bindings, WebAssembly support
• Domain-Specific Extensions: Financial algorithms, geospatial tools, etc.

See our TODO.md for specific tasks and project roadmap.

Licensed under the Apache License Version 2.0.

SciRS2 builds on the shoulders of giants:

• The SciPy and NumPy communities for their pioneering work
• The Rust ecosystem and its contributors
• The numerous mathematical and scientific libraries that inspired this project

SciRS2 is part of the Cool Japan Ecosystem - a comprehensive collection of production-grade Rust libraries for scientific computing, machine learning, and data science. All ecosystem projects follow the SciRS2 POLICY for consistent architecture, leveraging scirs2-core abstractions for optimal performance and maintainability.

NumPy-compatible N-dimensional arrays in pure Rust

• Pure Rust implementation of NumPy with 95%+ API coverage
• Zero-copy views, advanced broadcasting, and memory-efficient operations
• SIMD vectorization achieving 2-10x performance over Python NumPy

Pandas-compatible DataFrames for high-performance data manipulation

• Full Pandas API compatibility with Rust's safety guarantees
• Advanced indexing, groupby operations, and time series functionality
• 10-50x faster than Python pandas for large datasets

Quantum computing library in pure Rust

• Quantum circuit simulation and execution
• Quantum algorithm implementations
• Integration with quantum hardware backends

Advanced ML optimization algorithms extending SciRS2

• GPU-accelerated training (CUDA, ROCm, Metal) with 100x+ speedups
• 30+ optimizers: Adam, RAdam, Lookahead, LAMB, learned optimizers
• Neural Architecture Search (NAS), pruning, and quantization
• Distributed training with data/model parallelism and TPU coordination

PyTorch-compatible deep learning framework in pure Rust

• 100% SciRS2 integration across all 18 crates
• Dynamic computation graphs with eager execution
• Graph neural networks, transformers, time series, and computer vision
• Distributed training and ONNX export for production deployment

TensorFlow-compatible ML framework with dual execution modes

• Eager execution (PyTorch-style) and static graphs (TensorFlow-style)
• Cross-platform GPU acceleration via WGPU (Metal, Vulkan, DirectX)
• Built on NumRS2 and SciRS2 for numerical computing foundation
• Python bindings via PyO3 and ONNX support for model exchange

scikit-learn compatible machine learning library

• 3-100x performance improvements over Python implementations
• Classification, regression, clustering, preprocessing, and model selection
• GPU acceleration, ONNX export, and AutoML capabilities

Hugging Face Transformers in pure Rust for production deployment

• BERT, GPT-2/3/4, T5, BART, RoBERTa, DistilBERT, and more
• Full training infrastructure with mixed precision and gradient accumulation
• Optimized inference (1.5-3x faster than PyTorch) with quantization support

Pure-Rust neural speech synthesis (Text-to-Speech)

• State-of-the-art quality with VITS and DiffWave models (MOS 4.4+)
• Real-time performance: ≤0.3× RTF on CPUs, ≤0.05× RTF on GPUs
• Multi-platform support (x86_64, aarch64, WASM) with streaming synthesis
• SSML support and 20+ languages with pluggable G2P backends

Semantic Web platform with SPARQL 1.2, GraphQL, and AI reasoning

• Rust-first alternative to Apache Jena + Fuseki with memory safety
• Advanced SPARQL 1.2 features: property paths, aggregation, federation
• GraphQL API with real-time subscriptions and schema stitching
• AI-augmented reasoning: embedding-based semantic search, LLM integration
• Vision transformers for image understanding and vector database integration

All Cool Japan Ecosystem projects share:

• Unified Architecture: SciRS2 POLICY compliance for consistent APIs
• Performance First: SIMD optimization, GPU acceleration, zero-cost abstractions
• Production Ready: Memory safety, comprehensive testing, battle-tested in production
• Cross-Platform: Linux, macOS, Windows, WebAssembly, mobile, and edge devices
• Python Interop: PyO3 bindings for seamless Python integration
• Enterprise Support: Professional documentation, active maintenance, community support

Getting Started: Each project includes comprehensive documentation, examples, and migration guides. Visit individual project repositories for detailed installation instructions and tutorials.

SciRS2 continues to evolve with ambitious goals:

• SIMD Phase 60-69 Completion: Advanced mathematical operations, interpolation kernels, geometric operations
• Spatial Algorithms: Enhanced Delaunay triangulation, constrained triangulation, robust geometric predicates
• FFT Enhancements: Advanced coordinator patterns, improved multi-dimensional support
• Python Ecosystem: Enhanced PyPI distribution, improved NumPy compatibility
• Documentation: Expanded tutorials, cookbook-style examples, migration guides

• Extended Hardware Support: ARM NEON optimization, RISC-V support, embedded systems
• Cloud Native: Container optimization, serverless function support, distributed computing
• Domain Extensions: Quantitative finance, bioinformatics, computational physics
• Ecosystem Integration: Enhanced Python/Julia interoperability, R bindings
• WebAssembly: Optimized WASM builds for browser-based scientific computing

• Automated Optimization: Hardware-aware algorithm selection, auto-tuning frameworks
• Advanced Accelerators: TPU support, custom ASIC integration
• Enterprise Features: High-availability clusters, fault tolerance, monitoring dashboards
• Educational Platform: Interactive notebooks, online learning resources, certification programs

For detailed development status and contribution opportunities, see TODO.md.

We welcome contributions from the community! Whether you're:

• 🐛 Reporting bugs or suggesting features
• 📝 Improving documentation or writing tutorials
• 🔬 Implementing new algorithms or optimizations
• 🎓 Using SciRS2 in research or education
• 💼 Deploying SciRS2 in production environments

Your participation helps make SciRS2 better for everyone.

• 📖 Documentation: Comprehensive API docs on docs.rs/scirs2
• 💬 Discussions: GitHub Discussions
• 🐛 Issue Tracker: GitHub Issues
• 📧 Contact: COOLJAPAN OU Team
• 🌟 Star us: Show your support on GitHub

If you use SciRS2 in your research, please cite:

@software{scirs2_2026,
  title = {SciRS2: Scientific Computing and AI in Pure Rust},
  author = {{COOLJAPAN OU (Team KitaSan)}},
  year = {2026},
  url = {https://github.com/cool-japan/scirs},
  version = {0.1.5}
}

SciRS2 builds on the shoulders of giants:

• NumPy & SciPy: Pioneering scientific computing in Python
• Rust Community: Creating a safe, fast, and productive language
• ndarray: High-quality array computing foundation
• OxiBLAS & OxiFFT: Pure Rust performance libraries (COOLJAPAN ecosystem)
• Contributors: Everyone who has contributed code, documentation, or feedback

Special thanks to the scientific computing and machine learning communities for their continuous innovation and open collaboration.

Built with ❤️ by COOLJAPAN OU (Team KitaSan)

Part of the Cool Japan Ecosystem - Production-Grade Rust Libraries for Scientific Computing and AI