SciRS2 Core

Production-Ready Scientific Computing Core for Rust

🎯 SciRS2 Core v0.1.0 (Released December 29, 2025) - Production-ready foundation providing comprehensive abstractions for the entire SciRS2 ecosystem with ultra-performance SIMD, multi-backend GPU support, and advanced parallel processing.

🚀 Quick Start

[dependencies]
scirs2-core = { version = "0.1.2", features = ["validation", "simd", "parallel"] }

use scirs2_core::prelude::*;

// Create and validate data
let data = array![[1.0, 2.0], [3.0, 4.0]];
check_finite(&data, "input_matrix")?;

// Perform operations with automatic optimization
let normalized = normalize_matrix(&data)?;
let result = parallel_matrix_multiply(&normalized, &data.t())?;
# Ok::<(), Box<dyn std::error::Error>>(())

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🎯 Current Release: stable (December 29, 2025)

✅ Major Achievements

SciRS2 POLICY Framework (COMPLETE)

• ✅ Comprehensive ecosystem policy document (SCIRS2_POLICY.md)
• ✅ Layered abstraction architecture with core-only external dependencies
• ✅ Mandatory scirs2-core module usage across all non-core crates
• ✅ Migration guide and module boundaries documentation
• ✅ Prelude module for common imports (scirs2_core::prelude)

Ultra-Performance SIMD Optimization (COMPLETE)

• ✅ 14.17x speedup over scalar operations with bandwidth-saturated processing
• ✅ NEW: 1.4x-4.5x speedup over standard SIMD with ultra-optimizations (Dec 2025)
  • Addition: 3.38x, Multiplication: 3.01x, Dot Product: 3.93x, Sum: 4.04x
  • Multiple accumulators (4-8) for instruction-level parallelism
  • Aggressive loop unrolling (8-way) with memory prefetching
  • FMA instructions and alignment-aware processing
• ✅ Cache-line aware processing with non-temporal stores
• ✅ Software pipelining with register blocking
• ✅ TLB-optimized memory access patterns
• ✅ Adaptive selector combining all optimization techniques

Ecosystem-Wide SIMD Integration (COMPLETE)

• ✅ Signal processing: 15-25x speedup (convolution, filtering)
• ✅ Autograd: Thread-safe environments with PyTorch-compatible APIs
• ✅ FFT/Spectral: 12-25x speedup (DCT/DST, FrFT, FHT)
• ✅ Statistics: 20-40x speedup (moments, Monte Carlo, bootstrap, QMC)

GPU Kernel Infrastructure (COMPLETE)

• ✅ Multi-backend GPU support (CUDA, ROCm, Metal, WGPU, OpenCL)
• ✅ Comprehensive elementwise operation kernels
• ✅ Advanced optimization kernels (Adam, SGD, RMSprop, AdaGrad)
• ✅ Utility kernels (Reduce, Scan, MatMul, Transpose)
• ✅ Backend-specific implementations with automatic fallback

Advanced Core Infrastructure (COMPLETE)

• ✅ Tree reduction algorithms with configurable strategies
• ✅ Parallel scan operations (inclusive/exclusive)
• ✅ NUMA-aware processing with topology detection
• ✅ Advanced error recovery with circuit breaker patterns
• ✅ 30+ mathematical constants, 40+ physical constants
• ✅ 10+ specialized chunking strategies
• ✅ Smart allocators and bandwidth optimizers

Documentation and Developer Experience (COMPLETE)

• ✅ Enhanced lib.rs documentation (docs.rs ready)
• ✅ Comprehensive migration guide (18KB) with recipes
• ✅ Module boundaries document (12KB) with clear anti-patterns
• ✅ API reference documentation with extensive examples

Quality Metrics (CURRENT)

• ✅ 100% compilation success across all modules
• ✅ 478/480 tests passing (2 ignored, 0 failed)
• ✅ Zero build warnings with comprehensive linting
• ✅ Cross-platform compatibility (Linux, macOS, Windows)

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🚀 Future Plans

v0.2.0: Performance and Scale (Q1 2026)

• Additional SIMD operations for remaining modules
• Enhanced memory management APIs for large datasets
• Distributed computing support for multi-node computation
• Advanced profiling tools with minimal overhead

1.0 Stable Release (Q4 2026)

• API stability guarantees with semantic versioning
• 95%+ code coverage across all modules
• Enterprise features and commercial support
• Complete documentation suite with tutorials

Post-1.0: Research and Innovation (2027+)

• Quantum computing integration
• Federated learning support
• Advanced distributed computing features

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🏗️ SciRS2 POLICY Framework

SciRS2 Core v0.1.0 provides the SciRS2 Ecosystem Policy that establishes architectural consistency:

🎯 Core Principles

• Layered Architecture: Only scirs2-core uses external dependencies directly
• Unified Abstractions: All other crates use scirs2-core re-exports
• Ecosystem Consistency: Consistent APIs, version control, and type safety

✅ Benefits for Developers

• Consistent APIs: Same interface patterns across all SciRS2 modules
• Version Control: Centralized dependency management
• Type Safety: Unified type system prevents incompatible types
• Performance: Core optimizes all external library usage

📋 Policy Implementation

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

// ✅ REQUIRED in non-core crates
use scirs2_core::random::*;    // Instead of rand::*
use scirs2_core::array::*;     // Instead of ndarray::*
use scirs2_core::complex::*;   // Instead of num_complex::*

See SCIRS2_POLICY.md for complete details.

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✨ Key Features

🔬 Scientific Computing Foundation

• NumPy/SciPy Compatibility: Drop-in replacements for common operations
• ndarray Extensions: Advanced indexing, broadcasting, statistics
• Data Validation: Comprehensive validation system
• Type Safety: Robust numeric type system

⚡ High Performance

• Ultra-Optimized SIMD: Up to 14.17x faster than scalar operations
• Multi-Backend GPU: CUDA, ROCm, Metal, WGPU, OpenCL support
• Parallel Processing: Work-stealing scheduler with NUMA awareness
• Smart Memory: Intelligent allocators and bandwidth optimization

🔧 Production Ready

• Error Handling: Comprehensive error system with recovery
• Observability: Built-in logging, metrics, distributed tracing
• Resource Management: Memory allocation and GPU pooling
• Testing: Property-based testing framework

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📦 Feature Modules

Core Features (Always Available)

// Error handling
use scirs2_core::{CoreError, CoreResult, value_err_loc};

// Mathematical constants
use scirs2_core::constants::{PI, E, SPEED_OF_LIGHT};

// Validation utilities
use scirs2_core::validation::{check_positive, check_shape, check_finite};

Data Validation (validation feature)

use scirs2_core::validation::data::{Validator, ValidationSchema, Constraint};

let schema = ValidationSchema::new()
    .require_field("temperature", DataType::Float64)
    .add_constraint("temperature", Constraint::Range { min: -273.15, max: 1000.0 });

let validator = Validator::new(Default::default())?;
let result = validator.validate(&data, &schema)?;

GPU Acceleration (gpu feature)

use scirs2_core::gpu::{GpuContext, select_optimal_backend};

let backend = select_optimal_backend()?;
let ctx = GpuContext::new(backend)?;

let mut buffer = ctx.create_buffer::<f32>(1_000_000);
buffer.copy_from_host(&host_data);

Memory Management (memory_management feature)

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

// Process large arrays in chunks
let processor = ChunkProcessor2D::new(&large_array, (1000, 1000));
processor.process_chunks(|chunk, coords| {
    println!("Processing chunk at {:?}", coords);
})?;

// Memory pooling
let mut pool = BufferPool::<f64>::new();
let mut buffer = pool.acquire_vec(1000);

SIMD Operations (simd feature)

use scirs2_core::simd::{simd_add, simd_multiply, simd_fused_multiply_add};

let a = vec![1.0f32; 1000];
let b = vec![2.0f32; 1000];
let c = vec![3.0f32; 1000];

let result = simd_fused_multiply_add(&a, &b, &c)?; // (a * b) + c

Parallel Processing (parallel feature)

use scirs2_core::parallel::{parallel_map, parallel_reduce, set_num_threads};

set_num_threads(8);
let results = parallel_map(&data, |&x| expensive_computation(x))?;
let sum = parallel_reduce(&data, 0.0, |acc, &x| acc + x)?;

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🎯 Use Cases

Scientific Data Analysis

use scirs2_core::prelude::*;

// Load and validate
let measurements = load_csv_data("experiment.csv")?;
check_finite(&measurements, "data")?;

// Statistical analysis
let correlation_matrix = calculate_correlation(&measurements)?;
let outliers = detect_outliers(&measurements, 3.0)?;

Machine Learning Pipeline

use scirs2_core::{gpu::*, validation::*, array_protocol::*};

// Validate training data
validate_training_data(&features, &labels, &schema)?;

// GPU-accelerated training
let gpu_config = GPUConfig::high_performance();
let gpu_features = GPUNdarray::new(features, gpu_config);

Large-Scale Data Processing

use scirs2_core::memory::*;

// Memory-efficient processing
let memory_mapped = MemoryMappedArray::<f64>::open("large_dataset.bin")?;
let processor = ChunkProcessor::new(&memory_mapped, ChunkSize::Adaptive);

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🔧 Configuration

Feature Flags

# Minimal scientific computing
scirs2-core = { version = "0.1.2", features = ["validation"] }

# High-performance CPU computing
scirs2-core = { version = "0.1.2", features = ["validation", "simd", "parallel"] }

# GPU-accelerated computing
scirs2-core = { version = "0.1.2", features = ["validation", "gpu", "cuda"] }

# Full-featured development
scirs2-core = { version = "0.1.2", features = ["all"] }

Available Features

Feature	Description	Use Case
validation	Data validation and integrity	All applications
simd	CPU vector acceleration	CPU-intensive computations
parallel	Multi-core processing	Large datasets
gpu	GPU acceleration	GPU computing
cuda	NVIDIA CUDA backend	NVIDIA GPUs
memory_management	Advanced memory utilities	Large-scale apps
array_protocol	Extensible array system	Framework development
logging	Structured logging	Production deployment
profiling	Performance monitoring	Optimization
all	All stable features	Development

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📊 Performance

Ultra-Optimized SIMD Performance (targeting 80-90% memory bandwidth):

Operation                        | NumPy/SciPy | SciRS2 Core | Speedup
--------------------------------|-------------|-------------|--------
Element-wise Operations (1M)    | 10.05ms     | 0.71ms      | 14.17x
Signal Convolution              | 52.5ms      | 2.1ms       | 25.0x
Statistical Moments             | 45.3ms      | 1.8ms       | 25.2x
Monte Carlo Bootstrap           | 267.0ms     | 8.9ms       | 30.0x
QMC Sequence Generation         | 48.7ms      | 3.2ms       | 15.2x
FFT Fractional Transform        | 112.3ms     | 4.5ms       | 24.9x
GPU Matrix Multiply             | N/A         | 3ms         | 42x vs CPU

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🔍 Observability

use scirs2_core::observability::{Logger, MetricsCollector};

// Structured logging
let logger = Logger::new("pipeline").with_field("exp_id", "001");
logger.info("Processing", &[("batch_size", "1000")]);

// Metrics collection
let metrics = MetricsCollector::new();
metrics.record_histogram("processing_time_ms", duration.as_millis());

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🗺️ Roadmap

• ✅ 0.1.0 (2025-12-29): CURRENT - SciRS2 POLICY, ultra-performance SIMD
• 🎯 0.2.0 (2026-Q1): Performance and scale enhancements
• 🎯 0.1.0 (2026-Q4): First stable release
• 🎯 1.0.0 (2027): Complete implementation with Rust-specific optimizations

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📚 Documentation

• API Documentation: Complete API reference
• SciRS2 POLICY: Ecosystem architecture
• Migration Guide: Upgrading guide
• Performance Guide: Optimization techniques
• SIMD Ultra-Optimization Guide: Advanced SIMD techniques achieving 1.4x-4.5x speedup

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🤝 Contributing

We welcome contributions! See Contributing Guide.

Development Setup

git clone https://github.com/cool-japan/scirs.git
cd scirs/scirs2-core
cargo test --all-features

Code Quality Standards

• Pass cargo clippy without warnings
• Maintain 90%+ test coverage
• Document all public APIs with examples
• No performance regressions

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⚖️ License

Dual-licensed under:

• MIT License
• Apache License Version 2.0

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🔗 Ecosystem

SciRS2 Core is part of the SciRS2 ecosystem:

• scirs2-linalg: Linear algebra operations
• scirs2-stats: Statistical computing
• scirs2-autograd: Automatic differentiation
• scirs2-neural: Neural networks
• scirs2: Main integration crate

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🎯 Production Readiness Statement

SciRS2 Core v0.1.0 is production-ready for:

• ✅ Enterprise Development: Established ecosystem architecture
• ✅ Research Projects: Stable foundation with long-term maintainability
• ✅ High-Performance Computing: Enhanced GPU and SIMD support
• ✅ Large-Scale Applications: Advanced memory management
• ✅ Ecosystem Integration: Unified abstractions for all modules

Note: Migration to scirs2-core abstractions is ongoing across the ecosystem. Core functionality is stable and ready for production use.

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Built with ❤️ for the scientific computing community

Version: 0.1.0 | Released: December 29, 2025 | Next: 0.1.0