SciRS2 - Scientific Computing and AI in Rust

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.

Quick Start

# 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

Key Highlights

✨ 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: 38,768 tests (all-features) / 36,989 (default-features) + 5,136 doc-tests — workspace-wide via cargo nextest — plus 3.1M+ lines of Rust code, 29 workspace crates 📊 Comprehensive: Linear algebra, statistics, ML, FFT, signal processing, computer vision, and more

Project Overview

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.

🎉 Release Status: v0.6.5 (2026-07-31)

Latest Stable Release - v0.6.5 (July 31, 2026) 🚀

• ✅ 38,768 Tests (all-features) / 36,989 (default-features) + 5,136 Doc-Tests: Full workspace-wide test suite via cargo nextest, across 29 workspace crates
• ✅ 3.1M+ Lines of Rust Code: Comprehensive coverage of scientific computing and AI/ML
• ✅ 29 Workspace Crates: Specialized modules for every scientific computing domain
• ✅ 81,100+ Public API Items: Extensive, well-documented API surface
• ✅ Near-complete implementation: Minimal stubs remaining across all modules
• ✅ Pure Rust by Default: OxiBLAS, OxiFFT, oxiarc-* - zero C/Fortran dependencies
• ✅ Zero Warnings Policy: Clean build with 0 compilation errors, 0 clippy warnings, 0 rustdoc warnings
• 📅 Release Date: July 31, 2026

What's New in 0.6.5 — Ignore-Audit Bug Hunt: the Live Backward Pass Was Mostly Dead Code:

• scirs2-autograd: the single most impactful fix in this release. The live backward-pass dispatcher (a ~670-line if/else chain keyed on op-name strings) covered only 58 of 281 differentiable op implementations; every op it didn't recognize silently fell through to an identity gradient, with no error or warning. Dispatch now consults each op's own Op::grad() implementation first, converting roughly 223 wrong-or-absent gradients to correct ones — including every elementary math function (sqrt/exp/ln/trig/hyperbolic-trig/log2/log10/abs) and activation (softplus/elu/swish/gelu/mish). Also fixed: reduce_sum/transpose/gather were only correct under an all-ones cotangent, reduce_mean lost its 1/N factor, sigmoid_cross_entropy and BatchMatMul were misidentified by fragile substring matching, concat/einsum/tensordot panicked during backprop, and SymmetricEigenOp now genuinely diagonalizes via cyclic Jacobi instead of separate per-size special cases. The public custom-gradient API (custom_op, scale_gradient, selective_stop_gradient, detach) was a complete no-op regardless of the user's backward closure — now genuinely functional.
• scirs2-linalg: a real, convergence-checked general (non-symmetric) eigenvalue/Schur engine (Householder→Hessenberg reduction, then implicit double-shift Francis QR with deflation) now backs decomposition::schur, lapack::eig, and eigen::advanced_precision_eig, replacing per-call implementations that either never checked for convergence or only worked on already-diagonal input.
• scirs2-stats: fixed an asymmetric/backwards one-sided Kolmogorov-Smirnov p-value (printing logically-inverted "Rejected"/"Not rejected" conclusions), a hardcoded-zero F-test p-value in polyfit, a self-deadlocking ErrorMonitor, and garbage-output Niederreiter/Sobol QMC sequence generators.
• scirs2-graph: spectral clustering and Hungarian matching now compute real linear-algebra/optimal-assignment answers instead of a random/stand-in result; watts_strogatz_graph no longer hangs on ~99.8% of seeds at p > 0 (its rewiring step checked has_node, always true, instead of has_edge).
• scirs2-io / scirs2-integrate / scirs2-series / scirs2-spatial / scirs2-ndimage / scirs2-special: real NetCDF3 read/write (was a no-op stand-in), real DOP853/RK23 adaptive-step error estimators, a fixed out_of_core streaming hang/unbounded-memory-growth/double-count, fixed inverted octree/quadtree k-NN BinaryHeap ordering (was returning the farthest candidates) and an always-zero A* path cost, a mmap loader that ignored its own file header (shifting every element), and gamma(x) silently returning inf for x in ~[140.5, 171].
• scirs2-signal: wired 83 previously-orphaned-but-real files back into the crate — a full Kalman filter family (standard/extended/unscented/ensemble/information/particle), a BSS/ICA toolkit, compressed-sensing sparse recovery (OMP/basis-pursuit/LASSO/CoSaMP), and a SciPy-ShortTimeFFT-class STFT port — and deleted ~167 unreachable legacy/duplicate files (541 total .rs files, 255 unreachable — 47% of the crate).
• Workspace-wide: completed a full #[ignore]-legitimacy audit — every ignored test workspace-wide actually run and root-caused, not just read — dropping the count from 132 (~31% bare/no-reason) to 59, every one now reason-tagged (requires-gpu:/requires-env:/slow:/bench:/not-implemented:) and enforced going forward by a new cargo-scirs2-policy lint.

See CHANGELOG.md [0.6.5] for the complete list.

What's New in 0.6.4 — wasm32 Follow-Up Fix:

• oxifft wasm32 threading fix: oxifft 0.4.1 added a hard compile_error! when its threading (rayon) feature is active on wasm32 targets. scirs2-fft and scirs2-signal now declare oxifft via target-gated [target.'cfg(...)'.dependencies] tables — full defaults off-wasm, default-features = false, features = ["std"] on wasm32 — resolving the error.
• scirs2-wasm and scirs2 (meta-crate) resume publishing: both were skipped from 0.6.3 because the fix above landed too late to affect the scirs2-fft/scirs2-signal artifacts crates.io already had at that version. They catch up at 0.6.4.

See CHANGELOG.md [0.6.4] for the complete list.

What's New in 0.6.3 — Windows-Compatibility Hardening:

• Heap corruption fixed: scirs2-spatial's DistancePool allocated cache-aligned buffers via a raw System.alloc call, then freed them through Box<[f64]>'s default-aligned Drop — an alloc/dealloc layout mismatch that is undefined behavior. glibc's allocator tolerated it; Windows' does not, corrupting the heap (STATUS_HEAP_CORRUPTION). Now allocates through plain Vec/Box<[f64]>. scirs2-stats's AdaptiveMemoryManager had a related bug: it re-derived the deallocation strategy from config instead of the strategy allocate actually resolved for that pointer, so Adaptive-configured pools could be freed through the wrong allocator; allocate now records the resolved strategy per pointer for deallocate to look up.
• Stack overflow fixed: scirs2-symbolic's EmlNode gained an explicit iterative Drop impl — the compiler-derived recursive one overflowed the stack when a deep expression tree (e.g. a 10,000-node chain) went out of scope, fitting in Linux's 8 MB default thread stack but not Windows' 1 MB default (STATUS_STACK_OVERFLOW).
• Process abort fixed: scirs2-transform's AdvancedMemoryPool::prewarm_common_sizes eagerly pre-allocated up to ~5.5 GB of spare buffers just to construct an AdvancedPCA; Linux's overcommit hid the cost, Windows backed the whole reservation up front and aborted. Pre-warming is now capped by a 64 MB budget.
• Path validation fixed: scirs2-core's CrossPlatformValidator::validate_windows_path flagged the drive-letter colon in any absolute Windows path (e.g. C:\Users\...) as an invalid character, rejecting every such path.
• Numerical correctness: scirs2-signal's Francis double-shift QR (ESPRIT phase estimation) had two bugs letting eigenvalues silently drift from the input matrix's; n4sid_estimate's least-squares solve used normal equations, which silently returned bogus huge-norm solutions for rank-deficient (non-persistently-exciting) inputs instead of erroring — both now go through an SVD-based minimum-norm solve.
• Dependency bumps: oxifft 0.3.2→0.4.1, oxicuda-* 0.5.1→0.5.3, oxiz 0.2.4→0.3.0.

See CHANGELOG.md [0.6.3] for the complete list.

What's New in 0.6.2 — Pure Rust Hardening: HDF5, Tracy, NUMA & OpenCL:

• Real pure-Rust HDF5 backend: MAT v7.3 (HDF5-based) support is now available in default builds — EnhancedMatFile/V73MatFile no longer require a system libhdf5. The in-tree hdf5_lite reader (2,697 lines) is retired in favor of oxih5, which widens format coverage (superblock v2/v3, fractal heaps, extensible arrays, virtual datasets, szip) and fixes a critical silent-data-loss bug where compressed-dataset writes were silently dropped, plus a mis-parsed version-2 dataspace header that had been self-concealed by a buggy test fixture.
• C/C++ dependencies removed: hdf5 (C-linked), libnuma, tracy-client (C++) and opencl3 are all gone from the dependency graph. scirs2-core's Tracy profiling is now a pure-Rust Chrome-Trace-Event exporter; NUMA topology discovery reads real /sys data instead of estimating "8 cores per node"; OpenCL support now runtime-dlopens the system ICD instead of linking at build time. scirs2-spatial's NumaTopology::detect() had the same fabricated-data problem (hardcoded core/memory estimates) — it now reads the real Linux sysfs topology, with an honest single-node fallback elsewhere.
• BREAKING (TLS): reqwest/ureq now build with rustls-no-provider, dropping the C aws-lc-rs/ring backends; SciRS2 auto-installs a pure-Rust oxitls-rustcrypto-provider on first use unless the application already installed its own CryptoProvider.
• scirs2-ndimage: CUDA kernel JIT now compiles through the pure-Rust oxicuda-nvrtc crate (runtime dlopen, zero build-time CUDA SDK dependency).
• Dependency bumps: oxih5/oxih5-core 0.1.3→0.2.2, oxisql-* 0.3.2→0.4.0, oxiz 0.2.3→0.2.4, oxicuda-* 0.5.0→0.5.1 (+new oxicuda-nvrtc), Cranelift 0.133→0.134 (fixes a memmap2 unsound advisory, RUSTSEC-2026-0186).

See CHANGELOG.md [0.6.2] for the complete list.

What's New in 0.6.1 — Real Codegen, DLPack Safety & GPU-Dispatch Honesty:

• Real model-serving codegen: scirs2-neural's ModelPackager::package() now genuinely compiles a Rust project via cargo/rustc for native binaries (PackageFormat::Native) and C-shared-library (PackageFormat::CSharedLibrary) targets, replacing the previous placeholder byte-writing stubs; new serving module split into mod.rs/types.rs/packager.rs/codegen.rs/tests.rs.
• Critical DLPack fix: a missing #[repr(C)] on to_dlpack's BackingStore in scirs2-python caused a process-crashing SIGBUS whenever the DLPack capsule was garbage-collected; also fixed a double-consumption bug in the capsule destructor and a stride-handling bug in from_dlpack that silently produced wrong data for non-contiguous/transposed tensors.
• GPU-dispatch and hardware-detection honesty: scirs2-datasets's AdvancedGpuOptimizer now performs real wgpu/GpuNdarray GPU dispatch instead of a simulated mock (minor breaking change: BenchmarkResult.gpu_time_ms/.speedup are now Option<f64>, None when no real GPU dispatch ran); scirs2-core's PlatformCapabilities::detect() now runtime-probes for real NVIDIA/Metal GPU hardware instead of hardcoding cuda_available to false; scirs2-special's permanently-failing execute_kernel GPU path now has a real CPU fallback for gamma/bessel_j0/erf; wgpu adapter/device discovery across 7 crates now restricts to Backends::PRIMARY (Vulkan/Metal/DX12/BrowserWebGPU) instead of Backends::all(), so a broken secondary GL backend can no longer report false availability and then die on first use.
• New API surface: F distribution ppf() (inverse CDF) in scirs2-stats, plus opt-in real memory tracking in its SciPy benchmark framework; hamming() distance in scirs2-spatial; finance facades (AdvancedMonteCarloEngine, RealTimeRiskMonitor, ExoticOptionPricer, RiskAnalyzer) in scirs2-integrate; training_history() on scirs2-core's generative-sampling models (NormalizingFlow and others) and export_data() on ProductionProfiler; simd_distance_squared_euclidean on SimdUnifiedOps.
• Correctness fixes: scirs2-autograd's gradient-dispatch engine now actually invokes CondOp/LogDetOp backward rules (previously dead code behind op-name string dispatch), plus a proper Wielandt-deflation SVD fix and a condition-number computation fix; scirs2-signal's waverec now reconstructs to the correct original length (was 2x for non-Haar filters) with real perfect-reconstruction verification; restored SIMD paths for scirs2-interpolate's weighted_sum/squared_euclidean_distance (previously always fell back to scalar); fixed a GEMM operand-layout bug in the scirs2-datasets/scirs2-interpolate/scirs2-optimize CUDA paths (mistagged ColMajor, but oxicuda-blas requires RowMajor); two Miri-detected undefined-behavior fixes (unaligned-reference UB in cast_bytes_to_slice, a missing-else branch in a SIMD kernel).
• Housekeeping: removed 14 dead/orphaned stub types from scirs2-stats; split 3 oversized (>2000-line) files into module directories via SplitRS (scirs2-io's advanced_coordinator.rs/hdf5/mod.rs, scirs2-series's utils.rs); dependency bumps — oxiarc-archive/-lz4/-bzip2/-zstd/-deflate/-snappy/-brotli 0.3.3 → 0.3.6 (the 0.3.4 regression this was previously pinned around is fixed upstream as of 0.3.5), oxisql-core/-sqlite-compat 0.3.1 → 0.3.2, oxicuda-* 0.4.0 → 0.5.0; cargo-scirs2-policy's --format flag is now a validated enum instead of silently falling back on typos.

See CHANGELOG.md [0.6.1] for the complete list.

What's New in 0.6.0 — Pure-Rust CUDA Decentralization:

• oxicuda-* CUDA backends: introduced a direct, per-crate, off-by-default, runtime-probed cuda feature across 10 crates — scirs2-fft, scirs2-symbolic, scirs2-interpolate, scirs2-special, scirs2-stats, scirs2-graph, scirs2-linalg, scirs2-optimize, scirs2-datasets, scirs2-vision — decentralizing GPU acceleration out of scirs2-core. Every path is f64-native, real CUDA PTX→driver JIT (not a CPU/wgpu simulation), and additive alongside the existing wgpu portability story.
• wgpu feature standardization (breaking): every real wgpu/WebGPU portability feature across the ecosystem renamed to a single wgpu name (was wgpu_backend/gpu_wgpu/wgpu_fft/wgpu_kernels/wgpu_rbf).
• Pure Rust win: retired scirs2-core's cudarc-based CUDA backend entirely, dropping the cudarc dependency; GpuBackend::Cuda now directs users to the per-crate oxicuda-* features.

See CHANGELOG.md [0.6.0] for the complete list.

What's New in 0.5.1 — Correctness, API-Stability & Pure Rust Hardening:

• Exact autograd gradients: new TraceBackwardOp (reverse-mode gradient for trace) and a tensor_ops/decomposition_backward.rs module with exact spectral gradients — matrix-sqrt (Sylvester equation), matrix-log/matrix-power (Daleckii–Krein divided differences), and reduced-SVD VJP (degenerate-singular-value detection). Previously these returned all-zeros or wrong shapes.
• API stability: restored the #[non_exhaustive] attribute on scirs2-core's CoreError (lost previously); a new core_error_non_exhaustive compile-fail test in scirs2-stability-tests now guards the contract.
• GPU/CUDA honesty: CUDA/GPU paths across scirs2-core, scirs2-linalg, and scirs2-fft now report BackendNotAvailable / NotImplemented / Option<f64> metrics when no real device or measurement exists, instead of returning fabricated contexts or invented throughput numbers.
• Zarr v2/v3: chunked-array support is now publicly exported (pub mod zarr) — directory stores, codec pipeline, and chunk-boundary slice I/O.
• Pure Rust hardening: removed the C/MPFR rug, C-backed rusqlite, tokenizers, and external either/hex/urlencoding/data-encoding crates — arbitrary precision now on oxinum-*, SQLite on oxisql-sqlite-compat, blake3 built with its no-ASM pure feature. Ecosystem dep bumps: OxiArc 0.3.3, OxiSQL 0.3.1, OxiNum 0.1.2, OxiH5 0.1.3, OxiZ 0.2.3, OxiCode 0.2.4, plus OxiEML 0.1.2.
• Numerical bug fixes: scirs2-series ADF test now uses an SVD-based Moore–Penrose pseudo-inverse for rank-deficient designs; plus 5 newly-fixed numerical bugs — SSPRK(5,4) integrator (now true 4th-order), Haar perfect-reconstruction check, cheby2/bessel bandpass transfer functions, and TimeSeriesReservoir exponential recency weighting.

What's New in 0.5.0 — CAS & Symbolic Mathematics + GPU Acceleration (Waves 53–77):

Core EML/CAS substrate (Waves 53–57):

• scirs2-symbolic — Complete EML-IR-native CAS: eml::tree, LoweredOp flat IR, stack-safe evaluator, symbolic differentiation (grad/hessian/jacobian), algebraic simplification with fixed-point rewriting
• cas::canonicalize — EML-native canonical form with 7 algebraic rewrite rules; e-graph equality saturation; cas::pattern engine; cas::identity_db (11 trig/log identities)
• cas::smt — OxiZ QF_NRA SMT integration: EmlSmtSolver, structural-hash fast path, Ackermann transcendental encoding for 16 ops, certified rewriting with RAII safety
• compile::to_jit — Cranelift CPU JIT for LoweredOp; to_jit_auto auto-dispatches CPU/GPU by batch size threshold
• compile::to_gpu — WGSL compute shader JIT from LoweredOp tape; real wgpu submission via eval_batch (Wave 75)
• LaTeX export — eml::to_latex: \frac, \cdot, a^{b}, \sqrt, \pi/e exact constants, \operatorname{arcsinh} — stack-safe on deeply nested trees
• Symbolic regression — beam-search SR engine with with_constraints (SMT-pruned BoundedOutput + Monotonic); discover_multi, discover_ode (SINDy-style); NUMA-aware parallel predict
• Python bindings — scirs2.symbolic sub-namespace: PyEmlTree, PyCanonical, lower/simplify/grad/eval_real/discover; GIL-releasing discover
• scirs2-autograd symbolic feature — eml_scalar_op creates differentiable scalar tensors; float-tape ↔ EML gradient parity; criterion benchmark suite
• scirs2-optimize symbolic — symbolic::newton (exact Hessian/gradient), symbolic::lagrangian/build_kkt (Lagrangian+KKT Newton), L-BFGS symbolic, trust-region symbolic

Advanced CAS (Waves 59–70):

• cas::solve/solve_system/solve_ode — algebraic solver, 3-tier system solver (Bareiss/Gröbner/transcendental), 5 ODE family solvers
• cas::integrate_rational — Risch-LITE rational integration
• cas::moments_catalog/expected_fisher_catalog/noether_conservation — closed-form statistical moments, Fisher information, Noether conservation laws
• cas::series — Taylor + Padé approximants in EML form
• cas::cse_dag::CseDag — structural-hash CSE DAG with topological-order evaluation
• diffgeom module — Tensor/Metric/christoffel/ricci/einstein/covariant_derivative/Riemann tensor/Weyl tensor
• scirs2-linalg::symbolic — det_symbolic, eigenvalues_symbolic_2x2, expm_symbolic, StructureKind recognition, spectral dispatch
• scirs2-stats::mle_symbolic — gradient-descent MLE from symbolic PDF
• scirs2-neural::symbolic::rope_attention — closed-form RoPE attention logit

Wave 73–77 GPU + Algorithms:

• scirs2-core — NUMA-aware par_map_chunks (Linux pthread affinity pin, rayon fallback Darwin/WASM); GpuNdarray<f32> (7 WGSL kernels: elementwise, matmul, sum, transpose); concat_axis.wgsl (axis>0) + reduce_sum_axis.wgsl (rank≥3)
• scirs2-interpolate — Real wgpu RBF kernel-matrix + evaluation (WGSL, kernel_id uniform, OnceLock GPU probe cache)
• scirs2-graph — Real wgpu BFS (level-sync, atomicCompareExchange), Bellman-Ford SSSP (edge-parallel atomicMin), true delta-stepping WGSL (light/heavy phase + changed_flag convergence); CPU-parallel fallback via rayon+AtomicU32
• scirs2-optimize — CG GPU (GpuNdarray dot/direction-update), Newton GPU (Hessian-vector matmul), L-BFGS GPU (two-loop recursion via GpuNdarray)
• scirs2-symbolic — ALiBi positional bias (alibi_slope, alibi_bias_expr, verify_symbolic_vs_numerical); SR-as-prior for time series (discover_series_prior, series_prior_regularization); SymbolicPriorLoss in scirs2-neural
• scirs2-integrate — Pantelides index reduction (Hopcroft-Karp O(E√V) + Tarjan SCC); Wiktorsson 2001 Lévy-area for SDE strong order 1.5
• scirs2-spatial — 2D+3D Hilbert curve sort (24-state Butz/Hamilton for 3D)

What was in 0.4.3

• 36,446 Tests across 29 workspace crates — integration test pipelines (ML/signal/NLP/vision/graph/scientific)
• Dependency upgrades: oxifft 0.1.4, sha2 0.11, egui/eframe 0.34 — latest COOLJAPAN ecosystem
• scirs2-core: Async GPU transfer, unified memory, RRB-tree, Tracy profiler, stream allocator, memory defrag, NUMA bandwidth optimization
• scirs2-linalg: Auto-precision solver dispatch, GPU eigensolvers, H-matrix compression (hierarchical matrices)
• scirs2-special: Spheroidal wave functions, Mathieu-Hill, f16 mixed-precision, GPU auto-dispatch, Clebsch-Gordan SU(2)/SU(3)/SO(5), Hall polynomials, Hecke L-functions, elliptic L-functions
• scirs2-fft: Ring-buffer STFT, cache-oblivious FFT, streaming FFT (out-of-core)
• scirs2-signal: GPU spectrograms, matched filter bank, batched Welch PSD + EFDD modal analysis
• scirs2-sparse: ILU(0) mixed CPU/GPU preconditioning
• scirs2-optimize: GDAS/SNAS/predictor-based NAS, CMA-ES optimizer, subspace embedding (JL/Gaussian/sparse, sketched LS)
• scirs2-integrate: GPU LBM, ODE ensemble, sparse grid quadrature, particle filter
• scirs2-interpolate: Physics-informed RBF, random RBF, GPU deep kriging + active learning
• scirs2-io: Iceberg tables, DataFusion provider, object-store abstraction, S3 multipart, GCS + Azure SAS, exactly-once semantics
• scirs2-datasets: ndarray generators + sharding, HuggingFace integration, dataset sharding
• scirs2-text: USE/SimCSE/HDP topic modeling, Unicode tokenizer, enhanced BPE + chat templates, sentence embeddings, multilingual
• scirs2-series: PC causality analysis, all v0.4.0 items verified
• scirs2-neural: NAS repair (74 tests), Mamba SSM, QAT, model tracing
• scirs2-numpy: DLPack protocol, masked arrays, structured dtypes, array protocol
• scirs2-metrics: Rotated IoU, 17 new tests
• scirs2-python: special/interpolate/integrate bindings
• Zero warnings: cargo clippy + rustdoc + fmt all clean; Apache-2.0 license compliance enforced
• scirs2-wasm TypeScript bindings: Full TypeScript type declarations (19 API sections, ~96 exported symbols) covering stats, signal, linear algebra, FFT, ML models, streaming ops, React hooks, Web Worker utilities, FinalizationRegistry memory management
• scirs2-core SIMD examples: 5 new SIMD benchmark and demo programs
• Build infrastructure: Vendored bitflags 0.6.0 patch; refined ARM NEON pathfinder_simd build patch

What was in 0.4.0

• 60+ clippy warnings fixed — zero-warning workspace (clippy, rustdoc, compilation)
• Flash Attention 2, QAT, ONNX export, LoRA/DoRA/GPTQ — production neural network training and deployment
• GPU PDE solvers, GPU FFT pipeline, GPU SpMV — hardware-accelerated scientific computing
• Temporal GNN (TGAT/TGN), Graph Transformers (GraphGPS/Graphormer) — state-of-the-art graph ML
• NeRF/instant-NGP, 3D detection, depth completion — advanced computer vision
• NUMA-aware scheduler, lock-free data structures — high-performance core infrastructure
• WebGPU/WASM backend with 76 tests — browser-side GPU compute
• Conformal prediction, Bayesian NNs, INLA — uncertainty quantification and Bayesian ML
• Cache-oblivious FFT, adaptive sparse FFT — advanced spectral methods
• mdBook documentation (26 EN + 12 JP pages) — comprehensive bilingual docs
• Distribution validation (78 tests, 15+ distributions) — numerical accuracy verified
• Deep Kriging, GP Surrogate, online streaming interpolation — advanced interpolation
• Neural Audio (Conv-TasNet/vocoder/enhancement) — audio ML pipeline
• Delta Lake, TileDB, Arrow Flight, Kafka — enterprise data I/O
• PCMCI causality, physics-informed time series — advanced time series analysis

What was in 0.3.4

• OxiARC Compression Upgrades: Upgraded all OxiARC compression libraries (oxiarc-archive, oxiarc-lz4, oxiarc-bzip2, oxiarc-zstd, oxiarc-core, oxiarc-deflate) from 0.2.4 to 0.2.5
• Crates.io Migration: Migrated oxiarc-snappy and oxiarc-brotli from local path dependencies to crates.io version 0.2.5
• Clippy Cleanup: Fixed ~50 clippy warnings across the workspace (sort_by to sort_by_key, manual checked division, loop counters, redundant closures)
• Dependency Cleanup: Removed 10+ unused dependencies (ndarray-npy, x509-parser, itertools, num-rational, gmp-mpfr-sys, opentelemetry-prometheus, opentelemetry-semantic-conventions, mongodb, redis, prost) — eliminated zip crate from dependency tree

What was in 0.3.3

• Pure Rust Compression: Replaced C-based compression (flate2, lz4, zstd, bzip2) with oxiarc pure Rust alternatives across core, cluster, and io
• Pure Rust Memory Profiling: Replaced tikv-jemallocator with OS-native APIs (Mach task_info/procfs)
• WASM Target Support: Added getrandom WASM backend and wasm32 configuration
• Pure Rust Directory Detection: Replaced dirs crate with custom platform_dirs module
• Parquet Pure Rust: Configured parquet with pure Rust feature flags, switched Zstd to Brotli codec

What was in 0.3.2

• pyo3 0.28.2 Upgrade: Migrated Python bindings to pyo3 0.28.2 (Python::with_gil -> Python::attach)
• #[pyclass] Deprecation Fixes: Updated from_py_object attribute usage to resolve deprecation warnings
• Benchmark Modernization: Replaced deprecated criterion::black_box with std::hint::black_box across all benchmarks

What was in 0.3.1

• Neural Networks: Transformer architectures (GPT-2, T5, Swin), GNNs (GCN/GAT/GIN), diffusion models, capsule networks, spiking neural networks (SNN)
• Advanced Statistics: Gaussian process regression, survival analysis (Cox/Kaplan-Meier/Nelson-Aalen), Bayesian networks, copulas, nonparametric Bayes
• Signal Processing: OMP/ISTA compressed sensing, LMS/RLS adaptive filtering, MFCC/EMD, source separation (ICA/NMF)
• Graph Algorithms: Louvain/Girvan-Newman community detection, VF2 isomorphism, Node2Vec embeddings, network flow
• Sparse Linear Algebra: LOBPCG, IRAM, AMG, BCSR/ELLPACK formats, block preconditioners
• Time Series: TFT, N-BEATS, DeepAR, VECM, DFM, EGARCH/FIGARCH, online ARIMA
• Optimization: SQP, LP/QP interior point, SGD/Adam/NSGA-III, MIP/SDP/SOCP solvers
• FFT Extensions: Sparse FFT, Prony method, MUSIC, Lomb-Scargle, Burg method, NTT
• Interpolation: RBF, MLS, Floater-Hormann, spherical harmonics, kriging
• Special Functions: Mathieu, Coulomb wave functions, Wigner 3j/6j, Jacobi theta
• Computer Vision: Stereo matching, depth estimation, ICP point cloud registration, SLAM
• Julia Bindings: New Julia interface for seamless interoperability

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

🦀 Pure Rust by Default

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.

Features

Scientific Computing Core

• Linear Algebra (scirs2-linalg): Matrix operations, GMRES/PCG/BiCGStab iterative solvers, Lanczos/Arnoldi factorizations, CP-ALS/Tucker tensor decompositions, matrix functions (expm/logm/sqrtm), control theory (Riccati/Lyapunov)
• Statistics (scirs2-stats): Distributions (stable, GPD, von Mises-Fisher, Tweedie), Bayesian methods (NUTS/HMC/SMC), Gaussian processes, survival analysis (Cox/KM/AFT), copulas, Bayesian networks, causal inference
• Optimization (scirs2-optimize): MIP/SDP/SOCP solvers, Bayesian optimization, NSGA-III multi-objective, stochastic (SGD/Adam/SVRG), metaheuristics (ACO/SA/DE/Harmony), convex (ADMM/proximal), combinatorial
• Integration (scirs2-integrate): ODE/BVP/DAE solvers, PDE (FEM/LBM/DG), SDE/SPDE solvers, BEM, phase-field (Cahn-Hilliard/Allen-Cahn), port-Hamiltonian systems, QMC/IMEX methods
• Interpolation (scirs2-interpolate): RBF, MLS, PCHIP, spherical harmonics, kriging, B-spline surfaces, tensor product, natural neighbor, barycentric
• Special Functions (scirs2-special): Mathieu, Coulomb wave functions, spherical harmonics, Wigner 3j/6j/9j, Jacobi theta, Fox H-function, Heun, Appell, q-analogs, Weierstrass, polylogarithm
• Signal Processing (scirs2-signal): Matched filter, CFAR radar detection, Kalman/EKF/UKF state estimation, compressed sensing (OMP/ISTA/CoSaMP), MFCC, EMD/HHT, source separation (ICA/NMF), adaptive filtering (LMS/RLS)
• Sparse Matrices (scirs2-sparse): LOBPCG/IRAM eigensolvers, AMG, BCSR/ELLPACK formats, block preconditioners (Jacobi/SPAI/Schwarz), GCRO-DR recycled Krylov, domain decomposition
• Spatial Algorithms (scirs2-spatial): R*-Tree, Fortune's Voronoi, WGS84/UTM geodata projections, spatial statistics, trajectory analysis, sweep-line algorithms, 3D convex hull

Advanced Modules

• N-Dimensional Image Processing (scirs2-ndimage): Gabor/SIFT/HOG feature detection, watershed/SLIC/GrabCut segmentation, optical flow (Farneback/LK), 3D morphology, medical imaging, texture analysis (GLCM/LBP)
• Clustering (scirs2-cluster): GMM, SOM, HDBSCAN, Dirichlet process, kernel k-means, biclustering (Cheng-Church/FABIA), topological (Mapper/TDA), deep clustering (DEC), stream/online (CluStream/DenStream)
• FFT and Spectral Methods (scirs2-fft): Sparse FFT, Prony method, MUSIC/ESPRIT, Lomb-Scargle, NTT, CZT/FRFT, polyphase filterbank, all DCT/DST variants, wavelet packets, reassigned spectrogram
• I/O Utilities (scirs2-io): Protobuf/msgpack/CBOR/BSON/Avro serialization, Parquet/Feather/ORC columnar formats, streaming JSON/CSV/Arrow, cloud storage abstraction, HDF5-lite, schema management, ETL pipeline
• Sample Datasets (scirs2-datasets): Text/NER/QA, medical imaging, graph benchmarks, recommendation, anomaly detection, time series (UCR-compatible), synthetic generators
• Distribution Validation (scirs2-validation): Distribution validation utilities and reference values for statistical testing, PDF/CDF/PPF testing across 15+ distributions

AI and Machine Learning

• Automatic Differentiation (scirs2-autograd): Custom gradient rules, gradient checkpointing, JVP/VJP (forward/reverse mode), implicit differentiation, mixed precision (FP16/BF16), distributed gradient, Hessian computation
• Neural Networks (scirs2-neural): Transformers (GPT-2/T5/SWIN), GNNs (GCN/GAT/GraphSAGE/GIN), diffusion models (DDPM/DDIM), VAE, GAN, capsule networks, SNN, PPO/DPO RL, MoE, ViT/CLIP/ConvNeXt, knowledge distillation, quantization, pruning, meta-learning (MAML)
• Graph Processing (scirs2-graph): Louvain/Girvan-Newman community detection, VF2 isomorphism, Node2Vec embeddings, maximum flow (Dinic/push-relabel), temporal graphs, hypergraphs, force-directed layout, SVG visualization
• Data Transformation (scirs2-transform): UMAP, Barnes-Hut t-SNE, sparse PCA, persistent homology (TDA), optimal transport (Wasserstein/Sinkhorn), archetypal analysis, metric learning (LMNN/ITML), multiview (CCA/deep CCA)
• Metrics (scirs2-metrics): Detection (IoU/AP/mAP/NMS), ranking (NDCG/MAP/MRR), generative (FID/IS/LPIPS), fairness (demographic parity/equalized odds), segmentation, streaming metrics
• Text Processing (scirs2-text): BPE/WordPiece tokenizers, CRF/HMM sequence labeling, FastText, NER, topic modeling (LDA/NMF), coreference resolution, knowledge graph extraction, RST discourse analysis
• Computer Vision (scirs2-vision): Stereo depth estimation, ICP point cloud registration, PnP camera pose, dense optical flow, video processing, SLAM framework, panoptic/semantic/instance segmentation, 3D reconstruction (SfM)
• Time Series (scirs2-series): TFT/N-BEATS/DeepAR deep learning forecasting, VAR/VECM/DFM models, EGARCH/FIGARCH volatility, FDA (functional data analysis), conformal prediction, online ARIMA, Granger causality, hierarchical reconciliation

Performance and Safety

• Pure Rust by Default: 100% Rust with no C/C++/Fortran dependencies (OxiBLAS for BLAS/LAPACK, OxiFFT for FFT)
• Ultra-Optimized SIMD: Ecosystem-wide vectorization achieving 10-30x performance improvements
• Work-Stealing Scheduler: Adaptive parallel task execution with NUMA-aware allocation
• Multi-Backend GPU Acceleration: CUDA, ROCm, Metal, WGPU, OpenCL support
• Memory Efficiency: Smart allocators, buffer pools, zero-copy operations, cache-oblivious algorithms
• Safety: Memory safety and thread safety through Rust's ownership model; zero unwrap() in production code
• Error Handling: Comprehensive error system with context, recovery strategies, and circuit-breaker patterns

Project Scale

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

• 📊 Total Lines: ~4.2M lines across all files (Rust, Python, Julia, TOML, Markdown, etc.)
• 🦀 Rust Code: ~3.87M SLoC across 8,134 files (tokei measured)
• 📝 Documentation: Comprehensive comment lines + embedded Markdown in Rust docs
• 🧪 Testing: 37,442 tests (all-features) / 35,668 (default-features) + 5,000 doc-tests passing — workspace-wide via cargo nextest
• 📦 Modules: 29 workspace crates covering scientific computing, machine learning, and AI
• 🔌 Public API: 80,800+ public API items across all crates
• 🏗️ Development Effort: Estimated 89+ months with 136 developers (COCOMO model)
• 💰 Estimated Value: $137M+ development cost equivalent (COCOMO model)

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

Project Goals

• 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

Project Structure

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
├── scirs2-validation/        # Distribution validation utilities and reference values

# 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

Architectural Benefits

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

Advanced Core Features

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

GPU Acceleration

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(())
})?;

Memory Management

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);

Memory Metrics and Profiling

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();

Module Documentation

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

Complete Crate Reference (v0.6.5)

Note: scirs2-optim has been separated into the independent OptiRS project.

Implementation Strategy

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

Core Module Usage Policy

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

Dependency Management

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"]

Core Dependencies

SciRS2 follows the COOLJAPAN Pure Rust Policy. All default dependencies are 100% Pure Rust.

Core Pure Rust Dependencies

• oxiblas: Pure Rust BLAS/LAPACK implementation (no C/Fortran, no OpenBLAS/MKL required)
• oxifft: Pure Rust FFT with FFTW-comparable performance (no FFTW/CLFFT C library required)
• oxiarc-archive/oxiarc-*: Pure Rust archive/compression (replaces zip/zlib C bindings)
• oxicode: Pure Rust serialization (replaces bincode)
• ndarray: Multidimensional array operations (via scirs2-core abstraction)
• num: Numeric abstractions
• rayon: Data-parallel processing

Infrastructure Dependencies

• serde/serde_json: Serialization/deserialization
• thiserror/anyhow: Error handling
• tokio: Async runtime (for async IO utilities)
• petgraph: Graph data structures
• image: Image encoding/decoding utilities

Optional Feature-Gated C Dependencies (not enabled by default)

• cuda feature: NVIDIA CUDA GPU acceleration
• mpsgraph feature: Apple Metal GPU acceleration (macOS only)
• arbitrary-precision feature: GMP/MPFR arbitrary precision arithmetic

Recent Development History

v0.3.1 (Released March 9, 2026) - Massive Ecosystem Expansion

Major Feature Release

• 🚀 29 Workspace Crates: Comprehensive modular ecosystem for scientific computing and AI
• 🚀 27,632 Tests: Full test suite with comprehensive coverage
• 🚀 Advanced Neural Networks: Transformers, GNNs, diffusion models, SNN, capsule networks
• 🚀 Statistics & Probabilistic ML: Gaussian processes, Bayesian networks, survival analysis, copulas
• 🚀 Graph Algorithms: Community detection, GNN embeddings, isomorphism, flow algorithms
• 🚀 Signal Processing: Compressed sensing, adaptive filtering, source separation, synchrosqueezing
• 🚀 Optimization: SQP, MIP, SDP, SOCP, Bayesian optimization, metaheuristics (ACO/SA/DE)
• 🚀 Time Series: TFT, N-BEATS, DeepAR, VECM, DFM, EGARCH/FIGARCH models
• 🚀 Julia Bindings: New Julia interface for ecosystem interoperability
• 🚀 FFT Extensions: Sparse FFT, Prony, MUSIC, Lomb-Scargle, Burg, NTT
• 🚀 Sparse Linear Algebra: LOBPCG, IRAM, AMG, BCSR/ELLPACK, block preconditioners

v0.2.0 (Released February 12, 2026) - SIMD Expansion & Spatial Enhancement

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

v0.1.3 (Released January 25, 2026) - Maintenance & Enhancement

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

v0.1.2 (Released January 15, 2026) - Performance & Pure Rust Enhancement

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

Installation and Usage

System Dependencies

v0.6.5 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

Cargo Installation

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 = "0.6.5"

Or include only the specific modules you need:

[dependencies]
# Core utilities
scirs2-core = "0.6.5"

# Scientific computing modules
scirs2-linalg = "0.6.5"
scirs2-stats = "0.6.5"
scirs2-optimize = "0.6.5"

# AI/ML modules
scirs2-neural = "0.6.5"
scirs2-autograd = "0.6.5"
# Note: For ML optimization algorithms, use the independent OptiRS project

Example Usage

Basic Scientific Computing

// 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(())
}

Neural Network Example

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(())
}

GPU-Accelerated Example

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(())
}

Platform Compatibility

SciRS2 v0.6.5 (July 31, 2026) has been tested on the following platforms:

✅ Fully Supported Platforms

⚠️ Partially Supported Platforms

Platform-Specific Requirements

macOS / Linux

To run the full test suite with all features:

# No system dependencies required - Pure Rust!
cargo nextest run --nff --all-features  # 38,768 tests

Windows

# Build works successfully
cargo build

# v0.6.3 fixed several Windows-specific crash bugs (heap corruption from
# alignment-mismatched deallocation, stack overflow from recursive Drop glue,
# process abort from non-overcommitted eager allocation, and a path validator
# that rejected every absolute Windows path) — see CHANGELOG.md `[0.6.3]`.
# Full Windows CI validation of the complete suite is still pending.
cargo test

### Running Tests

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

```bash
# Install nextest
cargo install cargo-nextest

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

Current Status (v0.6.5 - Released July 31, 2026)

🎉 Production-Ready Features

Pure Rust Scientific Computing Stack

• 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

High-Performance Computing

• 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

Comprehensive Module Coverage

• 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: 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: 37,442 tests (all-features) / 35,668 (default-features) + 5,000 doc-tests, zero warnings policy, comprehensive error handling

New in v0.4.0

• ✨ Massive Feature Expansion: 39 waves of development adding 200+ major features
• ✨ Neural Networks: Flash Attention 2, QAT, ONNX export, LoRA/DoRA/GPTQ, KAN networks
• ✨ GPU Computing: PDE solvers, FFT pipeline, SpMV, tiled GEMM, adaptive dispatch
• ✨ Graph ML: Temporal GNN (TGAT/TGN), GraphGPS, Graphormer, E(n)-GNN, SPONGE
• ✨ Computer Vision: NeRF/instant-NGP, 3D detection, depth completion, video object segmentation
• ✨ Core Infrastructure: NUMA-aware scheduler, lock-free skiplist/B-tree, WebGPU/WASM backend
• ✨ Statistics: Conformal prediction (CQR/RAPS/Mondrian), Bayesian NNs, INLA, ADVI/Laplace/SWAG
• ✨ Zero Warnings: 60+ clippy warnings fixed, 0 errors, 0 warnings, 0 rustdoc warnings

Stable Modules (Production Ready — v0.6.5)

All 29 workspace crates are production-ready with comprehensive test coverage (38,768 tests all-features, 36,989 default-features, + 5,136 doc-tests).

Core Scientific Computing Modules

• Linear Algebra (scirs2-linalg): Full decompositions, iterative solvers (GMRES/PCG/BiCGStab/MINRES), tensor decompositions, matrix functions, control theory
• Statistics (scirs2-stats): 40+ distributions, Bayesian inference (NUTS/HMC/SMC), Gaussian processes, survival analysis, Bayesian networks, copulas, causal inference
• Optimization (scirs2-optimize): MIP/SDP/SOCP, Bayesian optimization, NSGA-III, stochastic (SGD/Adam/SVRG), metaheuristics, convex (ADMM/proximal), combinatorial
• Integration (scirs2-integrate): ODE/PDE/SDE/SPDE solvers, LBM, DG, phase-field, BEM, port-Hamiltonian, IGA, QMC
• Interpolation (scirs2-interpolate): RBF, PCHIP, MLS, kriging, spherical harmonics, B-spline surfaces, tensor product, natural neighbor
• Signal Processing (scirs2-signal): CFAR radar, Kalman/EKF/UKF, compressed sensing, MFCC, EMD/HHT, source separation, adaptive filtering, system identification
• FFT (scirs2-fft): Standard/sparse/fractional FFT, NTT, Lomb-Scargle, MUSIC, Prony, DCT/DST all variants, wavelet packets
• Sparse Matrices (scirs2-sparse): LOBPCG/IRAM eigensolvers, AMG, BCSR/ELLPACK/DIA/SELL-C-sigma, recycled Krylov, domain decomposition
• Special Functions (scirs2-special): Mathieu, Coulomb, spherical harmonics, Wigner 3j/6j/9j, Jacobi theta, Fox H-function, Heun, Appell, q-analogs, Weierstrass
• Spatial Algorithms (scirs2-spatial): R*-Tree, Fortune's Voronoi, geodata projections, trajectory analysis, spatial statistics, 3D convex hull
• Clustering (scirs2-cluster): GMM, SOM, HDBSCAN, Dirichlet process, biclustering, topological (Mapper), deep clustering, stream/online
• Data Transformation (scirs2-transform): UMAP, Barnes-Hut t-SNE, sparse PCA, persistent homology, optimal transport, metric learning, multiview learning
• Evaluation Metrics (scirs2-metrics): IoU/AP/mAP detection, NDCG ranking, FID/IS generative, fairness, segmentation, streaming metrics

Advanced Modules

• N-dimensional Image Processing (scirs2-ndimage): Gabor/SIFT/HOG, watershed/SLIC segmentation, optical flow, 3D morphology, medical imaging, texture analysis
• I/O Utilities (scirs2-io): Protobuf/msgpack/CBOR/BSON/Avro, Parquet/Feather/ORC, streaming JSON/CSV/Arrow, cloud storage, HDF5-lite, ETL pipeline
• Datasets (scirs2-datasets): Text/NER/QA, medical imaging, graph benchmarks, recommendation, anomaly, time series (UCR-compatible), synthetic generators

AI/ML Modules

• Automatic Differentiation (scirs2-autograd): JVP/VJP, custom gradients, checkpointing, mixed precision, distributed gradient, Hessian
• Neural Networks (scirs2-neural): Transformers (GPT-2/T5/SWIN/ViT/CLIP/ConvNeXt), GNNs, diffusion models, SNN, capsule networks, PPO/DPO, MoE, knowledge distillation, quantization
• Graph Processing (scirs2-graph): Louvain/Leiden community detection, VF2 isomorphism, Node2Vec, max-flow (Dinic), temporal graphs, hypergraphs, SVG visualization
• Text Processing (scirs2-text): BPE/WordPiece tokenizers, CRF/HMM labeling, FastText, NER, topic modeling (LDA), coreference, knowledge graph extraction
• Computer Vision (scirs2-vision): Stereo depth, ICP, PnP, dense optical flow, SLAM, panoptic/semantic/instance segmentation, SfM reconstruction
• Time Series Analysis (scirs2-series): TFT/N-BEATS/DeepAR forecasting, VAR/VECM/DFM, EGARCH/FIGARCH, FDA, conformal prediction, online ARIMA
• WebAssembly (scirs2-wasm): WasmMatrix operations, TypeScript type bindings, WASM SIMD, Web Worker parallel computation

Advanced Core Features Implemented

• 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

Key Capabilities

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

Installation

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

[dependencies]
scirs2 = "0.6.5"  # Core library with all modules
# Or individual modules:
scirs2-linalg = "0.6.5"  # Linear algebra
scirs2-stats = "0.6.5"   # Statistics
# ... and more

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

Performance Characteristics

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.

Core Module Usage Policy

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

Required Usage Patterns

// ❌ 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.

Development Roadmap

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

Development Branch Status

Current Branch: 0.6.5 (July 31, 2026)

Release Status: All major features through v0.6.5 have been implemented and tested (Waves 53–78, plus the 0.6.0 CUDA-decentralization, 0.6.1 hardening, 0.6.2 Pure-Rust dependency-elimination, 0.6.3 Windows-compatibility hardening, 0.6.4 wasm32 follow-up fix, and 0.6.5 ignore-audit bug-hunt cycles):

• ✅ 29 workspace crates fully implemented
• ✅ 78 waves of development completed
• ✅ Flash Attention 2, QAT, ONNX export, LoRA/DoRA/GPTQ in neural
• ✅ GPU PDE solvers, GPU FFT pipeline, GPU SpMV, real wgpu dispatch (BFS/SSSP/delta-stepping/RBF/CG/Newton/L-BFGS)
• ✅ Temporal GNN, Graph Transformers, NeRF/instant-NGP
• ✅ NUMA-aware par_map_chunks, lock-free data structures, GpuNdarray
• ✅ WebGPU/WASM backend, conformal prediction, Bayesian NNs
• ✅ Complete EML-IR CAS: canonicalize, e-graphs, SMT (OxiZ), JIT, GPU eval, diffgeom, ALiBi
• ✅ Pure-Rust oxicuda-* CUDA backend across 10 crates (0.6.0), real model-serving codegen + DLPack safety fixes (0.6.1), hdf5/tracy/libnuma/opencl3 C-dependency removal (0.6.2), Windows heap-corruption/stack-overflow/path-validation fixes (0.6.3), oxifft wasm32-threading compile-error fix enabling scirs2-wasm/scirs2 meta-crate to resume publishing (0.6.4), full #[ignore]-legitimacy audit fixing autograd's mostly-dead-code live backward pass (0.6.5)
• ✅ 38,768 tests (all-features) / 36,989 (default-features) + 5,136 doc-tests passing
• ✅ Zero warnings policy maintained (clippy, rustdoc, compilation)
• ✅ 81,100+ public API items documented

Next Steps:

• Ready for git commit and version tagging
• mdBook documentation (26 EN + 12 JP pages) completed
• Preparing for crates.io publication

Known Limitations

Python Bindings

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)

Platform Support

Fully Supported Platforms

• ✅ 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 (37,442 tests all-features, 35,668 default-features, + 5,000 doc-tests passing)

Module-Specific Notes

scirs2-autograd

• ✅ Fixed in v0.2.0: Optimizer::update() now correctly updates variables
• ✅ Fixed in v0.2.0: Eliminated warning spam during gradient computation
• ✅ Enhanced in v0.4.0: Custom gradient, checkpointing, FD/Richardson differentiation, JVP/VJP, implicit diff

scirs2-spatial

• ✅ New in v0.2.0: Enhanced Delaunay triangulation with modular Bowyer-Watson architecture (2D/3D/ND)
• ✅ New in v0.2.0: Constrained Delaunay triangulation support
• ✅ New in v0.4.0: R*-Tree, geodata handling, Voronoi Fortune algorithm, trajectory analysis
• ✅ Stable: KD-trees, distance calculations, convex hull, Voronoi diagrams

scirs2-optimize / scirs2-stats / scirs2-special

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

Delivered in v0.4.0 (Previously Planned)

All items from the v0.4.0 roadmap have been implemented:

• ✅ Flash Attention v2 and quantization-aware training (INT4/INT8) in scirs2-neural
• ✅ GPU-accelerated matrix operations (tiled GEMM, batched, adaptive dispatch) in scirs2-linalg
• ✅ Variational inference (ADVI/Laplace/SWAG) and causal inference in scirs2-stats
• ✅ GPU-accelerated PDE solvers (FDM+FEM) and adaptive mesh refinement in scirs2-integrate
• ✅ WebGPU/WASM backend with 76 tests for browser-side GPU compute
• ✅ Temporal GNN (TGAT/TGN) and graph transformers (GraphGPS/Graphormer) in scirs2-graph
• ✅ Distributed optimization (ADMM/PDMM/EXTRA) and hardware-aware NAS (DARTS) in scirs2-optimize
• ✅ Conformal prediction (CQR/RAPS/Mondrian/ACI) and multivariate deep learning in scirs2-series
• ✅ ONNX export support for neural network models
• ✅ mdBook documentation website (26 EN + 12 JP pages)
• ✅ Python PyPI wheel distribution via maturin

Future Enhancements (v0.5.0 Roadmap)

Planned for upcoming releases:

• Extended hardware support: ARM NEON optimization, RISC-V support, embedded systems
• Cloud-native features: container optimization, serverless support, distributed computing
• Domain extensions: quantitative finance, bioinformatics, computational physics
• Enhanced ecosystem integration: improved Python/Julia interoperability, R bindings
• Automated optimization: hardware-aware algorithm selection, auto-tuning frameworks

See TODO.md for the complete development roadmap.

Performance Tests

• Benchmark, GPU-only, and other environment-dependent tests are excluded from regular CI runs (59 tests marked #[ignore] as of the 0.6.5 ignore-legitimacy audit, each carrying a requires-gpu: / requires-env: / slow: / bench: / not-implemented: reason so the taxonomy is machine-checked by cargo-scirs2-policy's ignore_audit lint) to optimize build times. Run with cargo test -- --ignored to execute the full test suite including benchmarks.

Hardware-Dependent Features

• 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

Test Coverage

• Total tests: 37,442 passing across all modules with --all-features (35,668 with default features; plus 5,000 doc-tests)
• 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.

Contributing

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

Areas Where We Need Help

• 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.

Sponsorship

SciRS2 is developed and maintained by COOLJAPAN OU (Team Kitasan).

The COOLJAPAN Ecosystem represents one of the largest Pure Rust scientific computing efforts in existence — spanning 40+ projects, 500+ crates, and millions of lines of Rust code across scientific computing, machine learning, quantum computing, geospatial analysis, legal technology, multimedia processing, and more. Every line is written and maintained by a small dedicated team committed to a C/Fortran-free future for scientific software.

If you find SciRS2 or any COOLJAPAN project useful, please consider sponsoring to support continued development.

https://github.com/sponsors/cool-japan

Your sponsorship helps us:

• Maintain and expand the COOLJAPAN ecosystem (40+ projects, 500+ crates)
• Keep the entire stack 100% Pure Rust — no C/Fortran/system library dependencies
• Develop production-grade alternatives to OpenCV, FFmpeg, SciPy, NumPy, scikit-learn, PyTorch, TensorFlow, GDAL, and more
• Provide long-term support, security updates, and documentation
• Fund research into novel Rust-native algorithms and optimizations

License

Licensed under the Apache License Version 2.0.

Acknowledgments

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

🌐 COOLJAPAN Ecosystem

SciRS2 is part of the COOLJAPAN 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.

📊 Scientific Computing & Data Processing

NumRS2

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

PandRS

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

QuantRS2

Quantum computing library in pure Rust

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

🤖 Machine Learning & Deep Learning

OptiRS

Advanced ML optimization algorithms extending SciRS2

• GPU-accelerated training (CUDA, ROCm, Metal) with 10-50x speedups
• 19 production-ready optimizers: Adam, RAdam, Lookahead, LAMB, learned optimizers
• Neural Architecture Search (NAS), pruning, and quantization
• Distributed training with data/model parallelism and TPU coordination

ToRSh

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

TenfloweRS

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

SkleaRS

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

TrustformeRS

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 (up to 1.67x faster than PyTorch) with quantization support

🎙️ Speech & Audio Processing

VoiRS

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 & Knowledge Graphs

OxiRS

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

🧮 Formal Verification & Constraint Solving

OxiZ

Pure Rust SMT solver - Z3-compatible constraint solving engine

• Drop-in replacement for Z3 with no C/C++ dependencies
• Satisfiability Modulo Theories (SMT) for formal verification and program analysis
• Support for propositional logic, linear arithmetic, bitvectors, and arrays
• Integration with COOLJAPAN ecosystem for mathematical proof and optimization

OxiLean

Pure Rust Interactive Theorem Prover — Calculus of Inductive Constructions

• Zero-dependency kernel (115k SLOC TCB), WASM-first design
• Universe hierarchy, dependent types, inductive types, proof irrelevance, universe polymorphism
• Cargo integration for proof libraries as crates; 11 workspace crates

🌍 Geospatial & Data Processing

OxiGDAL

Pure Rust Geospatial Data Abstraction Library — Production-Grade GDAL Alternative

• 76 workspace crates, ~540k SLoC with 15 format drivers (GeoTIFF/COG, GeoJSON, GeoParquet, Zarr, FlatGeobuf, Shapefile, NetCDF, HDF5, GRIB, JPEG2000, VRT, COPC/LAS, GeoPackage, MBTiles, PMTiles)
• Full CRS transformations (20+ projections, 211+ EPSG codes), cloud-native I/O (S3/GCS/Azure), GPU acceleration
• Cross-platform: WASM, iOS, Android, embedded (no_std); zero C/C++ dependencies in default features

⚖️ Legal Technology

Legalis-RS

Rust Framework for Parsing, Analyzing, and Simulating Legal Statutes — "Governance as Code, Justice as Narrative"

• 23 operational jurisdictions (JP, US, EU, UK, DE, FR, SG, CN, IN, BR, etc.), 46 workspace crates, ~897k SLoC, 14,705 tests
• Deterministic legal logic separated from judicial discretion via LegalResult<T> type
• LLM integration, formal verification (SMT via OxiZ), statute diffing, smart contract export (Solidity/WASM/Ink!), Linked Open Data (RDF)

🤖 AI Infrastructure

OxiRAG

Four-Layer RAG Engine with SMT-Based Logic Verification and Knowledge Graph Support

• 4 layers: Echo (vector search), Speculator (draft verification with SLM), Judge (SMT verification via OxiZ), Graph (GraphRAG)
• Speculative RAG, context-aware prefix caching, on-the-fly distillation, hidden states manipulation
• Native + WASM cross-platform; Candle-based SLM integration

OxiFY

Graph-Based LLM Workflow Orchestration Platform in Pure Rust

• DAG-based workflow engine with type-safe execution; node types: LLM, Retriever, Vision/OCR, Code, IfElse, Tool
• Multi-provider LLM support (OpenAI, Anthropic), vector DB integration (Qdrant, pgvector), MCP support
• 16 workspace crates; ReBAC authorization (Zanzibar-style), JWT/OAuth2, REST API (Axum)

OxiGAF

Pure Rust Gaussian Avatar Reconstruction from Monocular Videos

• 512×512 multi-view generation with IP-Adapter, classifier-free guidance, and latent upsampling
• Differentiable 3D Gaussian Splatting rasterizer (wgpu), FLAME parametric head model
• 7 workspace crates, 796 tests, zero C/Fortran dependencies; PyTorch weight conversion bridge

🎬 Multimedia & Computer Vision

OxiMedia

Pure Rust reconstruction of OpenCV + FFmpeg — Patent-free multimedia and computer vision framework

• 106 workspace crates, ~2.16M SLoC; codec encode/decode (AV1, VP9, Opus, FLAC), container mux/demux (MP4, MKV, MPEG-TS, OGG)
• Streaming protocols (HLS, DASH, RTMP, SRT, WebRTC), transcoding pipelines, filter graphs (DAG-based)
• Computer vision: object detection, motion tracking, video stabilization, scene analysis, shot detection, denoising
• Professional image I/O (DPX, OpenEXR, TIFF), color management (ICC, ACES, HDR), quality metrics (PSNR/SSIM/VMAF)
• Zero C/Fortran dependencies, async-first (Tokio), WASM-ready, patent-free codecs only

🔗 Ecosystem 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.

Future Directions

SciRS2 continues to evolve with ambitious goals:

Near-Term (v0.4.x - v0.5.0)

• SIMD Phase 70-80: Additional advanced mathematical operations and optimizations
• Enhanced GPU Support: Improved multi-backend GPU acceleration and auto-tuning
• Python Ecosystem: Enhanced PyPI distribution, improved NumPy compatibility
• Documentation: Expanded tutorials, cookbook-style examples, migration guides
• Performance Tuning: Further optimization of hot paths

Medium-Term (v0.5.x - v0.6.0)

• 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

Long-Term Vision

• 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.

Community and Support

Get Involved

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.

Resources

• 📖 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

Citation

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 = {0.6.5}
}

Acknowledgments

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.

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Built with ❤️ by COOLJAPAN OU (Team KitaSan)

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