OxiCUDA

Pure Rust CUDA replacement -- cuBLAS, cuDNN, cuFFT, cuSPARSE, cuSOLVER, cuRAND and beyond in ~783K lines of safe Rust across 73 crates.

OxiCUDA replaces the entire NVIDIA CUDA Toolkit software stack with type-safe, memory-safe Rust code. The only runtime dependency is the NVIDIA driver (libcuda.so / nvcuda.dll); no CUDA SDK, no nvcc, no C/C++ toolchain is needed at build time. Optimized PTX assembly is generated directly from Rust data structures, and a built-in autotuner benchmarks kernel variants per GPU architecture to achieve near-peak throughput from Turing through Blackwell.

Architecture

+---------------------------------------------------------------+
|   SciRS2  |  OxiONNX  |  TrustformeRS  |  ToRSh              |
|   (Scientific Computing / ML / Inference Ecosystem)           |
+-------------------------------+-------------------------------+
                                |
+-------------------------------v-------------------------------+
|                         OxiCUDA                               |
|                     (Pure Rust GPU)                            |
|                                                               |
|  Vol.1 Foundation (4 crates)                                  |
|  +----------+ +--------+ +---------+ +---------+             |
|  | Driver   | | Memory | | Launch  | | Runtime |             |
|  +----------+ +--------+ +---------+ +---------+             |
|                                                               |
|  Vol.2 Codegen (2 crates)                                     |
|  +-----------+ +------------+                                 |
|  | PTX Gen   | | Autotune   |                                 |
|  +-----------+ +------------+                                 |
|                                                               |
|  Vol.3 Linear Algebra    Vol.4 Deep Learning                  |
|  +-------------+         +-------------+                      |
|  | BLAS        |         | DNN         |                      |
|  +-------------+         +-------------+                      |
|                                                               |
|  Vol.5 Scientific Computing (4 crates)                        |
|  +------+ +--------+ +--------+ +------+                     |
|  | FFT  | | Sparse | | Solver | | Rand |                     |
|  +------+ +--------+ +--------+ +------+                     |
|                                                               |
|  Vol.6 Signal    Vol.7 Comp.Graph  Vol.8 Training (2)         |
|  +---------+     +----------+      +-------+ +-------+        |
|  | Signal  |     | Graph    |      | Train | | Quant |        |
|  +---------+     +----------+      +-------+ +-------+        |
|                                                               |
|  Vol.9 Inference (3 crates)        Vol.10 RL                  |
|  +-------+ +------------+ +----+   +------+                   |
|  | Infer | | Dist-Infer | | LM |   |  RL  |                   |
|  +-------+ +------------+ +----+   +------+                   |
|                                                               |
|  Backends (7 crates)                                          |
|  +----------+ +--------+ +-------+ +--------+                 |
|  | backend  | | prims  | | Metal | | Vulkan |                 |
|  +----------+ +--------+ +-------+ +--------+                 |
|  +--------+ +-------+ +-----------+                           |
|  | WebGPU | | ROCm  | | LevelZero |                           |
|  +--------+ +-------+ +-----------+                           |
+-------------------------------+-------------------------------+
                                |
+-------------------------------v-------------------------------+
|              libcuda.so  (NVIDIA Driver, runtime only)        |
|              No SDK  /  No nvcc  /  No C Toolchain            |
+---------------------------------------------------------------+

Feature Highlights

Vol.1 -- Foundation (4 crates, 26,438 SLoC)

• Dynamic driver loading via libloading -- zero build-time SDK dependency
• DeviceBuffer<T> with Rust ownership semantics -- Send + Sync, RAII
• Type-safe launch! macro with compile-time grid/block validation
• CUDA Runtime API layer for high-level device management

Vol.2 -- PTX Codegen & Autotuner (2 crates, 46,081 SLoC)

• Rust DSL that generates PTX IR covering SM 7.5 through SM 10.0
• Tensor Core support: WMMA, MMA, WGMMA instruction generation
• Built-in autotuner with 3-tier dispatch (cached / tuned / default)
• Disk-based PTX cache keyed by kernel hash + GPU architecture

Vol.3 -- BLAS (1 crate, 27,226 SLoC)

• Full BLAS Level 1/2/3 (axpy, gemv, gemm, trsm, syrk, ...)
• GEMM dispatch: SIMT, Tensor Core, Split-K paths
• Batched GEMM: standard, strided, grouped
• Precision coverage: F16, BF16, TF32, F32, F64, FP8
• Elementwise ops (relu, gelu, sigmoid, silu) and reductions (softmax, variance)

Vol.4 -- DNN (1 crate, 37,428 SLoC)

• Convolution: implicit GEMM, im2col, Winograd 3x3, direct, fused Conv+BN+Act
• FlashAttention forward/backward, PagedAttention, decode attention
• MoE: top-k routing, token permutation, fused MoE kernel
• Normalization: BatchNorm, LayerNorm, RMSNorm, GroupNorm
• Pooling: max, average, adaptive, global
• Resize: nearest, bilinear, bicubic
• Quantization: FP8, INT8, block-scaled FP4

Vol.5 -- Scientific Computing (4 crates, 55,718 SLoC)

• FFT: Stockham, radix-2/4/8, mixed-radix, Bluestein, C2C/R2C/C2R, 2D/3D
• Sparse: CSR/CSC/COO/BSR/ELL, SpMV, SpMM, SpGEMM, SDDMM, ILU(0)/IC(0)
• Solver: LU, QR, SVD, Cholesky, eigendecomp, CG, BiCGSTAB, GMRES
• Rand: Philox, MRG32k3a, XORWOW, Sobol, uniform/normal/Poisson

Vol.6 -- Signal Processing (1 crate, 6,595 SLoC)

• Audio: MFCC, STFT, Mel filterbank, spectral features
• Image: Gaussian blur, Sobel edge detection, morphological ops
• DCT: Types I-IV with fast algorithms
• DWT: Haar, Daubechies wavelets
• Filtering: IIR/FIR filters, Butterworth, Chebyshev
• Correlation: cross-correlation, autocorrelation

Vol.7 -- Computation Graph (1 crate, 4,949 SLoC)

• CUDA Graph capture API (StreamCapture, GraphCapture)
• Execution plan with dependency-sorted node scheduling
• Event-based inter-node synchronization
• Sequential + parallel graph executors

Vol.8 -- GPU Training (2 crates, 10,532 SLoC)

• Mixed precision training (AMP): FP16/BF16 + loss scaling
• Gradient accumulation and clipping; EMA (exponential moving average)
• LR schedulers: cosine, warmup, cyclic, polynomial
• GPU-fused optimizers: Adam, AdamW, SGD, RMSProp, LAMB
• Checkpointing (model save/load)
• Quantization: INT8/INT4/FP8 weight quantization, block-scaled

Vol.9 -- Inference Engine (3 crates, 14,692 SLoC)

• KV-cache with paged attention (PagedKvCache) and prefix caching
• Speculative decoding
• Distributed inference pipeline (tensor/pipeline parallelism)
• LM inference: BPE tokenizer, vocabulary management, sampling strategies

Vol.10 -- Reinforcement Learning (1 crate, 5,536 SLoC)

• Replay buffers: Uniform, Prioritized (PER), N-step
• Policy distributions: Categorical, Gaussian (SAC reparameterization), Deterministic
• Advantage estimators: GAE, TD(λ), V-trace, Retrace(λ)
• Loss functions: PPO, DQN, Double-DQN, SAC, TD3
• Observation/reward normalization with Welford running stats
• Environment abstractions: Env, VecEnv (auto-reset)

Backends (7 crates, 28,400 SLoC)

• Backend trait abstraction for multi-GPU-runtime portability
• CUB-equivalent GPU primitives (scan, reduce, sort, histogram)
• Metal (macOS), Vulkan Compute, WebGPU, AMD ROCm, Intel oneAPI (LevelZero)

Pure Rust, Minimal Dependencies

OxiCUDA is built on a strict Pure Rust policy with minimal external dependencies. The entire codebase compiles with cargo build alone -- no C compiler, no Fortran runtime, no CUDA SDK, no nvcc, no pkg-config.

Dependency	Purpose	Type
libloading	Dynamic .so/.dll loading at runtime	Pure Rust
thiserror	Ergonomic error type derivation	Pure Rust
num-complex	Complex number types (FFT)	Pure Rust
half	FP16/BF16 types (optional)	Pure Rust
serde / serde_json	Autotune result DB (optional)	Pure Rust

The only runtime requirement is the NVIDIA GPU driver (libcuda.so on Linux, nvcuda.dll on Windows). On macOS the crate compiles but returns UnsupportedPlatform at runtime.

Quick Start

use oxicuda::prelude::*;

fn main() -> Result<(), oxicuda::Error> {
    // Initialize driver and select GPU device
    let device = Device::get(0)?;
    let ctx = Context::new(device)?;
    let stream = Stream::new(&ctx)?;

    // Allocate device memory
    let mut d_a = DeviceBuffer::<f32>::zeroed(1024)?;
    let mut d_b = DeviceBuffer::<f32>::zeroed(1024)?;
    let mut d_c = DeviceBuffer::<f32>::zeroed(1024)?;

    // Copy host data to device
    d_a.copy_from_host(&host_a)?;
    d_b.copy_from_host(&host_b)?;

    // Launch a GEMM: C = alpha * A @ B + beta * C
    let handle = BlasHandle::new(&stream)?;
    handle.gemm(
        Transpose::None, Transpose::None,
        m, n, k,
        1.0f32,            // alpha
        &d_a, lda,
        &d_b, ldb,
        0.0f32,            // beta
        &mut d_c, ldc,
    )?;

    stream.synchronize()?;

    // Copy result back to host
    let mut result = vec![0.0f32; m * n];
    d_c.copy_to_host(&mut result)?;
    Ok(())
}

Crate Overview

Crate	CUDA Equivalent	Description	SLoC	Tests
Vol.1 -- Foundation
oxicuda-driver	Driver API	FFI, device/context/stream/event/module	13,508	383
oxicuda-memory	cuMemAlloc	DeviceBuffer, PinnedBuffer, unified, pool	5,297	211
oxicuda-launch	cuLaunchKernel	Dim3, LaunchParams, launch! macro	5,112	214
oxicuda-runtime	CUDA Runtime	High-level cudaRT API layer	2,521	46
Vol.2 -- PTX Codegen & Autotuner
oxicuda-ptx	nvcc / CUTLASS	PTX IR, codegen DSL, Tensor Core gen	31,764	934
oxicuda-autotune	--	Search space, benchmark, tuning DB	14,317	421
Vol.3 -- Linear Algebra
oxicuda-blas	cuBLAS	BLAS L1/L2/L3, GEMM, batched, elementwise	27,226	722
Vol.4 -- Deep Learning
oxicuda-dnn	cuDNN	Conv, attention, MoE, norm, pool, quantize	37,428	1,006
Vol.5 -- Scientific Computing
oxicuda-fft	cuFFT	Stockham, radix-2/4/8, Bluestein, 1D/2D/3D	13,039	350
oxicuda-sparse	cuSPARSE	CSR/CSC/COO/BSR/ELL, SpMV, SpMM, SpGEMM	12,943	331
oxicuda-solver	cuSOLVER	LU, QR, SVD, Cholesky, eig, CG, GMRES	17,724	396
oxicuda-rand	cuRAND	Philox, MRG32k3a, Sobol, distributions	12,012	341
Vol.6 -- Signal Processing
oxicuda-signal	--	Audio/image DSP, DCT, DWT, IIR/FIR filters	6,595	240
Vol.7 -- Computation Graph
oxicuda-graph	CUDA Graphs	Graph capture, dep-sorted exec, events	4,949	175
Vol.8 -- GPU Training
oxicuda-train	--	AMP, grad accum/clip, LR schedulers, optimizers	6,214	167
oxicuda-quant	--	INT8/INT4/FP8 quantization, block-scaled	4,318	150
Vol.9 -- Inference Engine
oxicuda-infer	--	KV-cache, paged attention, speculative decode	5,632	186
oxicuda-dist-infer	--	Tensor/pipeline parallelism, distributed infer	3,279	80
oxicuda-lm	--	BPE tokenizer, vocab, sampling strategies	5,781	226
Vol.10 -- Reinforcement Learning
oxicuda-rl	--	Replay buffers, policy dists, PPO/DQN/SAC/TD3	5,536	200
Backends
oxicuda-backend	--	Backend trait abstraction	484	10
oxicuda-primitives	CUB	GPU scan, reduce, sort, histogram	4,502	142
oxicuda-metal	--	Metal compute backend (macOS)	4,395	152
oxicuda-vulkan	--	Vulkan Compute backend	5,116	86
oxicuda-webgpu	--	WebGPU backend	3,948	129
oxicuda-rocm	--	AMD ROCm backend	3,739	104
oxicuda-levelzero	--	Intel oneAPI / LevelZero backend	6,216	103
Vol.17 -- Generative AI
oxicuda-gen	--	Diffusion (DDPM/DDIM/DPM-Solver++/Flow Matching), CFG, VAE, LoRA	8,470	365
Vol.18 -- Graph Neural Networks
oxicuda-gnn	--	CSR/COO/Hetero graphs, GCN/GAT/GraphSAGE/GIN, pooling	10,698	401
Vol.19 -- State Space Models
oxicuda-mamba	--	HiPPO-NPLR, S4D/S5 selective scan, Mamba SSM, RWKV	11,535	514
Vol.20 -- Vision Transformers
oxicuda-vision	--	ViT, patch embedding, CLIP towers	10,829	496
Vol.21 -- Audio/Speech ML
oxicuda-audio	--	Conformer, Wav2Vec2, CTC/RNN-T, WaveNet, SpecAugment, x-vector	11,215	458
Vol.22 -- Time-Series Forecasting
oxicuda-timeseries	--	TCN, NHiTS, PatchTST, TimesNet, iTransformer, RevIN	10,493	333
Vol.23 -- Bayesian Deep Learning
oxicuda-bayes	--	Variational inference, MC Dropout, Deep Ensembles, SWAG, Laplace	10,203	385
Vol.24 -- Federated Learning
oxicuda-federated	--	FedAvg/FedProx/SCAFFOLD/FedAdam, DP, secure aggregation	7,969	351
Vol.25 -- Neural Architecture Search
oxicuda-nas	--	DARTS, supernet, NSGA-II, hardware-aware FLOPs predictor	6,577	224
Vol.26 -- Self-Supervised Learning
oxicuda-ssl	--	SimCLR/MoCo/BYOL/Barlow Twins/MAE/DINO	11,706	373
Vol.27 -- Adversarial Robustness
oxicuda-adversarial	--	FGSM/PGD/CW/TRADES/MART	9,006	387
Vol.28 -- Multi-Modal Learning
oxicuda-multimodal	--	Cross-modal attention, CLIP/ImageBind	7,788	275
Vol.29 -- Continual Learning
oxicuda-continual	--	EWC/SI/PackNet/GEM/DER++	12,642	427
Vol.30 -- 3D Geometry & Point Clouds
oxicuda-geometry3d	--	FPS/kNN/PointNet/DGCNN/ICP	9,552	315
Vol.31 -- Physics-Informed Neural Networks
oxicuda-pinn	--	PINN/NeuralODE/FNO/DeepONet	12,599	493
Vol.32 -- RLHF & Alignment
oxicuda-rlhf	--	DPO/IPO/KTO/ORPO/PPO-RLHF/reward-model	5,767	217
Vol.33 -- Meta-Learning
oxicuda-meta	--	MAML/FOMAML/ANIL/Reptile/ProtoNet	8,249	225
Vol.34 -- Neural Radiance Fields
oxicuda-nerf	--	NeRF/Instant-NGP/Mip-NeRF/TensoRF	6,878	227
Vol.35 -- Mixture of Experts
oxicuda-moe	--	Switch/Top-K/Expert-Choice/Soft-MoE	4,906	153
Vol.36 -- Tabular Deep Learning
oxicuda-tabular	--	TabNet/SAINT/FT-Transformer/NODE	7,811	214
Vol.37 -- Anomaly Detection
oxicuda-anomaly	--	DeepSVDD/LOF/COPOD/Mahalanobis/IsoForest	15,255	362
Vol.38 -- Quantum Simulation
oxicuda-quantum	--	State-vector/VQE/QAOA/QML-kernels	7,156	221
Vol.39 -- Approximate Nearest Neighbor
oxicuda-ann	--	HNSW/IVF/PQ/IVFPQ/LSH	7,509	202
Vol.40 -- Recommender Systems
oxicuda-recsys	--	ALS/BPR/NCF/DeepFM/SASRec/LightGCN	10,169	253
Vol.41 -- Causal Inference
oxicuda-causal	--	NOTEARS/IPW/S-T-X-learners/DML/CausalForest	21,669	594
Vol.42 -- Parameter-Efficient Fine-Tuning
oxicuda-peft	--	LoRA/QLoRA/AdaLoRA/Prefix-Tuning	14,694	479
Vol.43 -- Knowledge Distillation
oxicuda-distill	--	Hinton/FitNets/AT/CRD/DML/ZSKD	7,029	246
Vol.44 -- Optimal Transport
oxicuda-ot	--	Sinkhorn/EMD/Gromov-Wasserstein/Wasserstein-kmeans	19,461	480
Vol.45 -- Spiking Neural Networks
oxicuda-snn	--	LIF/IF/BPTT/STBP/SLAYER/STDP/ANN→SNN	10,683	329
Vol.46 -- Differential Privacy
oxicuda-privacy	--	DP-FTRL/DP-Adam/RDP/zCDP/PRV/OUE/RAPPOR	13,029	530
Vol.47 -- Hyperdimensional Computing
oxicuda-hdc	--	Binary/integer/complex HVs, AM/classifier	5,725	214
Vol.48 -- Evolutionary Algorithms
oxicuda-evol	--	CMA-ES/NSGA-II/MOEA-D/NEAT/DE/PSO/ACO	15,366	424
Vol.49 -- Topological Data Analysis
oxicuda-tda	--	Vietoris-Rips/persistent-homology/Mapper	6,480	209
Vol.50 -- Tensor Networks
oxicuda-tn	--	MPS/MPO/DMRG/TEBD/PEPS/TT-cross/CP-ALS/einsum	23,576	427
Vol.51 -- Sequence Models
oxicuda-seq	--	HMM/CRF/Kalman/EKF/Viterbi/Baum-Welch	13,336	384
Vol.52 -- Numerical PDE Solvers
oxicuda-pde	--	FDM/FEM/spectral/multigrid/CG	11,332	384
Vol.53 -- Manifold Learning
oxicuda-manifold	--	t-SNE/UMAP/LLE/Isomap/Diffusion-Maps/SMACOF	19,877	388
Vol.54 -- Statistical Inference
oxicuda-stats	--	t-test/ANOVA/KS/bootstrap/regression/power	17,685	542
Vol.55 -- Streaming Sketches
oxicuda-sketch	--	HyperLogLog/Count-Min/Bloom/t-Digest/MinHash	8,533	332
Vol.56 -- Survival Analysis
oxicuda-survival	--	Kaplan-Meier/Cox-PH/AFT/Fine-Gray/Brier	25,296	628
Vol.57 -- Convex Optimization
oxicuda-cvx	--	LP/QP/SOCP/SDP/ADMM/FISTA/proximal-gradient	12,790	387
Vol.58 -- Compressed Sensing
oxicuda-cs	--	OMP/CoSaMP/IHT/AMP/K-SVD/LASSO/nuclear-norm	6,127	108
Vol.59 -- Graph Algorithms
oxicuda-graphalg	--	BFS/DFS/Dijkstra/MST/flow/matching/SCC/TSP	6,392	139
Vol.60 -- Numerical Analysis
oxicuda-numeric	--	Root-finding/quadrature/special-functions/ODE/interpolation	6,061	212
Vol.61 -- 2D Computational Geometry
oxicuda-geom2d	--	Delaunay/Voronoi/convex-hull/sweep-line	6,754	204
Umbrella
oxicuda	--	Umbrella re-export crate	21,994	521
		Total	~782,571	23,535

Feature Flags

Flag	Default	Description
driver	on	CUDA driver API layer
memory	on	Device/pinned/unified memory
launch	on	Kernel launch primitives
ptx	off	PTX IR codegen DSL
autotune	off	Runtime autotuner with disk cache
blas	off	BLAS L1/L2/L3 and GEMM
dnn	off	Deep learning ops (conv, attention, MoE, norm)
fft	off	FFT transforms
sparse	off	Sparse matrix operations
solver	off	Linear solvers (LU, QR, SVD, Cholesky, CG)
rand	off	GPU random number generation
primitives	off	CUB-equivalent GPU primitives
pool	off	Async memory pool (CUDA 11.2+)
vulkan	off	Vulkan Compute backend
metal	off	Metal backend (macOS)
webgpu	off	WebGPU backend
rocm	off	AMD ROCm backend
level-zero	off	Intel oneAPI / LevelZero backend
wasm-backend	off	WebAssembly + WebGPU browser target
gpu-tests	off	Enable GPU hardware tests
full	off	Enable all features

Performance Targets

Operation	Target vs CUDA	Notes
SGEMM (FP32)	>= 95% cuBLAS	Autotuned tile sizes
HGEMM (FP16)	>= 95% cuBLAS	Tensor Core WMMA/MMA
Batch GEMM	>= 95% cuBLAS	Stream-K scheduling
Convolution (FP16)	>= 90% cuDNN	Implicit GEMM + Winograd
FlashAttention	>= 90% FA2	Tiled, causal mask
FFT (power-of-2)	>= 90% cuFFT	Stockham radix-2/4/8
SpMV (CSR)	>= 85% cuSPARSE	Architecture-tuned
LU / QR / SVD	>= 85% cuSOLVER	Blocked panel factorization

Supported GPU Architectures

Architecture	SM	Codename	Key Features
Turing	7.5	TU10x	INT8 Tensor Cores, RT Cores
Ampere	8.0	GA100	TF32, FP64 Tensor Cores, Async Copy
Ampere	8.6	GA10x	Third-gen Tensor Cores
Ada Lovelace	8.9	AD10x	FP8 Tensor Cores
Hopper	9.0	GH100	WGMMA, TMA, FP8, DPX
Blackwell	10.0	GB10x	FP4, Fifth-gen Tensor Cores

Platform Support

Platform	Status	Notes
Linux x86_64	Full support	Primary development target
Windows x86_64	Full support	nvcuda.dll loaded at runtime
macOS (ARM/x86)	Compile-only	Returns UnsupportedPlatform at runtime

Building

# Default build (no GPU features)
cargo build

# With all GPU features
cargo build --features "ptx,autotune,blas,dnn,fft,sparse,solver,rand"

# Full build (all features including backends)
cargo build --features full

# Check without GPU
cargo check --all-targets

Testing

# Unit tests (no GPU required)
cargo test

# Full test suite with GPU hardware
cargo test --features gpu-tests

# Run with nextest
cargo nextest run --all-features

Roadmap

Released (v0.1.8) -- 2026-05-21 (23,535 tests passing, 783K SLoC, 73 crates)

• Vol.1: Driver, Memory, Launch, Runtime -- foundation layer (4 crates)
• Vol.2: PTX codegen DSL, autotuner engine (2 crates)
• Vol.3: Full BLAS L1/L2/L3 with Tensor Core GEMM, SYR2K two-operand cross-product variant
• Vol.4: Convolution, FlashAttention, MoE, normalization, pooling, quantization
• Vol.5: FFT, sparse, solver, RNG (4 crates)
• Vol.6: Signal processing -- audio/image DSP, DCT, DWT, IIR/FIR filters
• Vol.7: Computation graph -- capture API, dep-sorted scheduling, parallel executor
• Vol.8: GPU training -- AMP, optimizers, LR schedulers, checkpointing, quantization (2 crates)
• Vol.9: Inference engine -- KV-cache, speculative decode, distributed infer, LM (3 crates)
• Vol.10: Reinforcement learning -- replay buffers, policy dists, PPO/DQN/SAC/TD3
• Backends: Metal, Vulkan, WebGPU, ROCm, LevelZero (7 crates)
• Vol.17: Generative AI -- diffusion schedulers, CFG, VAE, LoRA
• Vol.18: Graph Neural Networks -- GCN/GAT/GraphSAGE/GIN, pooling
• Vol.19: State Space Models -- HiPPO-NPLR, S4D/S5, Mamba SSM, RWKV
• Vol.20: Vision Transformers & CLIP -- ViT, patch embedding, dual-tower CLIP
• Vol.21: Audio/Speech ML -- Conformer, Wav2Vec2, CTC/RNN-T, WaveNet, SpecAugment
• Vol.22: Time-Series Forecasting -- TCN, NHiTS, PatchTST, TimesNet, iTransformer, RevIN
• Vol.23: Bayesian Deep Learning -- variational inference, MC Dropout, Ensembles, Laplace
• Vol.24: Federated Learning -- FedAvg/FedProx/SCAFFOLD/FedAdam, DP, secure aggregation
• Vol.25: Neural Architecture Search -- DARTS, supernet, NSGA-II, hardware-aware predictor
• Vol.26--61: SSL, Adversarial, Multimodal, Continual, 3D Geometry, PINN, RLHF, Meta-Learning, NeRF, MoE, Tabular, Anomaly, Quantum, ANN, RecSys, Causal, PEFT, Distillation, OT, SNN, DP, HDC, Evolutionary, TDA, Tensor Networks, Sequence Models, PDE, Manifold, Statistics, Sketches, Survival, CVX, Compressed Sensing, Graph Algorithms, Numerical Analysis, 2D Geometry

Next

• Published documentation on docs.rs
• GPU hardware benchmark validation (CI regression tracking)
• v1.0 completion criteria verification (see TODO.md)

Quick Links

• API Documentation
• GitHub Repository
• COOLJAPAN Ecosystem
• Changelog

Related COOLJAPAN Projects

Project	Description
SciRS2	Scientific computing (NumPy/SciPy equivalent)
ToRSh	Tensor operations (PyTorch equivalent)
TrustformeRS	Transformer models
OxiONNX	ONNX neural network inference
OxiBLAS	Pure Rust BLAS
OxiFFT	Pure Rust FFT

License

Licensed under the Apache License, Version 2.0. See LICENSE for details.

Copyright

(C) 2026 COOLJAPAN OU (Team KitaSan)