./nugetz

#AVX2

24 packages tagged with “AVX2

Dew.Math.Linux

Dew.Math.Linux provides the high-performance numerical computation capabilities of Dew.Math, but with native acceleration binaries compiled for Linux. It is designed for compute clusters, scientific servers, containerized deployment environments, and performance-critical Linux workloads. Core Numerical Capabilities: - Dense linear algebra (BLAS/LAPACK): decomposition and eigenvalue routines optimized for AVX/AVX-512 - Sparse matrix operations with Pardiso and UMFPACK direct solvers and Krylov-based iterative solvers - Complex-valued linear algebra and spectral computations - Polynomial operations, splines, interpolation, approximate function models, and spectral transforms - Probability distributions, stochastic simulation, and random number engines - Special function library suitable for numerical physics, statistics, and differential systems - Optimization algorithms for non-linear fitting, gradient models, linear programming, and statistical inference Performance Architecture: - Linux-native accelerated BLAS/LAPACK libraries - Multithreaded vectorized math with CPU feature dispatch (AVX/AVX2/AVX-512) - Low-overhead memory allocator for stable scaling under parallel workloads - Optional OpenCL GPU integration for Linux compute environments Platform Model: - Contains Linux native runtime binaries - For Windows native acceleration use: Dew.Math - For portable managed-only computation use: Dew.Math.Core Use Dew.Math.Linux for Linux HPC compute nodes, microservice model engines, AI research pipelines, and scalable distributed scientific processing.

v6.3.1037.4K
dotnetcsharplinuxnativenative-acceleration

Dew.Math

Dew.Math is the Windows-optimized high-performance numerical computation library for .NET. It provides a vectorized matrix and vector math environment with native runtime acceleration, multithreaded execution, and extensive algorithm libraries for scientific, engineering, financial, AI/ML and signal processing workloads. Core Numerical Capabilities: - Dense linear algebra (BLAS, LAPACK): SVD, QR, LQ, LU, eigenvalue problems, least-squares, rank reveals - Sparse matrix support: direct solvers (Pardiso, UMFPACK), iterative solvers (CG, BiCG, GMRES), preconditioning strategies, structured sparse formats - Complex number computation with fully vectorized math operations - Polynomial arithmetic, interpolation, splines, rational approximations, Chebyshev basis transforms - Numerical differentiation, root solving, non-linear systems, ODE support for stiff and non-stiff cases - Probability distributions (over 30 families), random number generators, Monte Carlo methods - Special mathematical functions (Airy, Bessel, Gamma-related, elliptic integrals, Legendre, etc.) Optimization and Modeling: - Non-linear curve fitting with Levenberg-Marquardt and trust-region refinements - Direct and constrained optimization (Simplex/Nelder–Mead, BFGS, Conjugate Gradient, LP, dual-phase simplex, Gomory cutting plane) - Vectorized expression parser for dynamic formula construction and symbolic-style evaluation Performance Architecture: - Native accelerated BLAS/LAPACK kernels with automatic CPU dispatch (AVX, AVX2, AVX-512) - Scalable multithreading with a lock-free memory allocator for low-GC overhead - Optional OpenCL GPU offloading for supported device targets Platform Model: - Contains Windows native acceleration binaries - For Linux native acceleration use: Dew.Math.Linux - For a pure managed, portable edition use: Dew.Math.Core Use Dew.Math when you require **maximum numerical performance on Windows** for HPC, simulation, economic modeling, data analytics, or scientific visualization workflows.

v6.3.1054.8K
dotnetcsharpwindowslinuxmacos

Dew.Lab.Studio

Dew Lab Studio bundles the Dew.Math, Dew.Signal, and Dew.Stats libraries into a unified package suitable for scientific, engineering, financial, and real-time signal-processing applications. It includes full numerical computation, vectorized signal analysis and filtering, statistical modeling, probability distributions, spectral transforms, optimization, and advanced data workflows. This package also integrates visualization via Dew.Math.TeePro, Dew.Signal.Tee, and Dew.Stats.Tee, which extend TeeChart for high-performance plotting of large datasets, spectrograms, signal traces, matrices, probability distributions, and live streaming data. No hard WinForms linking is introduced into your own code; the visualization libraries depend on WinForms internally while allowing you to use WinForms, WPF, Avalonia, or custom UI frameworks. Included Components: - Dew.Math (Windows native-accelerated numerical computation) - Dew.Signal (real-time DSP, filtering, transforms, spectral and streaming signal analysis) - Dew.Stats (probability distributions, statistical modeling, hypothesis testing, inference) - Dew.Math.TeePro (high-speed charting extensions for numerical data) - Dew.Signal.Tee (spectral / time-frequency / oscilloscope plotting extensions) - Dew.Stats.Tee (statistical visualization and histogramming helpers) Usage Model: - Use Dew Lab Studio for Windows desktop and server applications requiring interactive visualization, scientific and engineering debugging workflows, data interpretation, or real-time signal monitoring. - For Linux HPC systems: use Dew.Lab.Studio.Linux (accelerated native Linux builds). - For maximum cross-platform portability: use Dew.Lab.Studio.Core (managed-only .Core builds). Platform Notes: - Dew.*.Tee packages depend on WinForms internally. Therefore, projects targeting net8.0-windows or net9.0-windows must enable the Windows Desktop framework (WinForms).

v6.3.106.4K
dotnetcsharpwindowsmathsignal-processing

Dew.Signal.Linux

Dew.Signal.Linux is the Linux-native accelerated edition of the Dew.Signal digital signal processing library. It provides a full suite of DSP algorithms built on top of Dew.Math.Linux, delivering high-performance numerical processing with multithreaded AVX/AVX2/AVX-512 hardware acceleration. This package is intended for scientific servers, compute clusters, HPC pipelines, digital instrumentation, real-time data acquisition, industrial analytics, embedded Linux platforms, and cloud CPU workloads. Filter Design and Processing: - IIR filters: Butterworth, Chebyshev I/II, Elliptic, Bessel - Transformations: bilinear, matched-Z, frequency remapping, pole-zero and state-space formulations - FIR filters: window methods, Remez exchange, Hilbert transformers, differentiators, integrators, Savitzky–Golay smoothers, envelope detection - Multirate DSP: decimation, interpolation, half-band polyphase filters, zoom-spectrum workflows Spectral and Frequency-Domain Analysis: - FFT-based spectral estimation and spectrum analyzer infrastructure - Parametric estimators: Yule–Walker, Burg, Covariance, Modified Covariance - Chirp-Z transform, time-frequency spectrograms, bispectrum, bicoherence, coherence, transfer function estimation, phase unwrapping - Real/complex cepstrum and inverse cepstrum - Spectral statistics: noise floor, SFDR, THD, THDN, SINAD, RMS, SNR Signal Modeling, Streaming, and Synthesis: - White, pink, brownian, blue, violet and deterministic test signal generators - Continuous streaming components and dataflow processing units for real-time measurement systems - High-performance convolution, correlation, DCT/IDCT, interpolation and filtering kernels - Spectral forecasting based on controlled peak selection Integration and Platform Model: - Uses Dew.Math.Linux for native-accelerated numerical backend - Does **not** require WinForms or TeeChart (visualization is optional and external) - Suitable for server, embedded, batch compute, containerized, and headless execution Dew.Signal.Linux provides the same API surface as Dew.Signal, but is optimized specifically for Linux compute environments where high throughput and deterministic performance are required.

v6.3.109.6K
dotnetlinuxdspsignal-processingfiltering

Dew.Stats.Linux

Dew.Stats.Linux is the Linux-native accelerated edition of the Dew.Stats statistical computing library. It provides a comprehensive suite of tools for probability distributions, hypothesis testing, regression, multivariate analysis, experimental design, and time-series modeling, powered by the high-performance vectorized numerical backend in Dew.Math.Linux. This edition is designed for Linux-based compute servers, HPC pipelines, analytics microservices, research clusters, data acquisition systems, and real-time industrial environments. Statistical Capabilities: - Probability distributions (PDF, CDF, inverse CDF) for 36+ discrete and continuous models - Random number generators and parameter estimation - Descriptive statistics, histograms, ogives, quantiles, outlier analysis Hypothesis Testing and Inference: - Parametric tests (t, Z, F, Chi-Squared, Bartlett, Hotelling T²) - Non-parametric tests (Wilcoxon, Sign, Mann–Whitney, Anderson–Darling, Shapiro–Wilk, KS) - Confidence intervals, residual diagnostics, model fitness evaluation Regression and Statistical Modeling: - Linear, multiple linear, logistic, Poisson, ridge and nonlinear regression - ANOVA and ANCOVA - Principal Component Regression and regularization workflows Multivariate and Structural Analysis: - PCA (correlation/covariance) with eigen decomposition - PCA residuals, factor rotation, Bartlett tests, item analysis - Classical Multidimensional Scaling and dimensionality interpretation Time Series Modeling and Forecasting: - ACF and PACF analysis - ARMA, ARIMA and ARAR models - Exponential smoothing (single/double/triple) - Box-Ljung significance testing and forecasting evaluation High-Level Statistical Workflow Components: - TMtxANOVA, TMtxMulLinReg, TMtxNonLinReg, TMtxPCA, TMtxHypothesisTest, TMtxBinaryTest, TMtxMDScaling Platform Characteristics: - Uses **Dew.Math.Linux** for native BLAS/LAPACK acceleration with AVX2/AVX512 dispatch - Highly scalable under multi-threaded workloads - No Windows or WinForms dependencies - Headless execution suitable for batch, service, and compute-node environments Dew.Stats.Linux provides the full analytical capabilities of Dew.Stats, optimized specifically for Linux-based CPU compute environments.

v6.3.108.1K
dotnetlinuxstatisticsregressionanova

Dew.Stats

Dew.Stats provides a complete high-performance statistical and data analysis library built on the vectorized numerical engine of Dew.Math. It is designed for scientific analytics, quantitative finance, machine learning preprocessing, laboratory data processing, industrial measurement evaluation, and real-time statistical diagnostics. Probability and Random Distributions: - PDF, CDF and inverse CDF for 36+ continuous and discrete probability distributions - Random number generators, parameter estimation, Monte-Carlo sampling, bootstrap methods Descriptive and Exploratory Statistics: - Central moments, percentiles, quantiles, ranks, trimmed means, variance and covariance analysis - Histograms, ogives, cumulative series, outlier detection, normalization, scaling transforms Hypothesis Testing and Inference: - Parametric tests: t-test, Z-test, F-test, Chi-Squared, Bartlett, Hotelling T² - Non-parametric tests: Wilcoxon, Mann-Whitney, Sign, Anderson–Darling, Shapiro–Wilk, KS - Confidence intervals, p-values, power analysis Regression and Statistical Modeling: - Linear, multiple, ridge, logistic, Poisson, and nonlinear regression - ANOVA and ANCOVA models, principal component regression - Robust regression and regularization options Multivariate and Structure Analysis: - PCA (Principal Component Analysis), MDS (Multidimensional Scaling), item analysis, factor modes Time Series Analysis and Forecasting: - Autocorrelation (ACF), partial autocorrelation (PACF), smoothing, ARMA/ARIMA/ARAR models - Rolling statistics, forecasting diagnostics, Box-Ljung significance tests High-Level Components: - TMtxANOVA, TMtxMulLinReg, TMtxNonLinReg, TMtxPCA, TMtxHypothesisTest, TMtxBinaryTest, TMtxMDScaling — encapsulated workflow components for rapid application development Integration: - Uses Dew.Math for optimized vector/matrix operations with AVX2/AVX512 hardware acceleration - Optional charting and statistical visualization available through **Dew.Stats.Tee** (separate package) Dew.Stats is designed for reproducible, numerically stable computation in research, industrial, and engineering applications.

v6.3.105.5K
dotnetcsharpstatisticsregressionanova

Dew.Signal

Dew.Signal is a high-performance digital signal processing library built on top of Dew.Math, providing a comprehensive suite of optimized algorithms for real-time signal analysis, filtering, spectral estimation, modeling, and streaming signal workflows. The library is designed for scientific, engineering, audio, RF, vibration, instrumentation, control, and monitoring applications requiring both numerical accuracy and hardware-level performance on multi-core CPUs with AVX2/AVX512 support. Filter Design and Processing: - IIR filter design (Butterworth, Chebyshev I/II, Elliptic, Bessel), analog and digital domains - Order estimation, frequency transformations, bilinear and matched-Z transforms - State-space and zero-pole-numerator-domain modeling with group delay and stability analysis - FIR filter design using window methods and Remez exchange algorithm - FIR/Hilbert/differentiator/integrator design, Savitzky–Golay filtering, envelope detectors - Multi-rate filtering: half-band polyphase filters, decimation, interpolation, zoom-spectrum analysis Nonlinear and Adaptive Filters: - Sample-and-hold, sample-and-decay, and median filtering - High-quality rate conversion with 160dB stopband attenuation at high speed Spectral and Frequency-Domain Analysis: - FFT-based spectrum analyzer components with real-time UI integration support - Parametric spectral estimation: Yule–Walker, Burg, Covariance, Modified Covariance - Chirp-Z transform, bispectrum, bicoherence, transfer function, coherence estimation - Peak interpolation and peak-tracking enhancements, phase unwrapping - Real and complex cepstrum, inverse cepstrum - Spectral statistics: noise floor, SFDR, THD, THDN, SINAD, RMS, SNR measurements Signal Synthesis, Streaming, and Measurement: - Signal generators with stack-based vectorized function evaluation - Audio capture/playback with monitoring and triggering support - Data streaming and file format components for continuous acquisition and logging Forecasting and Time-Series Modeling: - Spectral forecasting based on controlled peak selection, enabling clear component-based prediction Integration and Extensions: - Works seamlessly with Dew.Math numeric structures (vectors/matrices) - Optional high-performance charting available via a separate Dew.Signal.Tee package (Windows visualization) - Part of the Dew Lab Studio ecosystem for unified math, DSP, and statistical analysis workflows Designed for reproducibility, determinism, and stable numerical behavior in long-running or real-time environments.

v6.3.105.7K
dotnetcsharpdspsignal-processingfiltering