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.
Get Started
$ dotnet add package Dew.Math.LinuxReadme
Dew.Math.Linux
High-performance vectorized numerical computation library for Linux
Dew.Math.Linux is the Linux-native accelerated edition of the Dew.Math numerical library. It provides high-performance matrix and vector computation using native BLAS/LAPACK acceleration, multithreading, and CPU feature dispatch. It is designed for HPC compute nodes, scientific servers, simulation engines, data processing pipelines, microservices, and cloud container deployments where Linux is the primary execution environment.
Core Numerical Capabilities
Dense Linear Algebra
- BLAS/LAPACK-based linear algebra with native acceleration
- Decompositions: SVD, QR, LQ, LU, eigenvalue problems, least-squares, rank-revealing methods
- Complex-valued matrices and vectorized complex arithmetic
- High-throughput matrix multiplication and factorization kernels
Sparse Matrix Computing
- Sparse matrix formats suitable for large systems
- Direct sparse solvers: Pardiso and UMFPACK
- Krylov-based iterative solvers: CG, BiCG, GMRES, and variants
- Preconditioning support for improved convergence stability
Optimization and Modeling
- Non-linear curve fitting (Levenberg-Marquardt and trust-region variants)
- Linear programming and constrained optimization
- Numerical root solving and non-linear system solution
Probability and Statistical Methods
- Random number generators for multiple probability distributions
- CDF/PDF evaluation and stochastic sampling
- Monte Carlo workflows and statistical summaries
Polynomial and Analytical Math
- Polynomial evaluation, interpolation, piecewise polynomial models, and spline fitting
- Numerical integration and differentiation routines
- Special functions including Airy, Bessel, Gamma-related, Legendre and elliptic integrals
Performance Architecture
- Native Linux BLAS/LAPACK acceleration
- CPU dispatching for AVX2 and AVX-512 instruction sets
- Scalable multithreading with low-GC pressure memory pool
- Capacity-based memory model to minimize allocation overhead
- Subranges (views) allow slicing matrix and vector memory without copying
- Optional OpenCL GPU compute integration (where supported)
Typical workloads see significant acceleration compared to purely managed math implementations.
Platform and Deployment Model
Dew.Math.Linux contains Linux-native runtime binaries.
Supported Linux Distributions (glibc-based):
- Red Hat Enterprise Linux (RHEL) 7.x, 8.x, 9.x
- Rocky Linux 7.x, 8.x, 9.x
- AlmaLinux 7.x, 8.x, 9.x
- CentOS 7.x (legacy), CentOS Stream 8 and 9
- Ubuntu and Debian (when glibc version matches above compatibility levels)
Deployment Environments
- HPC clusters (SLURM / PBS / LSF / Kubernetes batch compute nodes)
- Scientific Linux compute nodes
- Cloud compute machines (AWS, Azure, GCP)
- Docker / Podman / Kubernetes container images (no extra runtime installer required)
- On-prem microservice pipelines and model-serving backends
Drop-in Replaceable Architecture (Important)
Dew.Math.Linux shares the same namespaces and class layout as:
- Dew.Math (Windows accelerated)
- Dew.Math.Core (managed-only)
This allows switching runtime editions without modifying your source code.
Example workflow:
- Develop on Windows using Dew.Math.Core or Dew.Math.
- For Linux deployment, uninstall the Windows package and install Dew.Math.Linux.
- Rebuild. No code changes required.
No conditional compilation. No dual code paths. No API differences.
Package Relationship
| Package | Platform | Acceleration Model |
|---|---|---|
| Dew.Math | Windows | Native AVX2 / AVX-512 acceleration |
| Dew.Math.Linux | Linux | Native AVX2 / AVX-512 acceleration |
| Dew.Math.Core | All | Managed-only (portable, no native) |
Advanced Memory Model
- Vectors and Matrices expose a Capacity property to reduce allocation count
- Subrange support allows nested view-based operations without copying
- Object pool provides thread-local memory caching with minimal allocator contention
- Designed for high concurrency and deterministic performance under parallel workloads
- Nearly all math operations avoid internal memory allocations
Trial Version Usage on Linux
The trial version of Dew.Math.Linux must run under a debugger.
Since Visual Studio is not available on Linux, use one of the following:
- Visual Studio Code with the .NET debugger extension
- JetBrains Rider with debugging enabled
Running outside a debugger (for example, as a service or from command line) triggers evaluation mode.
Examples
(While UI samples are Windows-based, the computation API is the same on Linux.)
-
Dew Research GitHub Home: https://github.com/Dew-Research
-
WinForms / WPF / ASP.NET (.NET Framework) examples: https://github.com/Dew-Research/DewLabStudio-NETFramework-Samples
-
WinForms (.NET 6-9) examples: https://github.com/Dew-Research/DewLabStudio-NETCore-Samples
Documentation
-
API Reference: https://www.dewresearch.com/Help/VSNET/MtxVec/contents.html
-
User Guide (PDF): http://downloads.dewresearch.com/downloads/MtxVecV6-CSharp.pdf
-
Product Page: https://www.dewresearch.com/products/mtxvec
-
NuGet Publisher Profile: https://www.nuget.org/profiles/DewResearch
-
License: https://www.dewresearch.com/downloads/LicenseNET.pdf
License
Commercial license. See DewMathLicense.txt included in this package. All Dew.* NuGet packages on nuget.org are trial versions and require running inside a debugger.