Trilinos is an advanced software framework designed to facilitate the development of high-performance scientific applications. Trilinos provides a comprehensive suite of libraries and tools that support a wide range of computational tasks, from linear algebra and optimization to differential equations and mesh generation.
With a focus on scalability and efficiency, Trilinos enables researchers and engineers to tackle complex problems across various fields, including engineering, physics, and applied mathematics. Our modular architecture allows users to customize and extend functionalities, ensuring that Trilinos meets the evolving needs of the scientific community.
Join us in harnessing the power of Trilinos to drive innovation and discovery in computational science!
Quick Links
Explore Trilinos resources:
Getting started
First steps with Trilinos: installation and usage More...
Trilinos GitHub Repository
Access the source code and contribute to Trilinos development. More...
Doxygen Documentation
Explore the API documentation for Trilinos packages. More...
Capabilities
Trilinos offers a robust framework for scientific computing, providing tools and libraries designed to solve complex engineering and scientific problems. Explore our capabilities:
Performance Portability and Core Utilities
Tools for enabling efficient computation across diverse hardware architectures, including CPUs, GPUs, and emerging HPC systems. More...
Linear Solvers
Comprehensive suite of iterative and direct solvers for linear systems, and algorithms for solving large-scale eigenvalue problems. More...
Preconditioners
Methods for accelerating iterative solvers, including domain decomposition and multigrid techniques. More...
Discretization Utilities
Advanced tools for finite element assembly, field evaluation, and meshless approximation methods. More...
Nonlinear, Transient, and Optimization Solvers
Frameworks for solving nonlinear equations, time integration, and optimization problems. More...
Automatic Differentiation
Tools for efficiently computing derivatives using forward and reverse mode automatic differentiation. More...
Uncertainty Quantification
Methods for propagating uncertainty in large-scale multiphysics systems. More...
Partitioning and Load Balancing
Scalable algorithms for distributing computational workloads across processors. More...
Mesh and Geometry Tools
Utilities for mesh generation, manipulation, and I/O, supporting simulations on complex geometries. More...
Interfaces and Adapters
Cohesive interfaces for solvers, preconditioners, and Python wrappers. More...