A family of header-only, very fast and memory-friendly hashmap and btree containers.
- hash
- tables
- unordered-map
- unordered-set
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A family of header-only, very fast and memory-friendly hashmap and btree containers.
BS::thread_pool: a fast, lightweight, modern, and easy-to-use C++17 / C++20 / C++23 thread pool library
The C++ Standard Library for Parallelism and Concurrency
Kokkos C++ Performance Portability Programming Ecosystem: The Programming Model - Parallel Execution and Memory Abstraction
Multiphysics Object Oriented Simulation Environment: A robust, modern, extendable, finite element/finite volume framework that excels at creating complex multiphysics simulations.
Lightweight, general, scalable C++ library for finite element methods
The development repository for the deal.II finite element library
SIMD Vector Classes for C++
Kratos Multiphysics (A.K.A Kratos) is a framework for building parallel multi-disciplinary simulation software. Modularity, extensibility and HPC are the main objectives. Kratos has BSD license and is written in C++ with extensive Python interface.
A bleeding-edge, lock-free, wait-free, continuation-stealing tasking library built on C++20's coroutines
Powerful multi-threaded coroutine dispatcher and parallel execution engine
A Python platform to perform parallel computations of optimisation tasks (global and local) via the asynchronous generalized island model.
Collection of self-contained header-only libraries for C++17
An easy-to-use and fast library for task-based parallelism, utilizing coroutines.
C++20 concurrency framework with no compromises. Excellent performance, powerful features, and simple syntax.
A realtime visual programming language for interactive media.
Support vector machines (SVMs) and related kernel-based learning algorithms are a well-known class of machine learning algorithms, for non-parametric classification and regression. liquidSVM is an implementation of SVMs whose key features are: fully integrated hyper-parameter selection, extreme speed on both small and large data sets, full flexibility for experts, and inclusion of a variety of different learning scenarios: multi-class classification, ROC, and Neyman-Pearson learning, and least-squares, quantile, and expectile regression.