Here are some of the famous C++ Numerical Processing Libraries. The use cases of these libraries include Scientific Computing, Financial Modeling, Machine Learning, Data Analysis, and Image and Video Processing.
C++ numerical processing libraries are collections of functions and classes that allow developers to perform numerical computations in C++. These libraries are used for various applications, including scientific computing, data analysis, engineering, and financial analysis.
Let us look at some of these famous libraries.
- Offers support for both CPU and GPU acceleration for many of its algorithms.
- Allows developers to implement complex algorithms quickly.
- Is highly optimized for memory and speed usage.
- Is highly portable and can be used on different platforms, including Windows, Linux, Unix, and Mac OS.
- Includes extensive documentation for developers.
- Is available under the GNU General Public License and is open source.
- Provides a comprehensive suite of numerical tools for pricing financial derivatives and other instruments.
- Provides a wide range of APIs, allowing developers to customize and extend the library for their needs.
- Provides extensive functionality for pricing complex derivatives and instruments, like Monte Carlo simulations, path-dependent options, and exotic options.
- Uses advanced template techniques to enable fast and efficient vectorization of operations.
- Supports broadcasting and universal functions.
- Provides a powerful expression system that allows users to write complex expressions with minimal code.
- Leverages multi-threading, SIMD vectorization, and other techniques to maximize performance.
- Provides a simple and intuitive interface for working with numerical data.
- Offers various customization options, allowing users to tailor the library to their specific needs.
C++ 1982 Version:v0.8 License: Permissive (MIT)
- Offers a wide range of highly optimized SIMD vector operations, allowing users to use their processor's capabilities.
- Supports GPU acceleration, allowing users to leverage the power of their GPUs for faster numerical processing.
- Offers an easy-to-use API for distributed computing.
- Offers an extensive suite of robust, reliable, and accurate numerical algorithms for solving challenging scientific and engineering problems.
- Is designed with a modular architecture, allowing users to add or remove components quickly and easily.
- Is designed for scalability, allowing for efficient performance on a wide range of computing resources, from single-processor computers to parallel supercomputers.
- Supports both OpenCL and CUDA-enabled GPUs.
- Has Vector Expressions (VEX) language, which allows one to express numerical operations concisely and readably.
- Offers a range of data-distribution options, allowing users to customize their application's data layout.