{"repo":"auto-differentiation/QuantLibAAD","free":true,"listed":false,"github":"https://github.com/auto-differentiation/QuantLibAAD","clone":"git clone https://github.com/auto-differentiation/QuantLibAAD.git","description":"QuantLib with AAD","language":"C++","stars":42,"topics":["algorithmic-differentiation","quantitative-finance","risk-analysis"],"license":null,"category":"trading","readme_excerpt":"QuantLibAAD: QuantLib with XAD Automatic Differentiation in C++ Part of the XAD ecosystem - the fastest automatic differentiation library for C++. If this is useful to you, please star the core XAD repository. As a demonstrator of integration of the XAD automatic differentiation tool with real-world code, the latest release of QuantLib can calculate risks (sensitivities) with the help of XAD. The performance achieved on sample applications is many-fold superior to what has been reported previously with other tools. This demonstrates production quality use of the XAD library in a code-base of several hundred thousand lines. This repository contains integration headers, examples, and tests required for this integration. It is not usable stand-alone. JIT Compilation Support XAD is optimized for computing sensitivities efficiently in a single evaluation pass using adjoint mode. For workflows that require repeated evaluation across many scenarios — such as Monte Carlo simulations, XVA calculations, regulatory stress testing, or scenario-based risk analysis — XAD also supports recording computations into a JITGraph that can be compiled and re-evaluated efficiently using a JIT backend. xad-codegen is a high-performance native code generation backend that compiles recorded computation graphs to optimized x86-64 machine code, with support for AVX2 SIMD vectorization (4 paths per instruction). xad-codegen is available under a separate commercial license — contact us for more informatio","default_branch":null,"files":null,"tree":[],"storefront":"/r/auto-differentiation","claimed":false,"request_supported":{"post":"https://gitbuyer.com/r/auto-differentiation/QuantLibAAD/request-supported","requests":0},"note":"indexed from public GitHub; nothing is for sale on this page. Clone it from GitHub. Paid listings live at /search."}