The default solution for developers who want to extract a 100% performance from their hardware for computationally intensive software “The approach MatLogica takes to acceleration is novel in both its easy-to-use programming interface and high performance it achieves out-of-the-box. Straight-forward and minimal code changes, to make use of the libraries, offer leaps in performance for both xVA Pricing and Greeks Calculations.” “One notable feature of AADC is that complex model calibration procedures do not require any special attention as is often the case with other AAD approaches. For example, we “AADC-record” simple Dupire volatility calculations, regression-based continuation value calibration and even Monte-Carlo based Cheyette model calibrations, with no special care needed to back- propagate adjoints. This has allowed us to eliminate a large amount of complex and hard to maintain code.” "MatLogica offers a unique tool for developing machine learning applications in   C++. It combines the simplicity of object-oriented design and the performance of graph- based languages. For applications like time series analysis, recursive neural nets, and similar, the performance of MatLogica AADC is impressive.   Due to excellent vectorization and good support for multithreading, the execution on a CPU is very fast. In comparison with Tensorflow or Python, custom design implementation from scratch is simpler, making it an excellent tool for the implementation of novel algorithms. Using only C++ for implementation   improves portability and development of large software systems.” "Matlogica offers a graph-based framework in C++, optimized tor finance applications on modern hardware aware and complete win state-or-the-art AAD and proprietary on-the-fIy compilation. Quants will find that Matlogica seamlessly fits with their libraries and effortlessly accelerates pricing and risk by impressive amounts. And they will love the interaction with the founders" MatLogica's product changes the paradigm for quantitative software development eliminating the need to invest in optimisations. Quants can now focus on the models, and performance will be taken care of by MatLogica's JIT compiler "It's hard to develop well-performing models in C++.   I've been very impressed by performance gains up to two orders of magnitude obtained by MatLogica after some integration work on QuantLib, and by the fact that the same work also enabled AAD; especially considering that the library contains hundreds of thousands of lines of code developed over more than 20 years." Mahesh Bhat Principal Engineer, Intel Senior quant at Tier-2 bank ( case study available) Dr. Roland Olsson Associate Professor of Computer Science, Østfold University College Paul A. Bilokon CEO, Thalesians Ltd Visiting Professor, Imperial College London   Luigi Ballabio Co-founder and administrator of QuantLib Antoine   Savine Author of “Modern Computational Finance” * See a whitepaper led by Intel demonstrating up-to 1770x performance improvements for XVA pricing ** See Roland’s research demonstrating state-of-the-art neural network architectures for time series analysis based on MatLogica AADC