Presented at WBS 2025 Conference in Palermo, this work demonstrates how combining mathematical smoothing with AAD solves the autocallable Greeks challenge. Achieve 90% computational cost reduction with production-ready C++ implementation, open-source benchmarks, and validated convergence for the $104B autocallable market.

In this blog post, Maksim explores how MatLogica's AADC supercharges QuantLib, dramatically accelerating pricing and risk calculations without requiring a rewrite. By leveraging cutting-edge compiler techniques and automatic differentiation, this integration unlocks unprecedented performance gains for quants and developers alike.

ORE (Open Risk Engine) built on top of QuantLib presents a formidable challenge for quants and developers. While powerful, its complexity can make even seemingly simple tasks like setting up a Monte Carlo model quite daunting. This blog post demonstrates how we can transform ORE into a streamlined LiveRisk service for basic FX linear products using AADC.

A comprehensive benchmark comparing JAX, PyTorch and TensorFlow vs MatLogica AADC for Quantitative Applications—and the use of AADC in ML applications. Independent results with downloadable source code.

This whitepaper by Intel demonstrates substantial performance gains from 5th Gen Intel® Xeon® processors, including up to 2.08x improvement compared to 3rd Gen Xeon CPUs, as well as up to 1.26x gains from Intel AVX-512 technology compared to predecessor Intel AVX2 technology—using MatLogica AADC for quantitative finance workloads.

In this post, we discuss the origins of performance and the possibilities unveiled by the AADC Code Generation Kernels for Monte Carlo simulations and derivatives pricing.

As published in Risk.NET. A peer-reviewed paper by José Brito, Andrei Goloubentsev, and Evgeny Goncharov on Risk.net used MatLogica's AADC to efficiently compute gradients for functions involving squares of expectations as is typical for calibration in quantitative finance.

Discover a target architecture for a cloud-based Live Risk that uses Code Generation AAD™ to achieve fast and cheap computation of sensitivities, enabling guilt-free Live Risk with significant cost savings.

A detailed analysis of AAD tools—comparing the technology, advantages, and disadvantages of tape-based, code-transformation, code-generation AAD tools and MatLogica AADC for quantitative finance applications.

We present an elegant way to transition from overnight risk calculations to live risk without embarking on a multi-year IT transformation project. We show how the Automated Implicit Function Theorem (AIFT) and a modern Automatic Adjoint Differentiation (AAD) tool can be used in a real production code to achieve an 'always on' Risk Server, and we outline the steps required to transition.

We know NVIDIA GPU offers a massive number of CUDA Cores, but CPUs are not far behind for quantitative workloads. See our whitepaper that demonstrates how your CUDA analytics can be accelerated by AADC on a CPU with the option of AAD.

MatLogica's AADC enabled the client to supercharge their analytics by introducing AAD for risk computations and to accelerate pricing and scenario analysis. The MatLogica-enhanced analytics unlocked new revenue streams, lowered infrastructure costs, and improved risk management with 15-20x speedups.

Prof. Roland Olsson and his team used MatLogica's AADC to design state-of-the-art neural network architectures for time series analysis. It is up-to 3x more accurate than the available cutting-edge methods and the training time is several times lower due to MatLogica's technology.

The paper demonstrates a way to apply the Implicit Function Theorem in a not widely known way, which is important for practical AAD application and performance, particularly with complex calibration in quantitative finance.

No doubt, AAD is amazing. However, implementing it in practice has a lot of subtleties. For instance, how to deal with operations requiring an SVD decomposition? Our researchers have found an elegant solution to this problem.

Intel-led whitepaper demonstrating an up to 1770x performance increase for XVA pricing (and 830x for XVA risks!) on Intel processors when using Matlogica AADC. It is open-source and available at GitHub.

A paper in Parallel Universe Magazine №40 featuring a new approach that turns object-oriented, single-thread, scalar code into AVX2/AVX512 vectorized multi-thread and thread-safe lambda functions with no runtime penalty for quantitative finance applications.

Open-Source Benchmark demonstrating a leap in performance for valuation and AAD risk calculations using AADC on Intel Scalable Xeon CPUs. Code available for independent verification.

Remarks on stochastic automatic adjoint differentiation and calibration of financial models for derivatives pricing.

Dmitri Goloubentsev and Evgeny Lakshtanov wrote an article for Wilmott Magazine on how merging Code Transformation and Operator Overloading techniques leads to a major performance boost for automatic differentiation.