March, 2025
Speeding Up QuantLib with Matlogica’s AADC Library - by Maksim Kozyarchuk
In this blog post, Maksim explores how MatLogica’s ADC 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.
March, 2025
Transforming a Complex C++ System into a Lightning-Fast LiveRisk Service.
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.
December, 2024
Benchmark Results: AADC is 10x+ faster than JAX, PyTorch, and TensorFlow.
A benchmark comparing JAX, PyTorch and Tensorflow vs MatLogica AADC for Quantitative Applications - and the use of AADC in ML applications.
March, 2024
Continuing CPU Performance Gains for Matlogica Financial Analytics
This paper 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.
Jan, 2024
Accelerating Financial Simulations: Code Generation Kernels Explained
In this post, we discuss the origins of performance and the possibilities unveiled by the AADC Code Generation Kernels
Sep, 2023
Automatic adjoint differentiation for special functions involving expectations
As published in Risk.NET. A recent 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.
Dec, 2023
Guilt-free Live-Risk in the Cloud: a New AAD-powered Approach
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
Feb, 2023
AAD tools: comparison of approaches
A detailed analysis of AAD tools - comparing the technology, advantages, and disadvantages of tape-based, code-transformation, code-generation AAD tools and MatLogica AADC
Jan, 2023
How to Transition from Batch Risk to Real-time Risk
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.
July, 2022
An elegant approach to run existing CUDA analytics on both GPU and CPU, with added benefit of AAD
We know NVIDIA GPU offers a massive number of CUDA Cores, but CPUs are not far behind. See our whitepaper that demonstrates how your CUDA analytics can be accelerated by AADC on a CPU with an option of AAD.
June, 2022
Case Study: How a Major European Bank Revolutionised Their Front-Office Risk Management Using MatLogica AADC
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.
June, 2022
Automatic Synthesis of Neurons for Recurrent Neural Nets
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.
December, 2021
Automatic Implicit Function Theorem
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
September, 2021
Adjoint Differentiation for Generic Matrix Functions
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.
October, 2020
More Than a Thousand-fold Speedup for xVA Pricing Calculations with Intel® Xeon® Scalable Processors
Intel-led white paper demonstrating an up to 1770x performance increase for XVA pricing (and 830 for XVA risks!) on Intel processors when using Matlogica AADC. It is open-source and available at GitHub.
May, 2020
A New Approach to Parallel Computing Using Automatic Differentiation: Getting Top Performance on Modern Multicore Systems
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
August, 2020
Open-Source Benchmark
Open-Source Benchmark demonstrating a leap in performance for valuation and AAD risk calculations using AADC on Intel Scalable Xeon CPUs.
December, 2019
AAD and calibration
Remarks on stochastic automatic adjoint differentiation and calibration of financial models.
September, 2019
AAD: Breaking the Primal Barrier
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.