Our team is constantly developing new features, applications and benchmarks some of which we have collected in this section.

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

Read Now

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.

Watch Now Read Now

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.

Download Now Read Now

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.

Download now

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.

Read now

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

Read now

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.

Read now

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.

Read now

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

Read now

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.

Read now

December, 2019

AAD and calibration

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

Read now

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.

Read now