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Contact us and we will arrange a free demo for you and your team.
Supercharge analytics
You can code in traditional object-oriented languages such as C++ or Python while our product takes care of performance.
Your code will be easy to maintain. It will perform 6-100x faster on modern CPUs. Our AADC library will also compute sensitivities automatically when required.
Product highlights
We developed a just-in-time (JIT) compiler tailored for complex repetitive calculations and Automatic Adjoint Differentiation. It utilises native CPU vectorisation (AVX2/512) and is fully multi-thread safe, by design.
Many competing products claim huge increases in performance due to just introducing the AAD method. These claims often compare the performance to the bump-and-revalue approach which makes them overoptimistic.
At MatLogica we never do this. We compare "apples to apples" and our AAD performance gains are expressed relative to the traditional AAD libraries.
Our innovative compiler speeds up the AAD method itself and enables a pricing and scenario analysis unattainable with competing products.
Speed up and compute derivatives
Speed up your analytics by 6-100x and compute sensitivities using AAD, faster than the competitors.
Straightforward integration
MatLogica can be integrated in a few weeks, while others take months or years. The code changes are minimal.
Proven performance
We have impressive benchmarks. Together with Intel, we demonstrate a 1770x speedup for XVA. And there’s more!
How it works
Breakthrough technology
Our JIT compiler sifts through the code in runtime to extract all operations relevant to a specific task. It then optimizes the calculations and generates binary kernels to be executed on the CPU directly. The first kernel takes care of the forward calculations, the second (optional) performs the adjoint calculations for the sensitivities.
Instead of calling the original function, we perform repetitive calculations using the fully vectorized, multi-thread safe, and NUMA aware kernel that delivers the groundbreaking speed-up!
AAD Integration Strategies
Watch this presentation from a Quantitative Finance Conference, where Dmitri Goloubentsev presents AAD integration strategies. You will also see a real-life example of integrating AADC into an open-source library - Quantlib.
A New Approach to Parallel Computing Using Automatic Differentiation
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.
Would you like to know more?
MatLogica’s applications are far beyond traditional AAD.
Our product can be used to effectively develop Machine Learning in C++.
We also know how to do algorithmic differentiation for the Longstaff-Schwartz regression.