Math-Heavy Programming as it’s Supposed to Be

MatLogica gives quantitative and scientific developers the most flexible and easy-to-use tools to quickly build computational analytics that scale with modern hardware. With our technology, you can seamlessly calculate automatic analytical sensitivities.

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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 Central Processing Units (CPUs). Our AADC library will also compute sensitivities automatically when required.

Product Highlights

We developed an optimising just-in-time (JIT) compiler tailored for complex repetitive calculations and Automatic Adjoint Differentiation (AAD). 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 over-optimistic.

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 your competitors.

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Straightforward Integration

MatLogica can be integrated in a few weeks, while others take months or years. The code changes are minimal.

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Proven Performance

We have impressive benchmarks. Together with Intel, we demonstrate a 1770x speedup for XVA. And there’s more!

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Client Benefits

Our Recent Case Study

Learn how a major European bank used MatLogica-enhanced analytics to unlock revenue streams, lower infrastructure costs, improve risk management, AND increase client satisfaction.

With MatLogica, this Tier 2 bank achieved 15-20x speedups compared with manual adjoint differentiation. The shorter but sharper codebase increased the quant team’s efficiency, allowing them to keep up with regulatory requirements and, most importantly, create additional business opportunities. Read now

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.