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.
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.
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.
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
Open Source Benchmark
Open-Source Benchmark demonstrating a leap in performance for valuation and AAD risk calculations using AADC on Intel Scalable Xeon CPUs.
AAD and calibration
Remarks on stochastic automatic adjoint differentiation and calibration of financial models.
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.