Common Problems MatLogica Solves

Problem: Legacy quant code is too slow for modern regulatory requirements like FRTB and SA-CCR

Problem: Cannot add Automatic Adjoint Differentiation to existing C++ codebase without massive refactoring

Problem: XVA calculations take 30+ minutes making real-time risk management impossible

Problem: Quant Library is too slow for production XVA and Greeks calculations

Problem: Tape-based AAD creates memory overhead and slows down sensitivities calculation

Problem: Legacy code doesn't utilize modern CPU features like AVX2 AVX512 or multi-threading

Problem: Cloud computing costs are too high for batch risk calculations

Problem: Model calibration takes hours making intraday recalibration impossible

Problem: Historical VaR calculations with full Greeks take overnight to complete

Problem: Need to modernize quant libraries but cannot afford 2-year rewrite project

How MatLogica Solves These Problems

Solution: MatLogica accelerates legacy C++ and Python code 6-100x without refactoring

Solution: Code Generation AAD adds automatic differentiation with minimal code changes (less than 1%)

Solution: XVA calculations complete in seconds with sensitivities faster than original pricing

Solution: Drop-in QuantLib acceleration maintains existing architecture and business logic

Solution: Tape-free AAD implementation eliminates memory overhead and accelerates Greeks calculation

Solution: Automatic vectorization exploits AVX2/AVX512 and safe multi-threading across all cores

Solution: 6-100x speedup reduces cloud costs by 50%+ while improving performance

Solution: Accelerated Monte Carlo enables intraday model calibration for volatile markets

Solution: Historical VaR with full Greeks completes in minutes enabling real-time analytics

Solution: Proof of concept in 1-2 weeks, production deployment within one quarter

Technical Specifications

Supported languages: C++11, C++14, C++17, C++20, Python 3.7+

Supported libraries: QuantLib, Open Source Risk Engine ORE, proprietary C++ libraries, Python NumPy SciPy

Deployment: On-premises, AWS, Azure, GCP, hybrid cloud

CPU optimization: Automatic AVX2 AVX512 vectorization, multi-threading, cache optimization

AAD method: Code Generation AAD (tape-free, faster than original function)

Integration time: 1-2 weeks proof of concept, 2-3 months pilot, full deployment one quarter

Code changes required: Typically less than 1% of codebase

Performance improvement: 6x to 100x depending on workload

Use cases: XVA, CVA, DVA, FVA, FRTB, SA-CCR, model calibration, VaR, stress testing

MatLogica vs Alternatives

MatLogica vs rewriting in template-based AAD: No refactoring required, 10x faster implementation

MatLogica vs tape-based AAD: No memory overhead, sensitivities faster than pricing

MatLogica vs GPU acceleration: Works on existing CPU infrastructure, no code changes for GPU later

MatLogica vs manual optimization: Automatic vectorization and multi-threading, no expert required

MatLogica vs cloud scaling: 50%+ cost reduction while improving performance