Question 1

How does AADC modernize legacy quant systems without requiring a complete rewrite?

Accepted Answer

AADC uses a type replacement approach where you substitute your numeric types (like double) with AADC's idouble type. This requires less than 1% code changes while preserving your existing business logic, institutional knowledge, and proven algorithms. The JIT compiler then automatically optimizes your code and adds AAD capabilities without modifying your core logic.

Question 2

What performance improvements can I expect when modernizing legacy code with AADC?

Accepted Answer

Legacy systems typically see 6-100x speedup after AADC integration. Greeks calculations become faster than the original pricing, and cloud infrastructure costs can be reduced by 50% or more. The exact improvement depends on your specific workload, but most clients see positive ROI within 6-12 months.

Question 3

Can AADC help with FRTB and SA-CCR regulatory compliance?

Accepted Answer

Yes. FRTB and SA-CCR require extensive sensitivity calculations that are computationally expensive with traditional approaches. AADC's automatic differentiation provides all required Greeks and sensitivities in a single pass, making regulatory calculations 6-100x faster. This enables firms to meet regulatory deadlines and run more scenarios within compliance windows.

Question 4

How long does legacy system integration typically take?

Accepted Answer

Most legacy integrations follow a phased approach: Assessment (2-4 weeks), Proof of Concept on representative workload (4-6 weeks), Production Integration (2-3 months), and Full Rollout (varies by scope). The minimal code changes required (less than 1%) significantly reduce integration risk and timeline compared to traditional modernization approaches.

Question 5

Why should I use AADC for a new quant development project instead of building from scratch?

Accepted Answer

AADC enables 3-4x faster development because you can write clean, readable code without worrying about performance optimization or manual AAD implementation. You get production-grade performance from day one, automatic differentiation built-in, and can focus entirely on your models and business logic rather than low-level optimization.

Question 6

How does AADC compare to vendor solutions for new quant systems?

Accepted Answer

AADC offers 60-80% cost reduction compared to traditional vendor solutions. There are no per-core licensing fees, unlimited scalability, and full ownership of your technology stack. You avoid vendor lock-in, can customize without limitations, and maintain complete control over your roadmap and IP.

Question 7

Can I prototype quickly with AADC and still have production-ready performance?

Accepted Answer

Yes, this is one of AADC's key advantages. You can write straightforward Python or C++ code during prototyping, and AADC's JIT compiler automatically optimizes it to production-grade performance. There's no need to rewrite or optimize code when moving from prototype to production—the same code runs efficiently in both environments.

Question 8

What programming languages does AADC support for new development?

Accepted Answer

AADC natively supports C++ and Python. For C++, you use the idouble type replacement. For Python, the AADC Python Accelerator provides native integration with NumPy and common quant libraries. Both approaches deliver the same performance benefits and automatic differentiation capabilities.

Question 9

How does AADC accelerate QuantLib and ORE?

Accepted Answer

AADC integrates with QuantLib and ORE through type replacement, substituting numeric types with AADC's optimized types. This provides 6-100x speedup without modifying the open-source library code. You get community-validated algorithms with proprietary-grade performance, plus automatic AAD for all models.

Question 10

Can I use AADC with QuantLib to replace expensive vendor solutions?

Accepted Answer

Yes, many organizations use AADC-accelerated QuantLib/ORE to retire expensive vendor solutions. You get the benefit of community-validated, peer-reviewed algorithms with performance that matches or exceeds commercial alternatives, all without vendor licensing fees or lock-in.

Question 11

Does AADC work with the latest versions of QuantLib?

Accepted Answer

AADC maintains compatibility with current QuantLib and ORE versions. The integration approach is designed to work with the standard QuantLib API, so upgrades to new QuantLib versions typically require minimal adjustment. MatLogica provides guidance on version compatibility.

Question 12

What's the advantage of AADC over hand-coded AAD for QuantLib?

Accepted Answer

Hand-coding AAD for QuantLib is extremely time-consuming and error-prone, requiring deep expertise in both the library internals and adjoint methods. AADC provides automatic differentiation with no manual implementation required, works across all QuantLib models, and is mathematically guaranteed to be correct. Development time is reduced from months to days.

Question 13

How much can AADC reduce my cloud computing costs?

Accepted Answer

AADC typically reduces cloud costs by 50-99%, depending on your workload. The 6-1000x efficiency improvement means you need significantly fewer compute resources for the same calculations. Many clients see savings of approximately $100K per $1M of cloud spend, with ROI achieved in 2-4 months.

Question 14

How does AADC's cloud deployment work securely?

Accepted Answer

Source code stays on-premises—only binary kernels are deployed to cloud infrastructure. Source code is not included in kernels. The architecture supports hybrid on-prem/cloud deployments with elastic scaling.

Question 15

Can I combine cloud optimization with legacy modernization?

Accepted Answer

Yes, cloud optimization works with any implementation path. Most clients combine cloud savings with another route for maximum ROI. For example, you can modernize legacy code (Route 1) and immediately benefit from reduced cloud costs, achieving both faster calculations and lower infrastructure spending.

Question 16

How quickly will I see cloud cost savings after implementing AADC?

Accepted Answer

Cloud cost savings are typically visible in your next invoice after deployment. Since AADC reduces compute requirements by 6-1000x, you immediately need fewer instances, shorter run times, or both. Most organizations achieve positive ROI on cloud optimization within 2-4 months.

Question 17

Does AADC work with AWS, Azure, and Google Cloud?

Accepted Answer

Yes, AADC is cloud-agnostic and works with all major cloud providers including AWS, Azure, and Google Cloud. The binary kernel deployment model is platform-independent, allowing you to deploy across multiple clouds or maintain a hybrid architecture.

Question 18

What is AADC and how does it differ from traditional AAD implementations?

Accepted Answer

AADC (Automatic Adjoint Differentiation Compiler) is a JIT graph compiler that automatically provides Adjoint Algorithmic Differentiation without manual coding. Unlike traditional AAD that requires extensive code modification and expertise, AADC works through simple type replacement, handles all the complexity automatically, and delivers 6-1000x performance improvements.

Question 19

What makes AADC different from other automatic differentiation tools?

Accepted Answer

AADC is specifically designed for quantitative finance workloads with a unique JIT compilation approach. It requires minimal code changes (type replacement only), provides superior performance (6-1000x speedup), handles complex financial models natively, and supports production deployment. Unlike academic AD tools, it's battle-tested in tier-1 financial institutions.

Question 20

How do I choose the right AADC implementation route for my organization?

Accepted Answer

Consider your starting point: If you have working legacy code that's too slow, choose Legacy Optimization (Route 1). For new projects or vendor replacement, choose Greenfield Build (Route 2). If you're using or want to use QuantLib/ORE, choose Open-Source Acceleration (Route 3). Cloud Cost Optimization (Route 4) can be combined with any other route. MatLogica offers consultations to help determine the best path.

Question 21

What kind of support does MatLogica provide during AADC implementation?

Accepted Answer

MatLogica provides comprehensive support including initial assessment, proof-of-concept development, integration guidance, performance optimization, and ongoing technical support. The team includes quantitative finance experts who understand both the technology and the business context of your implementation.

Question 22

Is AADC suitable for real-time trading applications?

Accepted Answer

Yes, AADC is used in real-time trading environments. The performance improvements (Greeks faster than original pricing) make it ideal for trading desks that need real-time sensitivities. The JIT compilation approach means there's no interpretation overhead, and calculations run at near-native speed.

Question 23

Can AADC handle exotic derivatives and complex structured products?

Accepted Answer

Yes, AADC handles the full spectrum of derivative products including exotic options, structured products, and complex multi-asset instruments. The automatic differentiation works regardless of model complexity, and the JIT compiler optimizes even the most intricate payoff structures.

Question 24

What is the typical ROI timeline for AADC implementation?

Accepted Answer

ROI timelines vary by implementation route: Cloud Optimization sees positive ROI in 2-4 months through immediate infrastructure savings. Legacy Optimization typically achieves ROI in 6-12 months. Greenfield builds see ROI in 12-18 months but with 60-80% cost savings versus vendor alternatives. Open-Source acceleration provides immediate cost benefits through eliminated licensing fees.

Question 25

How does AADC ensure calculation accuracy and correctness?

Accepted Answer

AADC's automatic differentiation is mathematically proven to produce correct derivatives. The adjoint method computes exact sensitivities (not numerical approximations), eliminating finite-difference errors. The JIT compiler preserves numerical accuracy while optimizing performance, and results can be validated against existing systems during integration.

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