Product

Python Accelerator with Automatic Adjoint Differentiation

MatLogica Python Accelerator is a revolutionary tool designed to supercharge quantitative models in Python, enabling Monte Carlo simulations and AAD risk calculations at speeds over 1000x faster. Proven 10x+ faster than JAX, PyTorch and TensorFlow for quantitative finance workloads.

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1000×
Faster than Python
10×+
Faster than JAX/PyTorch
90%
Cloud Savings
Days
Not Months to Integrate

Transform Python Performance for Quantitative Finance

1000x faster simulations with automatic differentiation, 10x+ faster than JAX/PyTorch/TensorFlow

Python is loved by quants and data scientists for its simplicity, readability, and versatility. However, its performance has been a bottleneck for intensive computational tasks in quantitative finance—until now. The MatLogica Python Accelerator is a game-changer for professionals and organizations that rely on Python for developing quantitative models and Monte Carlo simulations. With a 1000x reduction in computation times, it enables clean architecture, 90% cloud cost savings, and more complex financial models while maintaining the integrity and readability of Python code. It has been independently benchmarked against JAX, PyTorch and TensorFlow and proves to be 10x+ faster than popular ML frameworks for quantitative workloads including derivatives pricing, risk calculations, and beyond.

Why Python Accelerator is Different:

✓
1000x faster than vanilla Python
✓
10x+ faster than JAX/PyTorch/TensorFlow
✓
AAD built-in for automatic Greeks computation
✓
NumPy compatible out of the box
✓
C++/Python two-way integration
✓
90% cloud savings on compute costs
✓
Days of effort vs months of optimization
✓
Cloud serialization for security
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Mix-Mode Execution

AADC uniquely supports mixed-language computational graphs, combining C++ and Python code into a single optimized kernel. The solution enables direct interaction between Python and C++ components in quantitative libraries. Functions can be recorded across Python and C++ and used to accelerate Monte Carlo simulations and computing sensitivities (Greeks) with unprecedented efficiency using automatic adjoint differentiation. This tool not only represents a leap in computational capability but also a significant stride towards optimizing developers' time and resources in financial modeling.

Python and C++ code integration visualization

Key Features

  • ✓ Python code compiled to native machine code, bypassing interpreter
  • ✓ C++ libraries callable from Python with no performance penalty
  • ✓ Unified optimization across language boundaries
  • ✓ NumPy ufuncs supported for native Python integration

How Mix-Mode Works

1
Record Mixed Functions

Mark inputs/outputs and record computational graphs that span both Python and C++ code

2
Unified Compilation

AADC's JIT compiler creates a single optimized kernel from the mixed-language graph

3
Execute Anywhere

Call the unified kernel from any supported language with full performance

Use Cases

Quant Libraries

Python front-end calling C++ pricing engines with unified AAD

Research to Production

Prototype in Python, optimize critical paths in C++, deploy as unified kernel

Legacy Integration

Wrap existing C++ libraries for Python access without performance penalty

Key Capabilities

Unprecedented Speed

Accelerate your Python Monte Carlo simulations by more than 1000x, making real-time analytics and complex quantitative modeling faster and more efficient than ever before.

Automatic Adjoint Differentiation

Leveraging MatLogica's patented Code Generation AAD™ technology, automatic differentiation capabilities for computing Greeks and sensitivities enhance accuracy and efficiency in your quantitative computations.

Cloud Serialization + 90% Savings

Achieve 90% or more in cloud computing cost reductions for quantitative workloads, optimizing your infrastructure resources and budget while maintaining performance.

Sustainable Green Technology

By optimizing computational time for Python simulations, the Python Accelerator reduces the carbon footprint associated with extensive financial data processing and Monte Carlo simulations.

Advanced NumPy Support

Integrate with existing Python/NumPy code, allowing you to enhance performance without a complete overhaul of your quantitative codebase. Support for NumPy ufuncs and functions out of the box.

Wide Application Range

Ideal for quantitative finance, financial engineering, data science, and anywhere Python is used for quantitative modeling, derivatives pricing, Monte Carlo simulations, and risk calculations.

Rapid Integration, Massive Results

Transform existing projects in days, not months

The MatLogica Python Accelerator offers the unique capability to code effortlessly in Python while achieving ultra-fast results. It brings performance optimization and Automatic Adjoint Differentiation (AAD) straight out of the box, a feat that traditionally demanded extensive effort and sophisticated expertise.

Choose Your Implementation Path

Find the right implementation path for your Python projects

Legacy Optimization

Accelerate existing C++ and Python codebases without rewrites

Greenfield Development

Build new applications with performance and AAD built-in

Open-Source Acceleration

Supercharge QuantLib, ORE, and other open-source libraries

Cloud Optimization

Reduce cloud costs by 50-99% with optimized compute

Get Started with Python Accelerator

Send us a note or book a free demo to see 1000x Python performance in action!

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