Interactive Benchmark

ML/AD Libraries

Machine learning frameworks with automatic differentiation

Performance Comparison

Trades
101001K
Scenarios
10K100K500K
Detailed Comparison

Framework Capabilities

Feature comparison across ML/AD frameworks

Feature AADC Python JAX PyTorch Autograd
AD Mode Forward + Reverse Forward + Reverse Reverse only Reverse only
Multi-threading Native Via XLA Limited No
Kernel Recording Yes JIT compilation TorchScript No
NumPy Compatible Full jax.numpy torch.Tensor Full
GPU Support Via NumPy/CuPy Native Native No
Finance Focus Purpose-built General ML Deep learning Educational

AADC provides specialized optimizations for Monte Carlo simulations and derivatives pricing that ML frameworks lack.

Production Reality

Why ML Frameworks Fall Short for Quant

Designed for ML training, not derivatives pricing

JAX / PyTorch

  • Python-only — no C++ codebase integration
  • 12-18s JIT compilation overhead per model change
  • Framework lock-in — must rewrite in framework syntax
  • Not optimized for quant workflows

Autograd

  • Pure Python — orders of magnitude slower
  • No C++ integration path
  • Research tool only — not production-viable

AADC Python Built for quant

  • Same C++ engine as AADC C++
  • Sub-second kernel recording
  • Works with existing NumPy code
  • Production-proven in quant systems
Learn More

Related Resources

How AADC Works

AADC extracts a computational graph (DAG) from existing analytics at runtime and compiles it to optimized machine code, achieving 100-1000× for Python and 6-100× for C++ with automatic derivatives.

Learn More

AADC for Python

Revolutionary Python accelerator delivering 1000x performance improvements for quantitative modeling and Monte Carlo simulations with automatic differentiation. Proven 10x+ faster than JAX, PyTorch, and TensorFlow for quantitative finance workloads.

Learn More

All Benchmarks

Independent benchmarks demonstrating AADC's performance advantages over traditional AAD approaches, GPU implementations, and ML frameworks.

Learn More
Test Environment

Benchmark Environment

All benchmarks executed on enterprise-grade server hardware

System Configuration

CPU2x Intel Xeon Platinum 8280L @ 2.70GHz
Cores56 physical (28 per socket), 112 threads
Architecturex86_64, Cascade Lake
L3 Cache77 MiB (38.5 MiB per socket)
RAM283 GB DDR4
OSLinux kernel 6.1.0-13-amd64 (Debian)
CPU Features: AVX-512, AVX2, FMA, AES-NI

Test Configuration

ModelAsian Option Monte Carlo
DynamicsGeometric Brownian Motion (GBM)
Timesteps252 (daily over 1 year)
GreeksDelta, Rho, Vega (3 sensitivities)
Threads8 (configurable)
SIMDAVX2 (4 doubles/instruction)
Note: AVX-512 (8 doubles/instruction) provides ~1.7x additional speedup on supported hardware

Compilers & Versions

GCC12.2.0 (Debian)
Clang14.0.6 (Debian)
Python3.11.2
NumPy1.26.x
AADC2.0.0
C++ compiled with -O3 -march=native -std=c++17