Interactive Benchmark

Language Comparison

Same algorithm across Python, C++, Julia, and Haskell

Performance Comparison

Trades
101001K
Scenarios
10K100K500K
Performance Breakdown

Price-Only vs Price + Greeks

AADC's AAD advantage becomes more pronounced with Greeks

Implementation Price Only Price + Greeks Greeks Overhead
AADC Python 0.54s 0.67s +23%
AADC C++ v2.1.0 0.70s 1.08s +54%
Optimised C++ (Safe) 0.27s 1.44s +433%
Julia 3.9s 15.5s +297%
Haskell* 162s 646s +299%
2.1x
AADC Python faster than Optimised C++ (with Greeks)
+23%
AADC Greeks overhead (vs +433% bump-revalue)
2.0x
Optimised C++ faster for price-only (no Greeks needed)

*Haskell took significantly longer at this scale. All measurements at 10 trades × 100K scenarios.

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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.

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AADC for C++

Combine ease of integration with high performance. A toolkit that delivers 6-1000x speedups and automatic derivatives with <1% code changes. Focus on your models—AADC handles performance and AAD automatically.

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AADC Scalability (C++)

AADC C++ achieves near-linear scaling across threads (28.5x at 32 threads), trades (100x for 10-1000), and scenarios (50x for 10K-500K). Memory scales only with scenarios (~1.9 MB per 1K). Predictable performance enables accurate capacity planning.

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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