Algorithmic Differentiation Video Series – Part 3

Many financial institutions have developed large quantitative finance analytics libraries over years, reaching over a million lines of code. Integrate AAD into these libraries becomes a real challenge. Many parts of the code base are affected and performance and memory consumption are major concerns.

The third part of the video series gives practical guidelines on how to cope with the complexities involved when integrating AD into large quant libraries. It outlines integration approaches, details techniques to limit the memory requirements of AAD, explains how external libraries can be integrated into the derivatives calculation and shows how to handle non-differentiable functions. It further gives performance tuning advice.

AAD Video Series Part 3 - Integrating AD in Large Quant Libraries

Video Duration: 21:56

  • AD integration approaches
  • Managing memory: Checkpointing
  • Integrating external libraries
  • Mixing differentiation modes
  • Handling non-differentiable functions
  • Performance tuning

More in this Series: