Cutting Edge Analytics

Adjoint Algorithmic Differentiation

Adjoint Algorithmic Differentiation (AAD) offers an efficient and robust alternative to compute sensitivities compared to the traditional bump-and-revalue approach (finite difference). However its implementation into large existing software applications is often considered challenging.

Xcelerit offers its AAD expertise and provides a full range of solutions. This ranges from tailored training programmes, advisory to help clients fit AAD into their exisiting application, and the fast and comprehensive open-source XAD tool.

Whitepaper: A Guide to Adjoint Algorithmic Differentiation

In this paper we will zoom on Algorithmic Differentiation as an efficient and robust alternative to compute sensitivities. We cover the theory and then focus on practical examples. The paper further gives guidelines on how to cope with the memory requirements, handle parallelisation, and how to incorporate external library functions.

  • Forward and adjoint algorithmic differentiation
  • Checkpointing techniques
  • Handling external function calls
  • Parallelising AD code
  • Differentiation of large code bases
  • This field is for validation purposes and should be left unchanged.

AAD Advisory

Xcelerit provides consulting and advisory services to help clients deploy AAD solutions. Xcelerit experts have successfully delivered AAD projects for a number of tier 1 investment banks. Xcelerit team has developed a unique workflow to help clients AAD-enable large in-house quantitative finance analytics libraries (typically multi-millions lines of code), while respecting memory constraints and achieving large performance gains.

  • This field is for validation purposes and should be left unchanged.
  • Integrate AAD into large code bases
  • Develop user-friendly AAD code
  • Overcome memory constraints
  • Achieve large performance gains

AAD Training

Xcelerit provides training to give clients solid background in the theoretical, software and computational aspects of adjoint algorithmic differentiation (AAD). Xcelerit trainers share their unique expertise to help clients AAD-enable large in-house quantitative finance analytics libraries (typically multi-millions lines of code), while respecting memory constraints and achieving large performance gains.

  • This field is for validation purposes and should be left unchanged.
  • AAD Background
  • Basic and advanced AAD code
  • Applying AAD to real-world code
  • Develop clean maintainable AAD code
  • AAD & Monte-Carlo applications
  • AAD & PDE solvers
  • AAD & model calibration
  • Reducing memory in adjoint code
  • Maximising AAD code performance

About Xcelerit

Xcelerit is a leading provider of cutting edge analytics solutions for Quantitative Finance, Engineering, and Research. The company’s mission is to accelerate the pace of business, innovation, and scientific discovery.

Xcelerit extensive experience enables the firm to deliver full solutions from expert training, advisory, and bespoke products. Its distinct competitive advantage derives from the unique combination of domain specialist knowledge and cutting-edge software expertise. This allows the firm to forge the most efficient solutions to better address our clients’ needs.

Xcelerit has received recognition as a finalist in the Red Herring Europe Top 100 award, the Red Herring Top 100 Global award, and the only two-time winner of HPCwire’s “Best use of High Performance Computing in Financial Services” award. Our satisfied customers include the leading firms in investment banking, asset management, and insurance. We inform and are informed through close partnerships with the world’s leading technology firms, including IBM, Intel, Nvidia, and many others.

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