[Quantlib-users] Fast sensitivities - speed test

[Jorg L. <jo...@xc...>]

  Hello all,

As of QuantLib 1.28 (October 2022), Automatic Differentiation (AD) can be
enabled in QuantLib using the open-source XAD AD tool and an XAD/QuantLib
integration module. QuantLib's AD-compatibility is actively maintained via
automated CI/CD checks, running daily against QuantLib's master branch.

Below are performance results for a set of examples using QuantLib, showing
the cost of AD compared to the matching plain pricing valuation (no AD) for
an arbitrary number of sensitivities. Those results are reproducible and
the code used is publicly available.

The results show the time taken by the AD enabled version of QuantLib vs.
the time taken for a plain valuation is between a factor of 1.4 and 2.8.
This means one can get arbitrary numbers of sensitivities in less than 2.8x
the time taken by a plain valuation. Details of the benchmark configuration
are included at the end of this email.

Equity Option Portfolio (98 sensitivities)
- Valuation run: 2.83 ms
- AAD run: 7.00 ms (2.47 X)

Barrier Option Replication (13 sensitivities)
- Valuation run: 1.48 ms
- AAD run: 4.16 ms (2.81 X)

Swap Portfolio (55 sensitivities)
- Valuation run: 26.05 ms
- AAD run: 36.28 ms (1.39 X)

Multicurve Bootstrapping (65 sensitivities)
- Valuation run: 192.11 ms
- AAD run: 299.63 ms (1.56 X)

More details here: https://auto-differentiation.github.io/quantlib/

Cheers,
Jorg

Benchmark configuration:
- QuantLib version: 1.30
- XAD version: 1.2.0
- OS: Windows Server 2022 Datacenter
- Compiler: Visual Studio 2022, 17.6.1
- RAM: 64GB
- CPU: Intel(R) Xeon(R) Platinum 8375C CPU @ 2.90GHz