# Adjoint Greeks VI – Source Code Transformation Pt. II

I will be talking about Automatic differentiation beyond typedef and operator overloading at this year’s QuantLib User Conference. Registration is free, but seats are limited. The blog post today is sort of a teaser, so do not miss to be in Düsseldorf on November 30th / December 1st.

To recall what has been done to make QuantLib AD-ready: We have two approaches. One is by CompatibL which in essence changes the typedef for Real in the library from double to some active type of an operator overloading tool, like CppAD::AD. Of course this doesn’t work just by relinking the typedef, but in effect it describes well what is going on.

The other approach is to transform the library’s code and introduce template parameters whenever we might potentially be interested in calculating derivatives. You can read more about this by selecting the tag Automatic Differentiation in the sidebar that can be expanded by clicking on the three horizontal lines in the upper right corner when you scroll to the top of this page.

Backward compatibility in the template approach is achieved by typedef’ing the original class names to template instantiations with T = Real, so existing code compiles without any changes. Well let’s say 99% of it, some few modifications have to be applied, but really not many (from my experience with the original examples and the test-suite).

The transformation is not applied to the full library (yet), but the vanilla IRD stuff works, so coupons, yield term structures, volatility termstructures, the GSR model. You can get it (together with some working examples) from my adjoint branch at https://github.com/pcaspers/quantlib/tree/adjoint.

Both approaches have clear advantages and disadvantages. One potential problem with the typedef approach is, that this might kill your performance in parts of the code where you do not even want to use AD. For example,

    Matrix_t<T> A(1024, 1024);
Matrix_t<T> B(1024, 1024);
...
Matrix_t<T> C = A * B;


will take 764 ms on my machine with T=double, but 8960 ms with T=CppAD::AD. This seems to be due to compiler optimizations (like using SIMD instructions) that are no longer applicable when switching from native double to the custom AD type. This is a penalty of 11.7x and you do not get anything for that. Note that I do not record any tape or something, I just use the double functionality wrapped in the AD type.

Of course there might be other operator overloading tools that are more efficient. To get a lower bound for the penalty factor I replaced T by a minimal wrapper, just storing a double and an additional native pointer and forwarding the operators +,*,+= in question here to the respective double operations. With this wrapper the penalty is 2.0x. This is much better. The perfect operator overloading tool will probably lie somewhere between 2.0 and 11.7. But even 2.0 is not good, if you can avoid it, is it?

In any case the template approach allows for selective activation of variables where AD is needed and use native instances of the library classes elsewhere. You can also mix both. For example you could do a model calibration with the double-instance of a class, then extract the calibrated parameters and plug them into the AD – version of the same model, which then maybe generates Monte Carlo paths for some CVA calculations in which you want to calculate an adjoint gradient.

Still there are use cases left where even the template approach will not help and this is when you want to apply AD to code sections like the one above, where a lot of dense double calculations take place, enabling the compiler to apply optimizations that are not possible with custom types.

We saw an example in https://quantlib.wordpress.com/2015/04/14/adjoint-greeks-iv-exotics. The time equivalent of around 80 NPV calculations (holy cow!) is consumed for one adjoint gradient calculation of a Bermudan swaption in the Hull White model. From theory we would expect a complexity of 4x for the adjoint sweep, in practice we are probably happy with any factor up to 10x. But with 80 NPV calculations you can do a lot of things and even if your gradient vector has 100 – 200 entries, the application of AD would merely result in a speed up of around 2x compared to a naive bump and revalue approach. This would not really justify the whole AD-thing.

One possible way out of this is to write the adjoint code by hand.

Another approach is to use source code transformation. I started with a toy example in one of my last posts.

In the meantime I implemented a Bermudan swaption engine that produces an AD gradient w.r.t. the $H$ and $\zeta$ vectors describing an LGM model and the relevant discount factors of an interest rate curve. For this I extracted the computational core of the Gaussian1dSwaptionEngine (which relies on numerical integration) and rewrote it in Fortran. Then I applied OpenAD/F to the code to generate adjoint code and compiled everything into a library. You can have a look at the project here: https://github.com/pcaspers/quantlib/tree/master/QuantLibOAD/lgm.

On the QuantLib side I wrote a pricing engine acting as a wrapper for the Fortran core: It can be used just as any other pricing engine, but provides the adjoint gradient vector as an additional result. The gradient is computed w.r.t. the model parameters, but it is easy to tranform this into the usual market variables’ sensitivities. This is done exactly the same way as in the post I mentioned earlier.

Testing the engine on a 10y Bermudan swaption with yearly calls (using 49 grid points per expiry for the numerical integration) yields the following results (you can find the example code here)

• single pricing on a Intel(R) Core(TM) i7-2760QM CPU @ 2.40GHz, single threaded, non-transformed code: 4.2 ms
• pricing + gradient (which is 105 dimensional in this example): 25.6 ms
• transformation of gradient w.r.t. model parameters: 6.2 ms