The advantages of payoff scripting based on a build-in interpreter or “on-the-fly compiler” instead of implementing the payoffs in C++ are obvious. Faster time-to-market because there is no need to recompile and deploy the C++ pricing library and people without deep C++ knowledge are able to develop and test new structured products. One disadvantage is often the execution speed of the chosen scripting language. Examples of languages I have seen/used for payoff scripting are Python (C++ interface boost::python), Lua, tinycc and CINT. When it comes to execution speed none of these are suited to build high performance solutions, see. e.g. [1]. This is especially true if the Monte-Carlo scenario generator is running on a GPU. The payoff scripting on the CPU can then easily become the bottleneck of your pricing library.
Scala is a modern programming language that integrates object-oriented and functional language features. The Scala compiler generates byte code for the Java VM. Therefore the execution speed of a Scala script is comparable with Java and roughly a factor of two slower than C++ [1].
The Scala compiler itself is a Scala object and can be used at runtime to compile and link new scripts or classes. In addition using JNI it is fairly easy to attach a Java VM to a C++ process and to exchange data between C++ and Scala. Also Scala offers a lot of features to design an “internal”, user-friendly domain specific language (DSL) for payoff scripting.
The code for a small QuantLib/Scala Monte-Carlo simulation in action is available here. It depends on QuantLib 1.0 or higher, a Java 1.6 VM and Scala 2.8/9. Overwrite the PayoffImpl.scala class to implement different payoffs without recompiling the C++ code.
HPC-QuantLib 