We're working on a specialised graph compiler for speeding up simulations and computing derivatives automatically (backpropagation/adjoint differentiation). It now supports C++, Python, and C#; AVX2 and AVX512 instruction sets, multithreading; Windows, and Linux.
Essentially, it allows model developers (such as quants in finance, engineers, and ML specialists) to code in Python without needing to think about the performance of repetitive calculations. As we all know, Python is one of the least efficient languages when it comes to complex calculations/simulations - and we help to resolve it. Long story short, with very few tweaks to the code certain types of calculations (such as pricing of derivatives, curve building, computing financial risks, or "small" NNs) can be accelerated by 100+ in Python and x20+ in C++/C#.
We're now looking to add support for Java (but it doesn't have Operator Overloading, so it's tricky), and some customers are asking to support GPU - which is a bit tricky because it's got a closed instruction set.
Essentially, it allows model developers (such as quants in finance, engineers, and ML specialists) to code in Python without needing to think about the performance of repetitive calculations. As we all know, Python is one of the least efficient languages when it comes to complex calculations/simulations - and we help to resolve it. Long story short, with very few tweaks to the code certain types of calculations (such as pricing of derivatives, curve building, computing financial risks, or "small" NNs) can be accelerated by 100+ in Python and x20+ in C++/C#.
We're now looking to add support for Java (but it doesn't have Operator Overloading, so it's tricky), and some customers are asking to support GPU - which is a bit tricky because it's got a closed instruction set.