C++ Backend Part3

Calling into C++ from another language normally requires an FFI and writing extra broiler plate code and having to worry about garbage collection and threading issues. Just take a look at how fucked up interfacing D is with C++. Don't miss the bottom of that doc on C++ Templates, from the doc: "D templates have little in common with C++ templates, and it is very unlikely that any sort of reasonable method could be found to express C++ templates in a link-compatible way with D. This means that the C++ STL, and C++ Boost, likely will never be accessible from D." There are many other system languages, like Rust, that want to kill off C++, but do not have a good way of interfacing with C++.

C++ is deeply entrenched, its not going anywhere. Rusthon transpiles to human readable C++, and you can configure the transpiler to use regular or smart pointers. Interfacing with an external C++ library is simple and requires no FFI, you can use C++ namespaces, template classes (via macros) and directly handle memory, passing the address of some variable or returning a pointer.

Caffe Deep Learning

Caffe is a large deep learning framework with many different types of neural networks. Getting it to compile and working on Fedora took me a couple of days, along the way I had to make lots of notes on the build and defines it required. The notes, build script, and code is all put into a single markdown file, see hello_caffe.md.

Testing Caffe with Rusthon has given me the chance to iron out the last details for working with external C++. Some functions in Caffe need to be passed the address of a variable, not the value or a pointer to it, the new builtin addr(X) takes the address of X and passes it to the function. If you need to initialize an array of template classes you can now use this trick.

with MyType as "some_template<float>":
    x = new( MyType() )
    y = []MyType()
    y.append( x )