las3_pub / predictable_parallel_patterns

This makes the programming model a full dataflow implementation, as it allows for branching and recursion.

Recursion works by using a function node, calling the graph again. We separated an graph invocation form an function invocation within an graph, making the graph only handle one concern.

We separated the structure (input-output flow) from the rest of the architecture and reworked some template programming to have better access to the types required at compile time.

The data can now flow into a graph, follow its path on inptus/outputs and be fetched from the graph after execution.
Currently graphs are executed synchronous.

We need some tricks in template programming to have a clean user facing API while internally using our classes with more capabilities.

This is the base building block for creating the actual dataflow functions and networks, as their main functionality is having data flow through connected nodes.

Now all algorithms are used without the parallel pre-/suffix and the for_each method has an specialization for integer ranges.

We do this to properly separate the cache alginment logic in the next step, allowing us to port all cache aligned objects without worrying about portability.

There are serval ways we could optimize the calls, but for now this should be enough for first tests.

Seperate library from other parts of project (samples, playground, testing, ...). We still are in one 'toplevel' cmake project, so we can for exmaple compile everything (library and playground) in debug mode, for simple development.