las3_pub / predictable_parallel_patterns

This allows the stack and deque class to use the same offset, making it work better with each other.

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.

Threads must be joined on scheduler termination and tasks must be pushed onto the stack to allow better memory management.

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

Please note that this does currently not execute, but only removed the 'old aspecs'. We are still missing launching the root fork_join task.

The __thread implementation of thread local, static variables seems more efficient, so we activate this by default. If it is (for some reason) not available/slower than the pthread version, one can toggle it for the specific system/processor later on using macros.

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.