las3_pub / predictable_parallel_patterns

Currently once we are serial no more parallel strains are executed. However, this could be changed using strain local resources in the future.

The strain_local resources can be used to get unique IDs for resources that would, in a serial execution, only be used once in the call chain. This is a first prototype implementation and needs further tesitng. The current implementation adds linear overhead to each steal proportional to the number of used  resources. We might be able to reduce it, but for a first version it is fine.

The flag can be used to run code annotated with PLS outside of a scheduler environment,
i.e. the app does not crash if the code is called without context. The compile option
allows to omit all spawn and sync calls during compilation, creating the equivalent serial code.

Add key-points required to capture an execution DAG. Currently all data is in-memory and printed out. In future work it would be good to store the DAG's and/or process them further.

Remove easy profiler for now. It might be useful for later on to get a grasp of general behaviour, but because of its issues with coroutines it is not worth profiling code with it right now.

I am not sure if this is appropriate, but it works and is no focus of this project.

It is now possible to use a memory mapped stack that throws a SIGSEV if thes coroutine stacks are exhausted.

Remove the strict static memory allocation scheme in favour of placing objects on the heap at startup. This still keeps the requirements posed for modern, high performance embedded systems, but makes APIs a lot cleaner.

We yield after num_thread failed steals in a row. This parameter can be tuned for better performance, but we stick to a sensible default just to prevent massive spinning.

We still see very sporadic crashes, however the current version is at least a starting point for refactoring and debugging. Next steps have to be to re-enable tooling support (i.e. add code to let sanitizers do their work).

The current version has race conditions and is hard to debug (especially because of the fibers, if a wrong thread executes on a fiber we get segfalts very fast). To combat this mess we now refactor the code bit by bit while also adding tests where it can be done with reasonably effort).

The project is currently really messy and there are sporadic sigsevs. This indicates that we still have a race in our code. Thread Sanitizer does not work with our current implementation, as it needs annotations for fibers.

The next step is to clean up the project and maybe add thread sanitizer support to our fiber implementation. This should help finding the remaining bugs.

The idea is to exclude as many sources as possible that could lead to issues with contention and cache misses. After some experimentation, we think that hyperthreading is simply not working very well with our kind of workload. In the future we might simply test on other hardware.

This version runs through our initial fft and fib tests. However, it is not tested further in any way. Additionally, we added a locking deque, potentially hurting performance and moving away from our initial goal.

The main issue seems to still be the fact that we have a lock free protocol where a steal can be pending. We plan to remove this fact next by introducing a protocol that works on a single atomic update.

The start_chain property does not make sense, as chains are purely 'virtual', i.e. they only fully exist when walking through the computation (by patching them on important events). We initially added the property as a helper for better runtime and simpler implementation, but we think without it we will not get as much inconsistency in the runtime state. Performance can be 're-added' later on.

It is still not working, however we now have no more redundant code, making debugging it simpler.

Everything so far is untested. We only made sure tha fast path still seems to function correctly. Next up is writing tests for both the fast and slow path to then introduce the slow path. After that we can look at performance optimizations.