docs: Added roadmap/todo

ROADMAP.md

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+# Roadmap
+
+A kind of roadmap that gives a rough idea about how the project will be continued and which features will be implemented, in the sense of a todo list.
+
+- [x] Being able to perform _some_ translations [PR#3](https://github.com/GridTools/jace/pull/3).
+- [ ] Basic functionalities:
+  - [ ] Annotation `@jace.jit`.
+  - [ ] Composable with Jax, i.e. take the Jax derivative of a Jace annotated function.
+  - [ ] Implementing the `stages` model that is supported by Jax.
+  - [ ] Handling Jax arrays as native input (only on single host).
+  - [ ] Cache the compilation and lowering results for later reuse.
+        In Jax these parts (together with the dispatch) are actually written in C++, thus in the beginning we will use a self made cache.
+- [ ] Implementing some basic `PrimitiveTranslators`, that allows us to run some early tests, such as:
+  - [ ] Backporting the ones from the prototype.
+  - [ ] Implement the `scatter` primitive (needed for pyhpc).
+  - [ ] Implement the `scan` primitive (needed for pyhpc).
+- [ ] _Initial_ optimization pipeline
+      In order to do benchmarks, we need to perform optimizations first.
+      However, the one offered by DaCe were not that well, so we should, for now, backport the ones from the prototype.
+- [ ] Support GPU code (relatively simple, but needs some detection logic).
+- [ ] Initial benchmark:
+      In the beginning we will not have the same dispatching performance as Jax.
+      But passing these benchmarks could give us some better hint of how to proceed in this matter.
+  - [ ] Passing the [pyhpc-benchmark](https://github.com/dionhaefner/pyhpc-benchmarks)
+  - [ ] Passing Felix' fluid project; possibility.
+- [ ] Support of static arguments.
+- [ ] Stop relying on `jax.make_jaxpr()`.
+      Look at the `jax._src.pjit.make_jit()` function for how to hijack the staging process.
+- [ ] Implementing more advanced primitives:
+  - [ ] Handling pytrees as arguments.
+  - [ ] Implement random numbers.
+  - [ ] `jax.numpy`.
+  - [ ] `jax.scipy`.
+- [ ] Passing the single host Jax unit tests.
+- [ ] Multi-Device capabilities, i.e. multiple GPUs but all on the same host.
+  - [ ] Passing the associated Jax unit tests.
+- [ ] Multi-Host capabilities, i.e. MPI.
+  - [ ] Passing the associated Jax unit tests.
+
+## General
+
+These are more general topics that should be addressed at one point.
+
+- [ ] Integrating better with Jax
+  - [ ] Support its array type (probably implement this in DaCe).
+- [ ] Increase the dispatching speed + Cache
+      Jax does this in C++, which is impossible to beat in Python, thus we have to go that root as well.
+- [ ] Debugging information.
+- [ ] Dynamic shapes
+      This could be done by making the inputs fully dynamic, and then use the primitives to simplify.
+      For example in an addition the shape of the two inputs and the outputs are the same.
+      That is knowledge that is inherent to the primitives itself.
+      However, the compiled object must know how to extract the sizes itself.
+- [ ] Defining a Logo:
+      It should be green with a nice curly font.
+
+# Optimization & Transformations
+
+The SDFG generated by Jace have a very particular structure, thus we could and probably should write some highly targeted optimization passes for them.
+Our experiments with the prototype showed that the most important transformation is Map fusion and the one in DaCe is essentially broken.
+
+- [ ] Modified state fusion; Because of the structure we have, this could make `Simplify` much more efficient.
+- [ ] Trivial Tasklet removal.
+      Since we will work a lot with Maps that are trivial (probably the best structure for fusing) we will end up with some of trivial Tasklets, i.e. `__out = __in`.
+      Thus, we should have a good way to get rid of them.
+- [ ] Modified Map fusion transformation.
+      We should still support parallel and serial fusion as the prototype did, but focusing on serial.