diff --git a/GSoC-2015-Ideas.md b/GSoC-2015-Ideas.md
index 92b6f28..72d2ce4 100644
--- a/GSoC-2015-Ideas.md
+++ b/GSoC-2015-Ideas.md
@@ -138,6 +138,56 @@ with learning about Nim compiler internals.
 
 **Mentor:** Araq ([@Araq](https://github.com/Araq)), def- ([@def-](http://github.com/def-))
 
+
+
+#### A strong theoretical foundation for Nim's parallel statement
+
+**Description:**
+
+Nim uses a fresh idea to model fork&join parallelism via its ``parallel``
+builtin that performs a range of additional checks at compile-time
+like array bounds checking or the so called "disjoint check". The disjoint
+check ensures the datasets are disjoint; that means every processed element is
+only accessed by one worker. This way no locking is required and yet (memory)
+safety is guaranteed.
+
+Unfortunately, how exactly this checking is performed is not
+yet documented: It's done in a pretty adhoc fashion that mostly uses
+the transitivity of inequality relations and some simple control flow analysis.
+
+However, in the context of *optimization* and code generation there has been
+lots of progress in commercial comilers in the form of auto vectorization.
+One such framework that enables auto vectorization is the polyhedral model.
+The polyhedral model deals with iteration spaces and serves to parallelize loop
+programs automatically by applying algebraic transformations. As it is a
+traditional optimization pass in a compiler it has to prove these
+transformations are correct. This is remarkably similar to the task of proving
+the code written down by a human being correct. So the idea of this task is to
+leverage the polyhedral model for program verification as opposed to program
+optimization.
+
+See these papers for an introduction of the polyhedral model:
+
+http://www.cs.ucla.edu/~pouchet/doc/cc-article.10.pdf
+
+http://icps.u-strasbg.fr/~bastoul/research/papers/Bas04-PACT.pdf
+
+
+Here is a sketch of a possible implementation:
+
+* Implement an AST to polyhedral model transformation.
+* Implement a semantic checking pass that works on the polyhedral model and
+  proves array bounds correct as well as that the datasets are disjoint.
+* In addition to that it needs to be proven that the code is free
+  of "write after write" or "write after read" etc. conflicts.
+
+
+**Desirable skills**: Knowledge of optimizers.
+
+**Difficulty:** Hard
+
+**Mentor:** Araq ([@Araq](https://github.com/Araq))
+
 ## Standard Library
 
 #### Enhance and expand standard library documentation