In the FP7 Harness project we are using LARA to derive optimised programs targeting a heterogeneous cloud platform. In particular, cloud programmers can express different versions of the same C function using different semantics, such as based on dataflow computing. LARA aspects, which capture domain-specific knowledge about the cloud infrastructure, can then be used to automatically optimise the code for different types of resources. This way, programmers do not have to know details about the cloud infrastructure while being able leverage their resources.

In our current project, we have identified four types of aspects for dataflow engines: (1) system aspects capture transformations and optimisation strategies that affect the whole application, such as hardware/software partitioning and run-time reconfiguration; (2) architectural aspects focus on low-level design optimisations that can be applied to designs to improve timing, resource usage or exploit specialised architectural features; (3) exploration aspects deal with strategies that generate multiple designs to find an optimal implementation based on user-specified constraints; (4) development aspects relate to concerns that have an impact on the design process, such as debugging, kernel simulation and improving compilation speed.

Paul Grigoras, Aspect Oriented Design for Dataflow Engines, Master of Engineering in Computing of Imperial College London, June 2013. http://www.doc.ic.ac.uk/teaching/distinguished-projects/2013/p.grigoras.pdf