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Abstract-Recent years have seen a growing trend towards the introduction of more advanced manycore processors. On the other hand, there is also a growing popularity for cheap, creditcard-sized, devices offering more and more advanced features and computational power.In this paper we evaluate Parallella -a small board with the Epiphany manycore coprocessor consisting of sixteen MIMD cores connected by a mesh network-on-a-chip. Our tests are based on classical genetic algorithms. We discuss some possible optimizations and issues that arise from the architecture of the board. Although we achieve significant speed improvements, there are issues, such us the limited local memory size and slow memory access, that make the implementation of efficient code for Parallella difficult.

Summary This paper introduces a new formal description of the execution model for agent‐based computing systems in the form of an adaptive dataflow decoupled from the domain‐specific semantics of the computation. We show that the execution models studied in previous work can be unified in this common model. The parameters of the model such as queuing policies and granularity of the data in the flow are analyzed. Several queueing alternatives are benchmarked to demonstrate how they affect the efficiency of the computation. Using the example of a multi‐agent evolutionary optimisation problem solver, the new approach is shown to outperform the classic one. This proposed model is well suited to functional languages and can be easily mapped onto different classes of hardware – from simple single‐core computers to distributed environments.