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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, credit-card-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.

Efficient parallel execution of genetic algorithms on Epiphany manycore processor