Efficient parallel execution of genetic algorithms on Epiphany manycore processor

Abstract

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.

A Heuristic Approach for Scheduling in Heterogeneous Distributed Embedded Systems

“…The well-known genetic algorithm is employed for efficient parallel execution on Epiphany manycore processors. The work in [10] shows an evaluate Parallella which is a small board with the Epiphany coprocessors consisting of sixteen MIMD cores connected by a mesh network-on-a-chip. The results are based on classical genetic algorithms.…”

Implementación de Algoritmos de Procesamiento Digital de Señales en Hardware Paralelo: Artículo de revisión

“…Implementación más rápida y más eficiente de la energía en el viejo DSP TI Keystone II de 28nm sobre el nuevo SoC NVIDIA TX1 de 20nm en todos los casos, sin embargo, Parallella fue la opción de menor consumo de energía. En (Faber and Boryczko, 2016) se evalúa Parallella, una pequeña tabla con el coprocesador multiprograma Epiphany que consta de dieciséis núcleos MIMD conectados por una red de malla en un chip. Las pruebas se basaron en algoritmos genéticos clásicos.…”

Genetic Algorithm (GA) for Multiprocessors Scheduling

“…Many previous studies have proven that using algorithm can significantly increase the completion time and efficiency of the processors running in parallel architecture [3], [4], [7], [8], [10], [11]. So, in this paper a Genetic Algorithm (GA) is designed and utilized to optimize the timespan of a Parallel Processor Job (PPJ) in hopes to further increase the overall performance and efficiency of the computer.…”