Le retrait progressif des États-Unis du Moyen-Orient a profité à l’Iran. Cette situation n’est pas acceptable pour l’Arabie Saoudite qui s’affirme comme une puissance régionale de plus en plus active. Pour freiner l’expansion iranienne, Riyad n’hésite pas à s’impliquer militairement, que ce soit en Syrie ou au Yémen. L’affrontement saoudo-iranien ne doit pas être vu comme un choc entre sunnites et chiites : la politique étrangère saoudienne ne suit pas des considérations religieuses.
The rise of artificial intelligence applications in recent years lead to an increasing demand for new, specialized hardware. Consequently, a European-wide research initiative has built the Spiking Neural Network Architecture (SpiNNaker) machine, a neuromorphic computer with a hardware design inspired by the brain. The biggest SpiNNaker machine features one million cores and is wired with very low latency inter-core links, which are optimised for transfers of very large numbers of small-sized data packets. This novel design allows for the efficient and scalable simulation of spiking neural networks. In this paper, we demonstrate that this design is also beneficial for other classes of applications, particularly for applications which require massive parallelism and the large-scale exchange of small messages. More specifically, we study the scalability of PageRank on SpiNNaker and compare it to an implementation on traditional hardware. In our experiments we demonstrate that PageRank on SpiNNaker speeds up execution up to a factor of 3×.
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