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The N-body simulations have become a powerful tool to test the gravitational interaction among particles, ranging from a few bodies to complete galaxies. Even though N-body has already been optimized on many parallel platforms, there are hardly any studies which take advantage of the latest Intel architectures based on AVX-512 instruction set. This SIMD set was initially supported by Intel's Xeon Phi Knights Landing (KNL) manycore processors launched at 2016. Recently, it has been included in Intel's general-purpose processors too, starting at the Skylake (SKL) server microarchitecture and now in its successor Cascade Lake (CKL). This paper optimizes the all-pairs N-body simulation on both current Intel platforms supporting AVX-512 extensions: a Xeon Phi KNL node and a server equipped with a dual CKL processor. On the basis of a naive implementation, it is shown how the parallel implementation (can) reach, through different optimization techniques, 2355 and 2449 GFLOPS on the Xeon Phi KNL and the Xeon CKL platforms, respectively.

In this work, we present an intelligent system developed for energy consumption distributed control and monitoring. It supports real time cloudbased data visualization of power profiles from different areas, so as to optimize overall power consumption. The local intelligent processing unit (LIPU) that control the different environments is described. The communication network model that allows connecting multiple LIPUs to apply power consumption policies defined by the organization is analyzed, and the unit's capabilities in relation to cloud connectivity and realtime processing are considered through a theoretical scalability study. Finally, we describe relevant implementation features in the context of "Facultad

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