This article is based on the application of supercomputing techniques through an example in the investigation of the astronomical phenomena of evolution of galactic nuclei and black holes within the time period of the Universe’s existence. Numerical models of stellar dynamics simulations on supercomputers are essential in this area of research. This work was done in various research centers around the world, and the software employed uses N-body techniques in C ++, CUDA (Compute Unifi ed Device Architecture) and parallelized by using a MPI (Message Passing Interface). The scalability of the problem allows one to access accurate numerical models regarding the evolution of the systems of millions of stars. The results show that black holes, in all its variations, defi ne the shape and dynamics of galaxies, at times comparable to the age of the Universe, and have, for that reason, consequ...

This article is based on the application of supercomputing techniques through an example in the investigation of the astronomical phenomena of evolution of galactic nuclei and black holes within the time period of the Universe’s existence. Numerical models of stellar dynamics simulations on supercomputers are essential in this area of research. This work was done in various research centers around the world, and the software employed uses N-body techniques in C ++, CUDA (Compute Unifi ed Device Architecture) and parallelized by using a MPI (Message Passing Interface). The scalability of the problem allows one to access accurate numerical models regarding the evolution of the systems of millions of stars. The results show that black holes, in all its variations, defi ne the shape and dynamics of galaxies, at times comparable to the age of the Universe, and have, for that reason, consequ...

This work proposes a semi-automated analysis and modeling package for Machine Learning related problems. The library goal is to reduce the steps involved in a traditional data science roadmap. To do so, Sparkmach takes advantage of Machine Learning techniques to build base models for both classification and regression problems. These models include exploratory data analysis, data preprocessing, feature engineering and modeling. The project has its basis in Pymach, a similar library that faces those steps for small and medium-sized datasets (about ten millions of rows and a few columns). Sparkmach central labor is to scale Pymach to overcome big datasets by using Apache Spark distributed computing, a distributed engine for large-scale data processing, that tackle several data science related problems in a cluster environment. Despite the software nature, Sparkmach can be of use for local ...