Highly Scalable Multiplication for Distributed Sparse Multivariate Polynomials on Many-core Systems

We present a highly scalable algorithm for multiplying sparse multivariate polynomials represented in a distributed format. This algo- rithm targets not only the shared memory multicore computers, but also computers clusters or specialized hardware attached to a host computer, such as graphics processing units or many-core coprocessors. The scal- ability on the large number of cores is ensured by the lacks of synchro- nizations, locks and false-sharing during the main parallel step.Comment: 15 pages, 5 figure

Variable kinematic beam elements coupled via Arlequin method

In this work, beamelements based on different kinematic assumptions are combined through the Arlequinmethod. Computational costs are reduced assuming refined models only in those zones with a quasi-three-dimensional stress field. Variable kinematics beamelements are formulated on the basis of a unified formulation (UF). This formulation is extended to the Arlequinmethod to derive matrices related to the coupling zones between high- and low-order kinematicbeam theories. According to UF, a N-order polynomials approximation is assumed on the beam cross-section for the unknown displacements, being N a free parameter of the formulation. Several hierarchical finite elements can be formulated. Part of the structure can be accurately modelled with computationally cheap low-order elements, part calls for computationally demanding high-order elements. Slender, moderately deep and deep beams are investigated. Square and I-shaped cross-sections are accounted for. A cross-ply laminated composite beam is considered as well. Results are assessed towards Navier-type analytical models and three-dimensional finite element solutions. The numerical investigation has shown that Arlequinmethod in the context of a hierarchical formulation effectively couples sub-domains having different order finite elements without loss of accuracy and reducing the computational cos

The Astropy Project: Building an Open-science Project and Status of the v2.0 Core Package

The Astropy Project supports and fosters the development of open-source and openly developed Python packages that provide commonly needed functionality to the astronomical community. A key element of the Astropy Project is the core package astropy, which serves as the foundation for more specialized projects and packages. In this article, we provide an overview of the organization of the Astropy project and summarize key features in the core package, as of the recent major release, version 2.0. We then describe the project infrastructure designed to facilitate and support development for a broader ecosystem of interoperable packages. We conclude with a future outlook of planned new features and directions for the broader Astropy Project

The Astropy Project: Building an inclusive, open-science project and status of the v2.0 core package

The Astropy project supports and fosters the development of open-source and openly-developed Python packages that provide commonly-needed functionality to the astronomical community. A key element of the Astropy project is the core package Astropy, which serves as the foundation for more specialized projects and packages. In this article, we provide an overview of the organization of the Astropy project and summarize key features in the core package as of the recent major release, version 2.0. We then describe the project infrastructure designed to facilitate and support development for a broader ecosystem of inter-operable packages. We conclude with a future outlook of planned new features and directions for the broader Astropy project

Esa/Pagmo2: Pagmo 2.9

This release features two new main features: the python decorator_problem problem, which allows to customise at runtime the behaviour of an existing problem (e.g., for adding logging), and the fork_island island, which enables in C++ the parallelisation of thread- unsafe problems and/or algorithms (such as IPOPT). There is, as usual, an assortment of smaller new features, bug fixes, and documentation and build system improvements. The full changelog is available, as usual, here: https://esa.github.io/pagmo2/changelog.html

Analytical method for perturbed frozen orbit around an Asteroid in highly inhomogeneous gravitational fields

This article provides a method for finding initial conditions for perturbed frozen orbits around inhomogeneous fast rotating asteroids. These orbits can be used as reference trajectories in missions that require close inspection of any rigid body. The generalized perturbative procedure followed exploits the analytical methods of relegation of the argument of node and Delaunay normalisation to arbitrary order. These analytical methods are extremely powerful but highly computational. The gravitational potential of the inhomogeous body is firstly stated, in polar-nodal coordinates, which takes into account the coefficients of the spherical harmonics up to an arbitrary order. Through the relegation of the argument of node and the Delaunay normalization, a series of canonical transformations of coordinates is found, which reduces the Hamiltonian describing the system to a integrable, two degrees of freedom Hamiltonian plus a truncated reminder of higher order. Setting eccentricity, argument of pericenter and inclination of the orbit of the truncated system to be constant, initial conditions are found, which evolve into frozen orbits for the truncated system. Using the same initial conditions yields perturbed frozen orbits for the full system, whose perturbation decreases with the consideration of arbitrary homologic equations in the relegation and normalization procedures. Such procedure can be automated for the first homologic equation up to the consideration of any arbitrary number of spherical harmonics coeffcients. The project has been developed in collaboration with the European Space Agency (ESA)

Esa/Pagmo2: Pagmo 2.7

This release adds 3 new algorithms: particle swarm optimization generational (GPSO), exponential natural evolution strategies (xNES), improved harmony search (IHS). The full changelog is available, as usual, here: https://esa.github.io/pagmo2/changelog.htm