Performance Trees: Implementation And Distributed Evaluation

In this paper, we describe the first realisation of an evaluation environment for Performance Trees, a recently proposed formalism for the specification of performance properties and measures. In particular, we present details of the architecture and implementation of this environment that comprises a client-side model and performance query specification tool, and a server-side distributed evaluation engine, supported by a dedicated computing cluster. The evaluation engine combines the analytic capabilities of a number of distributed tools for steady-state, passage time and transient analysis, and also incorporates a caching mechanism to avoid redundant calculations. We demonstrate in the context of a case study how this analysis pipeline allows remote users to design their models and performance queries in a sophisticated yet easy to use framework, and subsequently evaluate them by harnessing the computing power of a Grid cluster back-end.Accepted versio

Mathemagix User Guide

101 pagesThis manual describes the Mathemagix programming language (http://www.mathemagix.org)

On the Factor Refinement Principle and its Implementation on Multicore Architectures

The factor refinement principle turns a partial factorization of integers (or polynomi­ als) into a more complete factorization represented by basis elements and exponents, with basis elements that are pairwise coprime. There are lots of applications of this refinement technique such as simplifying systems of polynomial inequations and, more generally, speeding up certain algebraic algorithms by eliminating redundant expressions that may occur during intermediate computations. Successive GCD computations and divisions are used to accomplish this task until all the basis elements are pairwise coprime. Moreover, square-free factorization (which is the first step of many factorization algorithms) is used to remove the repeated patterns from each input element. Differentiation, division and GCD calculation op­ erations are required to complete this pre-processing step. Both factor refinement and square-free factorization often rely on plain (quadratic) algorithms for multipli­ cation but can be substantially improved with asymptotically fast multiplication on sufficiently large input. In this work, we review the working principles and complexity estimates of the factor refinement, in case of plain arithmetic, as well as asymptotically fast arithmetic. Following this review process, we design, analyze and implement parallel adaptations of these factor refinement algorithms. We consider several algorithm optimization techniques such as data locality analysis, balancing subproblems, etc. to fully exploit modern multicore architectures. The Cilk++ implementation of our parallel algorithm based on the augment refinement principle of Bach, Driscoll and Shallit achieves linear speedup for input data of sufficiently large size

SFQEDtoolkit: a high-performance library for the accurate modeling of strong-field QED processes in PIC and Monte Carlo codes

Strong-field quantum electrodynamics (SFQED) processes are central in determining the dynamics of particles and plasmas in extreme electromagnetic fields such as those present in the vicinity of compact astrophysical objects or generated with ultraintense lasers. SFQEDtoolkit is an open source library designed to allow users for a straightforward implementation of SFQED processes in existing particle-in-cell (PIC) and Monte Carlo codes. Through advanced function approximation techniques, high-energy photon emission and electron-positron pair creation probability rates and energy distributions are calculated within the locally-constant-field approximation (LCFA) as well as with more advanced models [Phys. Rev. A 99, 022125 (2019)]. SFQEDtoolkit is designed to provide users with high-performance and high-accuracy, and neat examples showing its usage are provided. In the near future, SFQEDtoolkit will be enriched to model the angular distribution of the generated particles, i.e., beyond the commonly employed collinear emission approximation, as well as to model spin and polarization dependent SFQED processes. Notably, the generality and flexibility of the presented function approximation approach makes it suitable to be employed in other areas of physics, chemistry and computer science.Comment: Published open access in Computer Physics Communications under CC BY 4.0. SFQEDtoolkit can be used as a black box, in which case the user can directly refer to "Appendix A. User guide". The repository with the associated open-source code is available on github https://github.com/QuantumPlasma/SFQEDtoolki