Heterogeneous Computational Model for Landform Attributes Representation on Multicore and Multi-GPU Systems

AbstractMathematical models are often used to simplify landform representation. Its importance is due to the possibility of describing phenomena by means of mathematical models from a data sample. High processing power is needed to represent large areas with a satisfactory level of details. In order to accelerate the solution of complex problems, it is necessary to combine two basic components in heterogeneous systems formed by a multicore with one or more GPUs. In this paper, we present a methodology to represent landform attributes on heterogeneous multicore and multi-GPU systems using high performance computing techniques for efficient solution of two-dimensional polynomial regression model that allow to address large problem instances

Automatic routine tuning to represent landform attributes on multicore and multi-GPU systems

The final publication is available at Springer via http://dx.doi.org/10.1007/s11227-014-1191-0Auto-tuning techniques have been used in the design of routines in recent years. The goal is to develop routines which automatically adapt to the conditions of the computational system in such a way that efficient executions are obtained indepen- dently of the end-user experience. This paper aims to explore programming routines that can be automatically adapted to the computational system conditions, making possible to use auto-tuning to represent landform attributes on multicores and multi- GPU systems using high- performance computing techniques for efficient solution of two-dimensional polynomial regression models that allow large problem instances to be addressed.This work has been partially supported by European Union ERDF and Spanish Government through TEC2012-38142-C04 project.Do Carmo Boratto, M.; Alonso-Jordá, P.; Giménez Cánovas, D.; Barreto, M. (2014). Automatic routine tuning to represent landform attributes on multicore and multi-GPU systems. Journal of Supercomputing. 70(2):733-745. https://doi.org/10.1007/s11227-014-1191-0S733745702Alberti P, Alonso P, Vidal A, Cuenca J, Giménez D (2004) Designing polylibraries to speed up parallel computations. Int J High Perform Comput Appl 1(1/2/3):75–84Frigo M, Johnson S (1998) FFTW: an adaptive software architecture for the FFT. Proc IEEE Int Conf Acoust Speech Signal Process 3:1381–1384Garland M (2010) Parallel computing with CUDA. In: IPDPS, pp 10–26Jerez S, Montávez JP, Giménez D (2009) Optimizing the execution of a parallel meteorology simulation code. In: IPDPS. IEEE Computer Society, Los Alamitos, CA, USANogueira L, Abrantes RP, Leal B (2008) A methodology of distributed processing using a mathematical model for landform attributes representation. In: Proceeding of the IADIS International Conference on applied computing, pp 17–21Nogueira L, Abrantes RP, Leal B, Goulart C (2008) A model of landform attributes representation for application in distributed systems. In: Proceeding of the IADIS International Conference on applied computingRawlings JO, Pantula SG, Dickey DA (1998) Applied regression analysis: a research tool. Springer, LondonRufino I, Galvao C, Rego J, Albuquerque J (2009) Water resources and urban planning: the case of a coastal area in Brazil. J Urban Environ Eng 3:32–42Song F, Tomov S, Dongarra J (2011) Efficient support for matrix computations on heterogeneous multicore and multi-GPU architectures. Tech Rep 250, LAPACK working noteWhaley C, Petitet A, Dongarra JJ (2000) Automated empirical optimization of software and the ATLAS project. Parallel Comput 27:21–3

Across Space and Time. Papers from the 41st Conference on Computer Applications and Quantitative Methods in Archaeology, Perth, 25-28 March 2013

This volume presents a selection of the best papers presented at the forty-first annual Conference on Computer Applications and Quantitative Methods in Archaeology. The theme for the conference was "Across Space and Time", and the papers explore a multitude of topics related to that concept, including databases, the semantic Web, geographical information systems, data collection and management, and more

Across Space and Time Papers from the 41st Conference on Computer Applications and Quantitative Methods in Archaeology, Perth, 25-28 March 2013

The present volume includes 50 selected peer-reviewed papers presented at the 41st Computer Applications and Quantitative Methods in Archaeology Across Space and Time (CAA2013) conference held in Perth (Western Australia) in March 2013 at the University Club of Western Australia and hosted by the recently established CAA Australia National Chapter. It also hosts a paper presented at the 40th Computer Applications and Quantitative Methods in Archaeology (CAA2012) conference held in Southampton

Generalized averaged Gaussian quadrature and applications

A simple numerical method for constructing the optimal generalized averaged Gaussian quadrature formulas will be presented. These formulas exist in many cases in which real positive GaussKronrod formulas do not exist, and can be used as an adequate alternative in order to estimate the error of a Gaussian rule. We also investigate the conditions under which the optimal averaged Gaussian quadrature formulas and their truncated variants are internal

MS FT-2-2 7 Orthogonal polynomials and quadrature: Theory, computation, and applications

Quadrature rules find many applications in science and engineering. Their analysis is a classical area of applied mathematics and continues to attract considerable attention. This seminar brings together speakers with expertise in a large variety of quadrature rules. It is the aim of the seminar to provide an overview of recent developments in the analysis of quadrature rules. The computation of error estimates and novel applications also are described