Enabling JXTA for High Performance Grid Computing

Grid computing has recently emerged as a response to the growing demand for resources (processing power, storage, etc.) exhibited by scientific applications. However, as grid sizes increase, the need for self-organization and dynamic reconfigurations is becoming more and more important. Since such properties are exhibited by P2P systems, the convergence of grid computing and P2P computing seems natural. However, using P2P systems (usually running on the Internet) on a grid infrastructure (generally available as a federation of SAN-based clusters interconnected by high-bandwidth WANs) may raise the issue of the adequacy of the P2P communication mechanisms. This paper evaluates the communication performance of the JXTA P2P library over SANs and WANs, for both J2SE and C bindings. We analyze these results and we evaluate solutions able to improve the performance of JXTA on such grid infrastructures

Language interoperability for high-performance parallel scientific components

With the increasing complexity and interdisciplinary nature of scientific applications, code reuse is becoming increasingly important in scientific computing. One method for facilitating code reuse is the use of components technologies, which have been used widely in industry. However, components have only recently worked their way into scientific computing. Language interoperability is an important underlying technology for these component architectures. In this paper, we present an approach to language interoperability for a high-performance parallel, component architecture being developed by the Common Component Architecture (CCA) group. Our approach is based on Interface Definition Language (IDL) techniques. We have developed a Scientific Interface Definition Language (SIDL), as well as bindings to C and Fortran. We have also developed a SIDL compiler and run-time library support for reference counting, reflection, object management, and exception handling (Babel). Results from using Babel to call a standard numerical solver library (written in C) from C and Fortran show that the cost of using Babel is minimal, where as the savings in development time and the benefits of object-oriented development support for C and Fortran far outweigh the costs

An adaptive offline implementation selector for heterogeneous parallel platforms

Heterogeneous Parallel Platforms, Comprising Multiple Processing Units And Architectures, Have Become A Cornerstone In Improving The Overall Performance And Energy Efficiency Of Scientific And Engineering Applications. Nevertheless, Taking Full Advantage Of Their Resources Comes Along With A Variety Of Difficulties: Developers Require Technical Expertise In Using Different Parallel Programming Frameworks And Previous Knowledge About The Algorithms Used Underneath By The Application. To Alleviate This Burden, We Present An Adaptive Offline Implementation Selector That Allows Users To Better Exploit Resources Provided By Heterogeneous Platforms. Specifically, This Framework Selects, At Compile Time, The Tuple Device-Implementation That Delivers The Best Performance On A Given Platform. The User Interface Of The Framework Leverages Two C&#43 &#43 Language Features: Attributes And Concepts. To Evaluate The Benefits Of This Framework, We Analyse The Global Performance And Convergence Of The Selector Using Two Different Use Cases. The Experimental Results Demonstrate That The Proposed Framework Allows Users Enhancing Performance While Minimizing Efforts To Tune Applications Targeted To Heterogeneous Platforms. Furthermore, We Also Demonstrate That Our Framework Delivers Comparable Performance Figures With Respect To Other Approaches.The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work has been partially supported by the Spanish ‘Ministerio de Economía y Competitividad’ under the project grant TIN2016-79637-P ‘Towards Unification of High Performance Computing (HPC) and Big Data Paradigms’ and the EU Projects ICT 644235 ‘RePhrase: REfactoring Parallel Heterogeneous Resource-Aware Applications’ and the FP7 609666 ‘Repara: Reengineering and Enabling Performance And poweR of Applications’

Modelling sea water intrusion in coastal aquifers using heterogeneous computing

The objective of this PhD research program is to investigate numerical methods for simulating variably-saturated flow and sea water intrusion in coastal aquifers in a high-performance computing environment. The work is divided into three overlapping tasks: to develop an accurate and stable finite volume discretisation and numerical solution strategy for the variably-saturated flow and salt transport equations; to implement the chosen approach in a high performance computing environment that may have multiple GPUs or CPU cores; and to verify and test the implementation. The geological description of aquifers is often complex, with porous materials possessing highly variable properties, that are best described using unstructured meshes. The finite volume method is a popular method for the solution of the conservation laws that describe sea water intrusion, and is well-suited to unstructured meshes. In this work we apply a control volume-finite element (CV-FE) method to an extension of a recently proposed formulation (Kees and Miller, 2002) for variably saturated groundwater flow. The CV-FE method evaluates fluxes at points where material properties and gradients in pressure and concentration are consistently defined, making it both suitable for heterogeneous media and mass conservative. Using the method of lines, the CV-FE discretisation gives a set of differential algebraic equations (DAEs) amenable to solution using higher-order implicit solvers. Heterogeneous computer systems that use a combination of computational hardware such as CPUs and GPUs, are attractive for scientific computing due to the potential advantages offered by GPUs for accelerating data-parallel operations. We present a C++ library that implements data-parallel methods on both CPU and GPUs. The finite volume discretisation is expressed in terms of these data-parallel operations, which gives an efficient implementation of the nonlinear residual function. This makes the implicit solution of the DAE system possible on the GPU, because the inexact Newton-Krylov method used by the implicit time stepping scheme can approximate the action of a matrix on a vector using residual evaluations. We also propose preconditioning strategies that are amenable to GPU implementation, so that all computationally-intensive aspects of the implicit time stepping scheme are implemented on the GPU. Results are presented that demonstrate the efficiency and accuracy of the proposed numeric methods and formulation. The formulation offers excellent conservation of mass, and higher-order temporal integration increases both numeric efficiency and accuracy of the solutions. Flux limiting produces accurate, oscillation-free solutions on coarse meshes, where much finer meshes are required to obtain solutions with equivalent accuracy using upstream weighting. The computational efficiency of the software is investigated using CPUs and GPUs on a high-performance workstation. The GPU version offers considerable speedup over the CPU version, with one GPU giving speedup factor of 3 over the eight-core CPU implementation

Resource provisioning in Science Clouds: Requirements and challenges

Cloud computing has permeated into the information technology industry in the last few years, and it is emerging nowadays in scientific environments. Science user communities are demanding a broad range of computing power to satisfy the needs of high-performance applications, such as local clusters, high-performance computing systems, and computing grids. Different workloads are needed from different computational models, and the cloud is already considered as a promising paradigm. The scheduling and allocation of resources is always a challenging matter in any form of computation and clouds are not an exception. Science applications have unique features that differentiate their workloads, hence, their requirements have to be taken into consideration to be fulfilled when building a Science Cloud. This paper will discuss what are the main scheduling and resource allocation challenges for any Infrastructure as a Service provider supporting scientific applications