Evaluating Zoltan for Static Load Balancing on BlueGene Architectures

The purpose of this TechBase was to evaluate the Zoltan load-balancing library from Sandia National Laboratories as a possible replacement for ParMetis, which had been the load balancer of choice for nearly a decade but does not scale to the full 64,000 processors of BlueGene/L. This evaluation was successful in producing a clear result, but the result was unfortunately negative. Although Zoltan presents a collection load-balancing algorithms, none were able to meet or exceed the combined scalability and quality of ParMetis on representative datasets

How to Implement a Protocol for Babel RMI

RMI support in Babel has two main goals: transparency & flexibility. Transparency meaning that the new RMI features are entirely transparent to existing Babelized code; flexibility meaning the RMI capability should also be flexible enough to support a variety of RMI transport implementations. Babel RMI is a big success in both areas. Babel RMI is completely transparent to already Babelized implementation code, allowing painless upgrade, and only very minor setup changes are required in client code to take advantage of RMI. The Babel RMI transport mechanism is also extremely flexible. Any protocol that implements Babel's minimal, but complete, interface may be used as a Babel RMI protocol. The Babel RMI API allows users to select the best protocol and connection model for their application, whether that means a WebServices-like client-server model for use over a WAP, or a faster binary peer-to-peer protocol for use on different nodes in a leadership-class supercomputer. Users can even change protocols without recompiling their code. The goal of this paper is to give network researchers and protocol implementors the information they need to develop new protocols for Babel RMI. This paper will cover both the high-level interfaces in the Babel RMI API, and the low level details about how Babel RMI handles RMI objects

Scientific Software Component Technology

We are developing new software component technology for high-performance parallel scientific computing to address issues of complexity, re-use, and interoperability for laboratory software. Component technology enables cross-project code re-use, reduces software development costs, and provides additional simulation capabilities for massively parallel laboratory application codes. The success of our approach will be measured by its impact on DOE mathematical and scientific software efforts. Thus, we are collaborating closely with library developers and application scientists in the Common Component Architecture forum, the Equation Solver Interface forum, and other DOE mathematical software groups to gather requirements, write and adopt a variety of design specifications, and develop demonstration projects to validate our approach. Numerical simulation is essential to the science mission at the laboratory. However, it is becoming increasingly difficult to manage the complexity of modern simulation software. Computational scientists develop complex, three-dimensional, massively parallel, full-physics simulations that require the integration of diverse software packages written by outside development teams. Currently, the integration of a new software package, such as a new linear solver library, can require several months of effort. Current industry component technologies such as CORBA, JavaBeans, and COM have all been used successfully in the business domain to reduce software development costs and increase software quality. However, these existing industry component infrastructures will not scale to support massively parallel applications in science and engineering. In particular, they do not address issues related to high-performance parallel computing on ASCI-class machines, such as fast in-process connections between components, language interoperability for scientific languages such as Fortran, parallel data redistribution between components, and massively parallel components. While industrial component systems do not directly address scientific computing issues, we leverage existing industry technologies and design concepts whenever possible

How the Common Component Architecture Advances Compuational Science

Computational chemists are using Common Component Architecture (CCA) technology to increase the parallel scalability of their application ten-fold. Combustion researchers are publishing science faster because the CCA manages software complexity for them. Both the solver and meshing communities in SciDAC are converging on community interface standards as a direct response to the novel level of interoperability that CCA presents. Yet, there is much more to do before component technology becomes mainstream computational science. This paper highlights the impact that the CCA has made on scientific applications, conveys some lessons learned from five years of the SciDAC program, and previews where applications could go with the additional capabilities that the CCA has planned for SciDAC 2

A Component Architecture for High-Performance Scientific Computing

The Common Component Architecture (CCA) provides a means for software developers to manage the complexity of large-scale scientific simulations and to move toward a plug-and-play environment for high-performance computing. In the scientific computing context, component models also promote collaboration using independently developed software, thereby allowing particular individuals or groups to focus on the aspects of greatest interest to them. The CCA supports parallel and distributed computing as well as local high-performance connections between components in a language-independent manner. The design places minimal requirements on components and thus facilitates the integration of existing code into the CCA environment. The CCA model imposes minimal overhead to minimize the impact on application performance. The focus on high performance distinguishes the CCA from most other component models. The CCA is being applied within an increasing range of disciplines, including combustion research, global climate simulation, and computational chemistry

On some properties of the Laplacian matrix revealed by the RCM algorithm

In this paper we present some theoretical results about the irreducibility of the Laplacian matrix ordered by the Reverse Cuthill-McKee (RCM) algorithm. We consider undirected graphs with no loops consisting of some connected components. RCM is a well-known scheme for numbering the nodes of a network in such a way that the corresponding adjacency matrix has a narrow bandwidth. Inspired by some properties of the eigenvectors of a Laplacian matrix, we derive some properties based on row sums of a Laplacian matrix that was reordered by the RCM algorithm. One of the theoretical results serves as a basis for writing an easy MATLAB code to detect connected components, by using the function “symrcm” of MATLAB. Some examples illustrate the theoretical results.The research has been supported by Spanish DGI grant MTM2010-18674, Consolider Ingenio CSD2007-00022, PROMETEO 2008/051, OVAMAH TIN2009-13839-C03-01, and PAID-06-11-2084.Pedroche Sánchez, F.; Rebollo Pedruelo, M.; Carrascosa Casamayor, C.; Palomares Chust, A. (2016). On some properties of the Laplacian matrix revealed by the RCM algorithm. Czechoslovak Mathematical Journal. 66(3):603-620. doi:10.1007/s10587-016-0281-yS60362066

The Computational Complexity of the Minimum Degree Algorithm

The Minimum Degree algorithm, one of the classical algorithms of sparse matrix computations, is widely used to order graphs to reduce the work and storage needed to solve sparse systems of linear equations. There has been extensive research involving practical implementations of this algorithm over the past two decades. However, little has been done to establish theoretical bounds on the computational complexity of these implementations. We study the Minimum Degree algorithm, and prove time complexity bounds for its widely used variants

Fiscal Year 2007

Abstract not provide