Gary Kumfert

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 coputing. In the scientific computing context, component models also promote collaboration using independently developed software, thereby allowing particular individals or groups to focus on the aspects of greatest interest to them. The CCA supports parallel and distributed coputing 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 ovehead 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 cobustion research, global climate simulation, and computtional chemistry.

Two Improved Algorithms for Envelope and Wavefront Reduction

Two algorithms for reordering sparse, symmetric matrices or undirected graphs to reduce envelope and wavefront are considered. The first is a combinatorial algorithm introduced by Sloan and further developed by Duff, Reid, and Scott; we describe enhancements to the Sloan algorithm that improve its quality and reduce its run time. Our test problems fall into two classes with differing asymptotic behavior of their envelope parameters as a function of the weights in the Sloan algorithm. We describe an efficientO(nlogn+m) time implementation of the Sloan algorithm, wheren is the number of rows (vertices), andm is the number of nonzeros (edges). On a collection of test problems, the improved Sloan algorithm required, on the average, only twice the time required by the simpler RCM algorithm while improving the mean square wavefront by a factor of three. The second algorithm is a hybrid that combines a spectral algorithm for envelope and wavefront reduction with a refinement step that uses a modified Sloan algorithm. The hybrid algorithm reduces the envelope size and mean square wavefront obtained from the Sloan algorithm at the cost of greater running times. We illustrate how these reductions translate into tangible benefits for frontal Cholesky factorization and incomplete factorization preconditioning.

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.

The design of sparse direct solvers using object-oriented techniques

We describe our experience in designing object-oriented software for sparse direct solvers. We discuss Spindle, a library of sparse matrix ordering codes, and OBLIO, a package that implements the factorization and triangular solution steps of a direct solver. We discuss the goals of our design: managing complexity, simplicity of interface, flexibility, extensibility, safety, and efficiency. High performance is obtained by carefully implementing the computationally intensive kernels and by making several tradeoffs to balance the conflicting demands of efficiency and good software design. Some of the missteps that we made in the course of this work are also described.

High-performance language interoperability for scientific computing through Babel

High-performance scientific applications are usually built from software modules written in multiple programming languages. This raises the issue of language interoperability which involves making calls between languages, converting basic types, and bridging disparate programming models. Babel provides a feature-rich, extensible, high-performance solution to the language interoperability problem currently supporting C, C++, FORTRAN 77, Fortran 90/95, Fortran 2003/2008, Python, and Java. Babel supports object-oriented programming features and interface semantics with runtime enforcement. In addition to in-process language interoperability, Babel includes remote method invocation to support hybrid parallel and distributed computing paradigms.

How the common component architecture advances computational 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.

Component Technology for High-Performance Scientific Simulation Software

We are developing scientific software component technology to manage the complexity of modern, parallel simulation software and increase the interoperability and re-use of scientific software packages. In this paper, we describe a language interoperability tool named Babel that enables the creation and distribution of language-independent software libraries using interface definition language (IDL) techniques. We have created a scientific IDL that focuses on the unique interface description needs of scientific software, such as complex numbers, dense multidimensional arrays, and parallel distributed objects. Preliminary results indicate that in addition to language interoperability, this approach provides useful tools for the design of modern object-oriented scientific software libraries. We also describe a web-based component repository called Alexandria that facilitates the distribution, documentation, and reuse of scientific components and libraries.

Component-based software for high-performance scientific computing

Recent advances in both computational hardware and multidisciplinary science have given rise to an unprecedented level of complexity in scientific simulation software. This paper describes an ongoing grass roots effort aimed at addressing complexity in high-performance computing through the use of Component-Based Software Engineering (CBSE). Highlights of the benefits and accomplishments of the Common Component Architecture (CCA) Forum and SciDAC ISIC are given, followed by an illustrative example of how the CCA has been applied to drive scientific discovery in quantum chemistry. Thrusts for future research are also described briefly.

Object-Oriented Design for Sparse Direct Solvers

We discuss the object-oriented design of a software package for solving sparse, symmetric systems of equations (positive definite and indefinite) by direct methods. At the highest layers, we decouple data structure classes from algorithmic classes for flexibility. We describe the important structural and algorithmic classes in our design, and discuss the trade-offs we made for high performance. The kernels at the lower layers were optimized by hand. Our results show no performance loss from our object-oriented design, while providing flexibility, ease of use, and extensibility over solvers using procedural design.

An Object-Oriented Collection of Minimum Degree Algorithms

The multiple minimum degree (MMD) algorithm and its variants have enjoyed 20+ years of research and progress in generating fill-reducing orderings for sparse, symmetric positive definite matrices. Although conceptually simple, efficient implementations of these algorithms are deceptively complex and highly specialized. In this case study, we present an object-oriented library that implements several recent minimum degree-like algorithms. We discuss how object-oriented design forces us to decompose these algorithms in a different manner than earlier codes and demonstrate how this impacts the flexibility and efficiency of our C++ implementation. We compare the performance of our code against other implementations in C or Fortran.