# Your search: "author:"Husbands, Parry""

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## Scholarly Works (16 results)

Many high performance applications run well below the peak arithmetic performance of the underlying machine, with inefficiencies often attributed to poor memory system behavior. In the context of scientific computing we examine three emerging processors designed to address the well-known gap between processor and memory performance through the exploitation of data parallelism. The VIRAM architecture uses novel PIM technology to combine embedded DRAM with a vector co-processor for exploiting its large bandwidth potential. The DIVA architecture incorporates a collection of PIM chips as smart-memory coprocessors to a conventional microprocessor, and relies on superword-level parallelism to make effective use of the available memory bandwidth. The Imagine architecture provides a stream-aware memory hierarchy to support the tremendous processing potential of SIMD controlled VLIW clusters. First we develop a scalable synthetic probe that allows us to parametize key performance attributes of VIRAM, DIVA and Imagine while capturing the performance crossover points of these architectures. Next we present results for scientific kernels with different sets of computational characteristics and memory access patterns. Our experiments allow us to evaluate the strategies employed to exploit data parallelism, isolate the set of application characteristics best suited to each architecture and show a promising direction towards interfacing leading-edge processor technology with high-end scientific computations.

Two popular webpage ranking algorithms are HITS and PageRank. HITS emphasizes mutual reinforcement between authority and hub webpages, while PageRank emphasizes hyperlink weight normalization and web surfing based on random walk models. We systematically generalize/combine these concepts into a unified framework. The ranking framework contains a large algorithm space; HITS and PageRank are two extreme ends in this space. We study several normalized ranking algorithms which are intermediate between HITS and PageRank, and obtain closed-form solutions. We show that, to first order approximation, all ranking algorithms in this framework, including PageRank and HITS, lead to same ranking which is highly correlated with ranking by indegree. These results support the notion that in web resource ranking indegree and outdegree are of fundamental importance. Rankings of webgraphs of different sizes and queries are presented to illustrate our analysis.

The Conjugate Gradient (CG) algorithm is perhaps the best-known iterative technique to solve sparse linear systems that are symmetric and positive definite. In previous work, we investigated the effects of various ordering and partitioning strategies on the performance of CG using different programming paradigms and architectures. This paper makes several extensions to our prior research. First, we present a hybrid(MPI+OpenMP) implementation of the CG algorithm on the IBM SP and show that the hybrid paradigm increases programming complexity with little performance gains compared to a pure MPI implementation. For ill-conditioned linear systems, it is often necessary to use a preconditioning technique. We present MPI results for ILU(0) preconditioned CG (PCG) using the BlockSolve95 library, and show that the initial ordering of the input matrix dramatically affect PCG's performance. Finally, a multithreaded version of the PCG is developed on the Cray (Tera) MTA. Unlike the message-passing version, this implementation did not require the complexities of special orderings or graph dependency analysis. However, only limited scalability was achieved due to the lack of available thread level parallelism.

Algebraic sub-structuring refers to the process of applying matrix reordering and partitioning algorithms to divide a large sparse matrix into smaller submatrices from which a subset of spectral components are extracted and combined to form approximate solutions to the original problem. In this paper, we show that algebraic sub-structuring can be effectively used to solve generalized eigenvalue problems arising from the finite element analysis of an accelerator structure.

Sparse systems of linear equations and eigen-equations arise at the heart of many large-scale, vital simulations in DOE. Examples include the Accelerator Science and Technology SciDAC (Omega3P code, electromagnetic problem), the Center for Extended Magnetohydrodynamic Modeling SciDAC(NIMROD and M3D-C1 codes, fusion plasma simulation). The Terascale Optimal PDE Simulations (TOPS)is providing high-performance sparse direct solvers, which have had significant impacts on these applications. Over the past several years, we have been working closely with the other SciDAC teams to solve their large, sparse matrix problems arising from discretization of the partial differential equations. Most of these systems are very ill-conditioned, resulting in extremely poor convergenc deployed our direct methods techniques in these applications, which achieved significant scientific results as well as performance gains. These successes were made possible through the SciDAC model of computer scientists and application scientists working together totake full advantage of terascale computing systems and new algorithms research.