this article, we emphasize practical issues; an earlier survey by Bern and Eppstein [24] emphasized theoretical results. Although there is inevitably some overlap between these two surveys, we intend them to be complementary.

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This paper explores the range of constraint-based techniques used to alter motions while preserving specific spatial features. We examine a variety of methods, defining a taxonomy of these methods that is categorized by the mechanism employed to enforce temporal constraints. We pay particular attention to a less explored category of techniques that we term per-frame inverse kinematics plus filtering, and we show how these methods may provide an easier to implement while retaining the benefits of other approaches

Numerical experiments are presented whereby the effect of reorderings on the convergence of preconditioned Krylov subspace methods for the solution of nonsymmetric linear systems is shown. The preconditioners used in this study are different variants of incomplete factorizations. It is shown that certain reorderings for direct methods, such as reverse Cuthill–McKee, can be very beneficial. The benefit can be seen in the reduction of the number of iterations and also in measuring the deviation of the preconditioned operator from the identity.

We consider performance tuning, by code and data structure reorganization, of sparse matrix-vector multiply (SpMV), one of the most important computational kernels in scientific applications. This paper addresses the fundamental questions of what limits exist on such performance tuning, and how closely tuned code approaches these limits.

. As the amount of electronic information increases, traditional lexical (or Boolean) information retrieval techniques will become less useful. Large, heterogeneous collections will be difficult to search since the sheer volume of unranked documents returned in response to a query will overwhelm the user. Vector-space approaches to information retrieval, on the other hand, allow the user to search for concepts rather than specific words and rank the results of the search according to their relative similarity to the query. One vector-space approach, Latent Semantic Indexing (LSI), has achieved up to 30% better retrieval performance than lexical searching techniques by employing a reduced-rank model of the term-document space. However, the original implementation of LSI lacked the execution efficiency required to make LSI useful for large data sets. A new implementation of LSI, LSI++, seeks to make LSI efficient, extensible, portable, and maintainable. The LSI++ Application Programming ...

We describe an object oriented sparse matrix library in C++ designed for portability and performance across a wide class of machine architectures. Besides simplifying the subroutine interface, the object oriented design allows the same driving code to be used for various sparse matrix formats, thus addressing many of the difficulties encountered with the typical approach to sparse matrix libraries. We also discuss the design of a C++ library for implementing various iterative methods for solving linear systems of equations. Performance results indicate that the C++ codes are competitive with optimized Fortran. 1 Introduction Sparse matrices are pervasive in scientific and engineering application codes. They often arise from finite difference, finite element, or finite volume discretizations of PDEs (e.g., in computational fluid dynamics) or from discrete, network-type problems (e.g., in circuit simulation). Over the past two decades, a number of research efforts have resulted in spars...

A new method is presented for distributing data in sparse matrix-vector multiplication. The method is two-dimensional, tries to minimise the true communication volume, and also tries to spread the computation and communication work evenly over the processors. The method starts with a recursive bipartitioning of the sparse matrix, each time splitting a rectangular matrix into two parts with a nearly equal number of nonzeros. The communication volume caused by the split is minimised. After the matrix partitioning, the input and output vectors are partitioned with the objective of minimising the maximum communication volume per processor. Experimental results of our implementation, Mondriaan, for a set of sparse test matrices show a reduction in communication compared to one-dimensional methods, and in general a good balance in the communication work.

. We investigate the use of sparse approximate inverse preconditioners for the iterative solution of linear systems with dense complex coefficient matrices arising from industrial electromagnetic problems. An approximate inverse is computed via a Frobenius norm approach with a prescribed nonzero pattern. Some strategies for determining the nonzero pattern of an approximate inverse are described. The results of numerical experiments suggest that sparse approximate inverse preconditioning is a viable approach for the solution of large-scale dense linear systems on parallel computers. Key words. Dense linear systems, preconditioning, sparse approximate inverses, complex symmetric matrices, scattering calculations, Krylov subspace methods, parallel computing. AMS(MOS) subject classification. 65F10, 65F50, 65R20, 65N38, 78-08, 78A50, 78A55. 1. Introduction. In the last decade, a significant amount of effort has been spent on the simulation of electromagnetic wave propagation phenomena to ad...

offer significant advantages over shared memory multiprocessors in terms of cost and scalability. Unfortunately, the utilization of all the available computational power in these machines involves a tremendous programming effort on the part of users, which creates a need for sophisticated compiler and run-time support for distributed memory machines. In this paper, we explore a new compiler optimization for regular scientific applications–the simultaneous exploitation of task and data parallelism. Our optimization is implemented as part of the PARADIGM HPF compiler framework we have developed. The intuitive idea behind the optimization is the use of task parallelism to control the degree of data parallelism of individual tasks. The reason this provides increased performance is that data parallelism provides diminishing returns as the number of processors used is increased. By controlling the number of processors used for each data parallel task in an application and by concurrently executing these tasks, we make program execution more efficient and, therefore, faster. A practical implementation of a task and data parallel scheme of execution for an application on a distributed memory multicomputer also involves data redistribution. This data redistribution causes an overhead. However, as our experimental results show, this overhead is not a problem; execution of a program using task and data parallelism together can be significantly faster than its execution using data parallelism alone. This makes our proposed optimization practical and extremely useful.