17536925
Building the Teraflops/Petabytes Production Supercomputing Center
Building the Teraflops/Petabytes Production Supercomputing Center Horst D. Simon, William T. C. Kramer, and Robert F. Lucas National Energy Research Scientific Computing Center (NERSC), Mail Stop 50B-4230, Lawrence Berkeley National Laboratory, Berkeley, CA 94720 {hdsimon, wtkramer, rflucas}@lbl.gov Abstract. In just one decade, the 1990s, supercomputer centers have undergone two fundamental transitions which require rethinking their operation and their role in high performance computing. The first transition in the early to mid-1990s resulted from a technology change in high performance computing architecture. Highly parallel distributed memory machines built from commodity parts increased the operational complexity of the supercomputer center, and required the introduction of intellectual services as equally important components of the center. The second transition is happening in the late 1990s as centers are introducing loosely coupled clusters of SMPs as their premier high performance computing platforms, while dealing with an ever-increasing volume of data. In addition, increasing network bandwidth enables new modes of use of a supercomputer center, in particular, computational grid applications. In this paper we describe what steps NERSC is taking to address these issues and stay at the leading edge of supercomputing centers. Introduction In just one decade, the 1990s, supercomputer centers have undergone two fundamental transitions which require a rethinking of the basic tenets of their operation and their role in the high performance computing (HPC) world. The first transition in the early to mid 1990s was a result of a technology change in high performance computing architecture. The introduction of highly parallel distributed memory machines built from commodity parts increased the operational complexity of the supercomputer center, and required the introduction of intellectual services as equally important components of the center. We have only recently completed this transition and developed the tools necessary to bring the revolution of the mid-1990s to a successful conclusion. Now three new developments are appearing which will again force us to step up to new challenges in supercomputer center management: (1) yet another change in the architecture of supercomputing platforms, (2) the increasing importance of managing large volumes of scientific data, and (3) the deployment of a new generation of high-speed wide-area
38908855
Large-Scale SVD and Subspace-Based Methods for Information Retrieval
A theoretical foundation for latent semantic indexing (LSI) is proposed by adapting a model first used in array signal processing to the context of information retrieval using the concept of subspaces. It is shown that this subspace-based model coupled with minimal description length (MDL) principle leads to a statistical test to determine the dimensions of the latent-concept subspaces in LSI. The effect of weighting on the choice of the optimal dimensions of latent-concept subspaces is illustrated. It is also shown that the model imposes a so-called low-rank-plus-shift structure that is approximately satisfied by the cross-product of the term-document matrices. This structure can be exploited to give a more accurate updating scheme for LSI and to correct some of the misconception about the achievable retrieval accuracy in LSI updating. Variants of Lanczos algorithms are illustrated with numerical test results on Cray T3E using document collections generated from World Wide Web.
35264400
Parallel Efficiency of the Lanczos Method for Eigenvalue Problems
LBNL-42828 Parallel Efficiency of the Lanczos Method for Eigenvalue Problems ^ Kesheng Wu+and Horst Simon* Abstract Two of the commonly used versions of the Lanczos method for eigenvalues problems are the shift-and-invert Lanczos method and the restarted Lanczos method. In this talk, we will address two questions, is the shift-and-invert Lanczos method a viable option on massively parallel machines and which one is more appropriate for a given eigenvalue problem? Introduction T h i s talk is o n how to compute eigenvalues a n d eigenvectors of large sparse s y m m e t r i c matrices o n massively parallel machines. One of the the most c o m m o n l y used algorithms for this task is the Lanczos m e t h o d w h i c h projects the large eigenvalue p r o b l e m onto a low dimensional K r y l o v subspace [7, 10]. I n most cases, the K r y l o v subspace basis is built t h r o u g h a series of matrix-vector m u l t i p l i c a t i o n s , the whole Lanczos m e t h o d c a n be implemented w i t h a matrix-vector m u l t i p l i c a t i o n routine a n d a few simple vector operations. T h i s a l g o r i t h m is highly efficient on parallel machines a n d it is effective for c o m p u t i n g extreme a n d well separated eigenvalues. To compute the interior or not well-separated eigenvalues, the shift-and-invert Lanczos m e t h o d is one of the most effective methods. Since the eigenvalues of a m a t r i x A are related to the eigenvalues of (A — c r l ) by a simple relation X(A) = a + 1/X((A — c r l ) ) a n d the corresponding eigenvectors of A a n d (A — c r l ) are identical, the shift-and-invert scheme computes the extreme eigenvalues of (A — c r l ) a n d deduce the corresponding eigenvalues of A. W i t h appropriate choice of a, the extreme eigenvalues of (A — al)^ are well separated and can be easily computed. T h e shift-and-invert Lanczos m e t h o d needs to b u i l d a K r y l o v subspace basis of (A — c r l ) w h i c h is done by solving a series of linear systems involving the coefficient m a t r i x (A — a I). T h e linear systems need to be solved accurately a n d the only reliable means to accomplish this is by using a direct m e t h o d [5]. Because it is difficult to implement direct methods o n d i s t r i b u t e d parallel computers, efficient parallel i m p l e m e n t a t i o n has not been widely available u n t i l recently [1, 8]. T h i s talk w i l l present our s t u d y of the parallel efficiency of the shift-and-invert Lanczos m e t h o d using these newly available direct solvers. T h e study w i l l show that the shift-and-invert Lanczos m e t h o d is ' T h i s w o r k was s u p p o r t e d b y t h e D i r e c t o r , Office o f E n e r g y R e s e a r c h , Office o f L a b o r a t o r y P o l i c y a n d Infrastructure M a n a g e m e n t , of the U . S . D e p a r t m e n t of E n e r g y under C o n t r a c t N o . D E - A C 0 3 - 7 6 S F 0 0 0 9 8 . T h i s r e s e a r c h u s e d r e s o u r c e s o f t h e N a t i o n a l E n e r g y R e s e a r c h S c i e n t i f i c C o m p u t i n g C e n t e r , w h i c h is s u p p o r t e d b y t h e Office o f E n e r g y R e s e a r c h o f t h e U . S . D e p a r t m e n t o f E n e r g y . 'Lawrence Berkeley National Laboratory/NERSC, Berkeley, CA Email: {ksni, hdsimon}81bl.gov.
46294089
1992 Gordon Bell Prize Winners
9150119
Thick-Restart Lanczos Method for Symmetric Eigenvalue Problems
This paper describes a restarted Lanczos algorithm that particularly suitable for implementation on distributed machines. The only communication operation is requires outside of the matrix-vector multiplication is a global sum. For most large eigenvalue problems, the global sum operation takes a small fraction of the total execution time. The majority of the computer is spent in the matrix-vector multiplication. Efficient parallel matrix-vector multiplication routines can be found in many parallel sparse matrix packages such as AZTEC [9], BLOCK-SOLVE [10], PETSc [3], P_SPARSLIB. For this reason, our main emphasis in this paper is to demonstrate the correctness and the effectiveness of the new algorithm.
36271721
Industrial Application Areas of High-Performance Computing
In 1993, a list of the top 500 supercomputer sites worldwide was made available for the first time. Since then, the Top500 list has been published twice a year. The list allows a detailed and well-founded analysis of the state of high-performance computing (HPC). This article summarizes the recent trends in application areas of HPC systems, focusing on the increase in industrial installations and applications.
17561186
Situated Action: A Symbolic Interpretation
The congeries of theoretical views collectively referred to as “situated action” (SA) claim that humans and their interactions with the world cannot be understood using symbol-system models and methodology, but only by observing them within real-world contexts or building nonsymbolic models of them. SA claims also that rapid, real-time interaction with a dynamically changing environment is not amenable to symbolic interpretation of the sort espoused by the cognitive science of recent decades. Planning and representation, central to symbolic theories, are claimed to be irrelevant in everyday human activity. 
 
We will contest these claims, as well as their proponents' characterizations of the symbol-system viewpoint. We will show that a number of existing symbolic systems perform well in temporally demanding tasks embedded in complex environments, whereas the systems usually regarded as exemplifying SA are thoroughly symbolic (and representational), and, to the extent that they are limited in these respects, have doubtful prospects for extension to complex tasks. As our title suggests, we propose that the goals set forth by the proponents of SA can be attained only within the framework of symbolic systems. The main body of empirical evidence supporting our view resides in the numerous symbol systems constructed in the past 35 years that have successfully simulated broad areas of human cognition.
21852626
Search and Reasoning in Problem Solving
In the course of the extensive research that has been done on problem solving in AI , several rather distinct ways have emerged for representing and thinking about problem solving tasks. One way to view problem solving is as search. We postulate some kind of space in which treasures are hidden. We build symbol structures (nodes) that model this space, and 'move ' operators that alter these symbol structures, taking us from one node to another. In this metaphor, solving a problem consists in searching the model of the space (selectively), moving from one node to another along links that connect them until a treasure is encountered. A second way of viewing problem solving is as reasoning. We postulate a system of logic that allows us to deduce new statements from axioms and previously deduced statements. We represent a problem by a set of axioms in the formal language of our logic. In this metaphor, solving the problem consists in accumulating more and more information (more and more statements) by inference until the answer to the problem has been found. A third way of viewing problem solving is as constraint satisfaction. We postulate a set of objects and various subsets defined by the constraints they satisfy. In this metaphor, solving a problem consists in narrowing down the original set to a subset or unique object that satisfies all of the constraints. In no sense are the metaphors mutually exclusive; metaphors seldom are. The same problem-solving algorithm can be viewed, now as search, now as reasoning, now as constraint satisfaction. Consider, for example, a simple theorem-proving program that works forward from a set of axioms, applying its rules of inference to these to obtain new expressions that can be added to the
37804897
Directions for Qualitative Reasoning
Sacks & Doyle provide an excellent overview of the fundamental limitations of the SPQR representations for reasoning about the qualitative properties of dynamic systems. We take this opportunity to outline some new directions for qualitative reasoning. In this paper, we provide a rigorous mathematical characterization for the term “qualitative property” in the context of static and dynamic systems. Based on these characterizations, we show that interval representations are well suited for reasoning about the qualitative properties of static systems such as qualitative comparative statics and qualitative stability. Moreover, we also show that symbolic computations help in the derivation of useful global properties of dynamic systems which can be used to guide numerical sampling of differential equations. The integration of symbolic and numeric methods provides a powerful approach for automating the qualitative analysis of differential equations.
30978174
Whether software engineering needs to be artificially intelligent
The author discusses the roles that humans now play versus the roles that could be taken over by artificial intelligence in developing computer systems. Also discussed is how the intelligent part of the automatic system can communicate effectively with humans. Topics covered include an artificial intelligence overview; weak methods; the heuristic search; the problem space; the knowledge base; expert systems; and conclusions drawn from a description of the general artificial intelligence paradigm.
6846387
Causality and Model Abstraction
Abstract Much of science and engineering is concerned with characterizing processes by equations describing the relations that hold among parameters of objects and govern their behavior over time. In formal descriptions of processes in terms of parameters and equations, the notion of causality is rarely made explicit. Formal treatments of the foundations of sciences have avoided discussions of causation and spoken only of functional relations among variables. Nevertheless, the notion of causality plays an important role in our understanding of phenomena. Even when we describe the behavior of a system formally in terms of acausal, mathematical relations, we often give an informal, intuitive explanation of why the system behaves the way it does in terms of cause-effect relations. In this paper, we will present an operational definition of causal ordering. The definition allows us to extract causal dependency relations among variables implicit in a model of a system, when a model is represented as a set of acausal, mathematical relations. Our approach is based on the theory of causal ordering first presented by Simon [22]. The paper shows how to use the theory and its extension in reasoning about physical systems. Further, the paper studies the relation of the theory to the problems of model aggregation.