17421545
Recent trends in the marketplace of high performance computing
In this paper we analyze major recent trends and changes in the High Performance Computing (HPC) market place. The introduction of vector computers started the area of 'Supercomputing'. The initial success of vector computers in the seventies was driven by raw performance. Massive parallel systems (MPP) became successful in the early nineties due to their better price/performance ratios, which was enabled by the attack of the 'killer-micros'. The success of microprocessor based on the shared memory concept (referred to as symmetric multiprocessors (SMP)) even for the very high-end systems, was the basis for the emerging cluster concepts in the early 2000s. Within the first half of this decade clusters of PC's and workstations have become the prevalent architecture for many HPC application areas on all ranges of performance. However, the Earth Simulator vector system demonstrated that many scientific applications could benefit greatly from other computer architectures. At the same time there is renewed broad interest in the scientific HPC community for new hardware architectures and new programming paradigms. The IBM BlueGene/L system is one early example of a shifting design focus for large-scale system. The DARPA HPCS program has the declared goal of building a Petaflops computer system by the end of the decade using novel computer architectures.
965932
Editors' Introduction
Not since the publication of Rachel Carson's Silent Spring (1962) has a book about the environment provoked such controversy as Bjorn Lomborg's The Skeptical Environmen-talist (2001). In the four decades since Silent Spring a popular environmental awareness and suspicion of ''chemicals'' and other aspects of modern industrial life has increasingly challenged and, in some cases, displaced scientific and technological optimism as an orthodox view throughout most of the industrialized world. A proliferation of nongovern-mental organizations and scientific bodies repeatedly invoke science to warn us of the potential for irreversible and, possibly, catastrophic environmental decline, unless people on the planet mend their ways. Powerful interests resist this message, increasingly by attacking the science that informs it. In this context, it may seem unsurprising that Lomborg's challenge to contemporary catastrophic views of inexorable global environmental decline has itself attracted criticism from environmentalists. Perhaps what is surprising are the amount of attention, the degree of opprobrium, and the ad hominem nature of the criticism that has been launched at Lomborg for a book that builds upon a tradition of similar arguments. Indeed the central theme of TSE, that some combination of business-as-usual and incremental change will be sufficient for children born today to ''get more food, a better education, a higher standard of living, more leisure time and far more possibi-lities—without the global environment being destroyed'', seems a relatively modest assertion of the cornucopian world view by comparison with some of Lomborg's predecessors, such as deconstructions of environmental concerns over the preceding decade including Ron Bailey's Ecoscam (1993) and Aaron Wildavsky's But is it True? (1995) provoked few serious rebuttals from scientists or environmental activists. The controversy over TSE serves to remind us of the anthropological observation that science is the trump card that we play in disputes about values. In our contemporary society, science has become the medium through which we understand nature. However, science has also become the medium through which we wage political battles not just on the environment, but also over many other contested issues. Hence, the debate over TSE provides a rich case study through which to examine issues associated with the relationships among science, policy, and politics, which observers on the right (e.g., Gough, 2003) and left (e.g., UCS, 2004) of the US political spectrum agree is a critical area for governance. The five papers that comprise this special issue were originally solicited for a symposium titled …
46294089
1992 Gordon Bell Prize Winners
6270234
Spectral Relaxation for K-means Clustering
The popular K-means clustering partitions a data set by minimizing a sum-of-squares cost function. A coordinate descend method is then used to find local minima. In this paper we show that the minimization can be reformulated as a trace maximization problem associated with the Gram matrix of the data vectors. Furthermore, we show that a relaxed version of the trace maximization problem possesses global optimal solutions which can be obtained by computing a partial eigendecomposition of the Gram matrix, and the cluster assignment for each data vectors can be found by computing a pivoted QR decomposition of the eigenvector matrix. As a by-product we also derive a lower bound for the minimum of the sum-of-squares cost function.
6041481
Parallel preconditioning and approximation inverses on the Connection Machine
The authors present a new approach to preconditioning for very large, sparse, non-symmetric, linear systems. It explicitly computes an approximate inverse to the original matrix that can be applied most efficiently for iterative methods on massively parallel machines. The algorithm and its implementation on the Connection Machine CM-2 are discussed in detail and supported by timings obtained from real problem data.<<ETX>>
26191709
The Implementation of the U.S. High Performance Computing and Communications Program
This paper gives an overview of the federal High Performance Computing and Communication (HPCC) Program. This initiative involves the Defense Advanced Projects Agency (DARPA), the Department of Energy (DOE), the National Aeronautics and Space Administration (NASA), and the National Science Foundation (NSF). Its goal is to advance U.S. science and industrial competitiveness by supporting the development of teraops high performance systems, the software and algorithms required to render those massively parallel systems efficiently usable, and the Gigabit networks to make those machines accessible, as well as to broaden the base of computationally literate scientists. We outline the HPCC effort and the rationale underlying it, and give an overview of some of the “Grand Challenge” work under way at Argonne’s Mathematics and Computer Science Division, as well as the Grand Challenges addressed by NASA’s Computational Aerosciences program.
28048643
Generalization of an Elementary Perceiving and Memorizing Machine
The Elementary Perceiver and Memorizer (EPAM) is a context, and contains sufficient information to retrieve the model of human associative memory and the processes of image of the stimulus from the association memory when human verbal learning, written in the list-processing language necessary. IPL-V. A generalization of the EPAM model (called EPAM The complex image building processes also provide an assoIII), based on earlier experiments on EPAM 11, has been dative mechanism by means of which earlier learning can formulated and programmed. usefully be brought to bear on later learning. New stimuli arc In EPAM 111. stimulus configurations and their internal learned by associating together and modifying images of images are allowed indefinite complexity of sub-parts. Complex stimuli already learned. The model is an attempt to resolve internal images are built by associating together, in list structhe problem of how meaningful learning differs from nontures, tokens for retrieving information about the sub-parts meaningful learning, from the association memory. Empirical explorations of the model are concerned generally The concatenation of tokens by the image building process with variations in behaviour as a result of varying stimulus allows for the association of stimuli in many different contexts. and response meaningfulness. The token signifies the occurrence of a stimulus in a particular 3.ieM6HTapHan nporpaMMa jhh "H3yqaioinHx MauaeT coSbiTHe CTHMyjia b oco66hhom KOHTeKCTe, a npiiuihh" (3IIJIHM (EPAM)) npeacTaß^weTCH Moaejibio cyTCTByeT b aaHHOM naineKe aocTaToiHO HHdpopMauHH, accouHaTHßiioil nfiMfiTH h npoueccoß H3yiemm mob <rro6bi BooCpawenHe CTHMyjia moskho 6mjio Bbißec" y iciOBCKa; saiman Mo,Te.-ib narmcaHa b bh«6 si3uko, hs accouHaTHßHofl naMHTH b tom cnyqae, eoJiH sto A-1H pa3paf)OTKH CnHCKOB IPL 15. Ha OCHOBaHHH OKaHCeTCH Heo6xo«HMbiM. pe3y.ibTaTon Go.ice paiiHHx nonwTOK cihopMHyjinpoCjioiKHbie npoueccw, no3Bo^aiouiHe EPAM 111 CO3Ba.iacb ii cnporpaHMHpoßa,iacb ooo6meHHaH Moae^b naßaTb BoofipasKeHHH, Taicwe 3anaxaioT accoimaTHBEPAM jiß.inioumncHTaX Ha3bißaeMoft EPAM 111. Hiiit MexaHH3M, npn noMoiiw KOToporo npeAbiAymne B EPAM 111, mc.no noaiacTefl KOMiuieKCHbix <~thonuTH MoryT 3<h<beKTHßHbiM o6pa3oM BJiHHTb Ha cnemv.iob h BiiyTpeiiHHx hx n3oGpan<eHHH MomeT decayroiiuie. Hoßbie CTHMyjiu H3yiaioTCfl b pe3yjibTaTe npe.ie.-n.no yc.iovKHfiTbcu. OioiKHbie BiiyTpeHHHe accoiiHpoßaHHtf (tbm caMMM h Mo«H<bHKau.HH) HsyienH3oopn>KeHHn-iiMeiomHeMHoroypoßHeflHH(})opMauHH hux panbine BOo6pajKeHHft. MoaeJib npeacTaßJineT o no.tCTpyuTypectpohtch cnocoSoM accouHHpoßaHHH coCoft nonbrricy peuiHTb 3a«aqy o tom, KaKHM oopa3OM 3iia«iKon miMRKOB, npw noMomH kotopux cBe^eHHH pa3JinqaTb 3HaiaromHe CBeaenHH ot He3HaiaiomHx. o no.macTnx mo>kho BUBO«HTb H3 accouHaTHßHofl 3MnHpmiecKHe onwTbi npoßOflHmnecH Ha Mo^e^b, naMtiTii. Boo6me roßopn, hmciot c paaHUMH BH«aMH noße-3auen.ieHne iiaMeKOB npoueccoM co3«aHHH boosaeHHn, npoHCxofIHWHMH H3 pa3HOBHfIHOCTH 3HaienHfi pn>Kenmi no3Bo.ineT accou.HHpoßaTb cTHMy^u, npoHCTHMy^a h oTBeTa. cxoanmiio H3 Miioro pa3Hbix kohtckctob. HaMeK 03HaLa machine elementaire pour percevoir et mettre en mblir Information concernant les sous-parties, a partir de la moire (EPAM) est un modele de la memoire humaine d'assomemoire dissociation. ciation. et dcs process de la facon humaine d'apprendre L'enchainement dcs signes au moyen de l'image eUabor^e verbalement, 6crit en langage IPL-V. En partant dcs r«sulpermet d'associer dcs excitations dans un grand nombre de tnts de tests anteVieurs, on a dlabore' et programme" une contextes diffe>ents. Le signe montre qu'il existe une excitagcneralisation dv modele EPAM, appele EPAM 111. tion dans un contexte particulier et une information suffisante Sur EPAM 111, les configurations d'excitation et les images peut, grace ace signe, regene>er l'image de I'excitation a inteneures de ces configurations ont une complexitc indefinie partir de la memoire dissociation, quand cela est necessaire. de sous-parlies. Dcs images internes complexes sont dlaborees Les procedis complexes gui 61aborent l'image fournissent en groupant. en liste de structures, les signes destine* a retaaussi un m6canisme dissociation, permettant d'elaborer faci405 IX, 1] automata theory lement la toute premiere information a partir de la toute informations ayant un sens, de celles gui n'en ont pas. derniere. Les tests empiriques dv modele se rapportent gene>alement On decouvre de nouvelles excitations en groupant (et en aux variations de fonctionnement, celles-ci £tant le resultat modifiant) les images d'excitations deja connues. Cc modele d'excitations et de reponses variables. est un essai de r6soudre le probleme de la differentiation dcs
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.
37317695
The mathematical bases for qualitative reasoning
The practices of researchers in many fields who use qualitative reasoning are summarized and explained. The goal is to gain an understanding of the formal assumptions and mechanisms that underlie this kind of analysis. The explanations given are based on standard mathematical formalisms, particularly on ordinal properties, continuous differentiable functions, and the mathematics of nonlinear dynamic systems.<<ETX>>
16243913
Experiments with a Heuristic Compiler
This report describes some experiments in constructing a compiler that makes use of heuristic problem~solving techniques such as those incorporated in the General Problem Solver (GPS) [1]. The experiments were aimed at the dual objectives of throwing light on some of the problems of constructing more powerful programming languages and compilers, and of testing whether the task of writing a computer program can be regarded as a "problem" in the sense in which that term is used in GPS. The present paper is concerned primarily with the second objective--with analyzing some of the problem-solving processes that are involved in writing computer programs. At the present stage of their development, no claims will be made for the heuristic programming procedures described here as practical approaches to the construction of compilers. Their interest lies in what they teach us about the nature of the programming task.
6977019
Rule Creation and Rule Learning Through Environmental Exploration
The task of learning from environment is specified. It requires the learner to infer the laws of the environment in terms of its percepts and actions, and use the laws to solve problems. Based on research on problem space creation and discrimination learning, this paper reports an approach in which exploration, rule creation and rule learning are coordinated in a single framework. With this approach, the system LIVE creates STRIPS-Iike rules by noticing the changes in the environment when actions are taken, and later refines the rules by explaining the failures of their predictions. Unlike many other learning systems, since LIVE treats learning and problem solving as interleaved activities, no training instance nor any concept hierarchy is necessary to start learning. Furthermore, the approach is capable of discovering hidden features from the environment when normal discrimination process fails to make any progress.