Dynamic Range Partitioning in Multiprocessor Database Implementations

Multiprocessor implementation of the relational database operators has recently received great attention in literature [1-4, 8, 11]. As the complexity of implementing the relational operators rests on the inter-node communication patterns involved in an operation, greater research attention has been focused on Join algorithms. The Join traffic patterns subsume those of the remaining relational operators. To effectively exploit parallelism in bucket based join implementations, the domain of the joining attributes must be partitioned into equal subranges. That is, the processing of each subrange requires roughly the same amount of time. A skewed distribution of workload significantly hinders performance. As relations exhibit a non-uniform attribute value distribution, possibly resulting from a previous operation, a priori determination of subrange boundary conditions results in a non-balanced workload across the processors. Performance degradation in parallel systems employing such static boundary subrange partitioning is demonstrated in Lakshmi and Yu [6]. That study exemplified that even a low degree of attribute skew results in a significant performance penalty. This paper proposes a statistical algorithm for dynamic determination of domain partitioning in bucket based join implementations. This statistics-based approach guarantees a near-uniform processor workload. A parameterization of the sample size versus the number of tuples is developed, and a proof of the validity of the approach is discussed. A simple illustrative example is presented

Three Highly Parallel Computer Architectures and Their Suitability for Three Representative Artificial Intelligence Problems

Virtually all current Artificial Intelligence (AI) applications are designed to run on sequential (von Neumann) computer architectures. As a result, current systems do not scale up. As knowledge is added to these systems, a point is reached where their performance quickly degrades. The performance of a von Neumann machine is limited by the bandwidth between memory and processor (the von Neumann bottleneck). The bottleneck is avoided by distributing the processing power across the memory of the computer. In this scheme the memory becomes the processor (a smart memory ). This paper highlights the relationship between three representative AI application domains, namely knowledge representation, rule-based expert systems, and vision, and their parallel hardware realizations. Three machines, covering a wide range of fundamental properties of parallel processors, namely module granularity, concurrency control, and communication geometry, are reviewed: the Connection Machine (a fine-grained SIMD hypercube), DADO (a medium-grained MIMD/SIMD/MSIMD tree-machine), and the Butterfly (a coarse-grained MIMD Butterflyswitch machine)

Parallel computing in information retrieval - An updated review

The progress of parallel computing in Information Retrieval (IR) is reviewed. In particular we stress the importance of the motivation in using parallel computing for Text Retrieval. We analyse parallel IR systems using a classification due to Rasmussen [1] and describe some parallel IR systems. We give a description of the retrieval models used in parallel Information Processing.. We describe areas of research which we believe are needed

Squashed embedding of E-R schemas in hypercubes

We have been investigating an approach to parallel database processing based on treating Entity-Relationship (E-R) schema graphs as dataflow graphs. A prerequisite is to find appropriate embeddings of the schema graphs into a processor graph, in this case a hypercube. This paper studies a class of adjacency preserving embeddings that map a node in the schema graph into a subcube (relaxed squashed or RS embeddings) or into adjacent subcubes (relaxed extended squashed or RES embeddings) of a hypercube. The mapping algorithm is motivated by the technique used for state assignment in asynchronous sequential machines. In general, the dimension of the cube required for squashed embedding of a graph is called the weak cubical dimension or WCD of the graph. The RES embedding provides an RES-WCD of O([left ceiling]log2n[right ceiling]) for a completely connected graph, Kn, and RS embedding provides an RS-WCD of O([left ceiling]log2n[right ceiling] + [left ceiling]log2m[right ceiling]) for a completely connected bigraph, Km,n. Typical E-R graphs are incompletely connected bigraphs. An algorithm for embedding incomplete bigraphs is presented.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/28651/1/0000467.pd

Automatic visual recognition using parallel machines

Invariant features and quick matching algorithms are two major concerns in the area of automatic visual recognition. The former reduces the size of an established model database, and the latter shortens the computation time. This dissertation, will discussed both line invariants under perspective projection and parallel implementation of a dynamic programming technique for shape recognition. The feasibility of using parallel machines can be demonstrated through the dramatically reduced time complexity. In this dissertation, our algorithms are implemented on the AP1000 MIMD parallel machines. For processing an object with a features, the time complexity of the proposed parallel algorithm is O(n), while that of a uniprocessor is O(n2). The two applications, one for shape matching and the other for chain-code extraction, are used in order to demonstrate the usefulness of our methods. Invariants from four general lines under perspective projection are also discussed in here. In contrast to the approach which uses the epipolar geometry, we investigate the invariants under isotropy subgroups. Theoretically speaking, two independent invariants can be found for four general lines in 3D space. In practice, we show how to obtain these two invariants from the projective images of four general lines without the need of camera calibration. A projective invariant recognition system based on a hypothesis-generation-testing scheme is run on the hypercube parallel architecture. Object recognition is achieved by matching the scene projective invariants to the model projective invariants, called transfer. Then a hypothesis-generation-testing scheme is implemented on the hypercube parallel architecture

Thinking Big in a Small World — Efficient Query Execution on Small-Scale SMPs

Many techniques developed for parallel database systems were focused on large-scale, often prototypical, hardware platforms. Therefore, most results cannot easily be transfered to widely available workstation clusters such as multiprocessor workstations. In this paper we address exploitation of pipelining parallelism in query processing on small multiprocessor environments. We present DTE/R, a strategy for executing pipelining segments of arbitrary length by replicating the segment's operator. Therefore, DTE/R avoids static processor-to-operator assignment of conventional processing techniques. Consequently, DTE/R achieves automatic load-balancing and skew-handling. Furthermore, DTE/R outperforms conventional pipelining execution techniques substantially

Parallel database operations in heterogeneous environments

Im Gegensatz zu dem traditionellen Begriff eines Supercomputers, der aus vielen mittels superschneller, lokaler Netzwerkverbindungen miteinander verbundenen Superrechnern besteht, basieren heterogene Computerumgebungen auf "kompletten" Computersystemen, die mit Hilfe eines herkömmlichen Netzwerkanschlusses an private oder öffentliche Netzwerke angeschlossen sind. Der Bereich des Computernetzwerkens hat sich über die letzten drei Jahrzehnte entwickelt und ist, wie viele andere Technologien, in bezug auf Performance, Funktionalität und Verlässlichkeit extrem gewachsen. Zu Beginn des 21.Jahrhunderts zählt das betriebssichere Hochgeschwindigkeitsnetz genauso zur Alltäglichkeit wie Elektrizität, und auch Rechnerressourcen sind, was Verfügbarkeit und universellen Gebrauch anbelangt, ebenso Standard wie elektrischer Strom. Wissenschafter haben für die Verwendung von heterogenen Grids bei verschiedenen rechenintensiven Applikationen eine Architektur von computational Grids konzipiert und darin Modelle aufgesetzt, die zum einen Rechenleistungen defnieren und zum anderen die komplexen Eigenschaften der Grid-Organisation vor den Benutzern verborgen halten. Somit wird die Verwendung für den Benutzer genauso einfach wie es möglich ist elektrischen Strom zu beziehen. Grundsätzlich existiert keine generell akzeptierte Definition für Grids. Einige Wissenschafter bezeichnen sie als hochleistungsfähige verteilte Umgebung. Manche berücksichtigen bei der Definierung auch die geographische Verteilung und ihre Multi-Domain-Eigenschaft. Andere Wissenschafter wiederum definieren Grids über die Anzahl der Ressourcen, die sie verbinden. Parallele Datenbanksysteme haben in den letzten zwei Jahrzehnten große Bedeutung erlangt, da das rechenintensive wissenschaftliche Arbeiten, wie z.B. auf dem Gebiet der Bioinformatik, Strömungslehre und Hochenergie physik die Verarbeitung riesiger verteilter Datensätze erfordert. Diese Tendenz resultierte daraus, dass man von der fehlgeschlagenen Entwicklung hochspezialisierter Datenbankmaschinen zur Verwendung herkömmlicher paralleler Hardware-Architekturen übergegangen ist. Grundsätzlich wird die gleichzeitige Abarbeitung entweder durch verteilte Datenbankoperationen oder durch Datenparallelität gelöst. Im ersten Fall wird ein unterteilter Abfragenabarbeitungsplan durch verschiedene Datenbankoperatoren parallel durchgeführt. Im Fall der Datenparallelität erfolgt eine Unterteilung der Daten, wobei mehrere Prozessoren die gleichen Operationen parallel an Teilen der Daten durchführen. Es liegen genaue Analysen von parallelen Datenbank-Arbeitsvorgängen für sequenzielle Prozessoren vor. Eine Reihe von Publikationen haben dieses Thema abgehandelt und dabei Vorschläge und Analysen für parallele Datenbankmaschinen erstellt. Bis dato existiert allerdings noch keine spezifische Analyse paralleler Algorithmen mit dem Fokus der speziellen Eigenschaften einer "Grid"-Infrastruktur. Der spezifische Unterschied liegt in der Heterogenität von Grid-Ressourcen. In "shared nothing"-Architekturen, wie man sie bei klassischen Supercomputern und Cluster- Systemen vorfindet, sind alle Ressourcen wie z.B. Verarbeitungsknoten, Festplatten und Netzwerkverbindungen angesichts ihrer Leistung, Zugriffszeit und Bandbreite üblicherweise gleich (homogen). Im Gegensatz dazu zeigen Grid-Architekturen heterogene Ressourcen mit verschiedenen Leistungseigenschaften. Der herausfordernde Aspekt dieser Arbeit bestand darin aufzuzeigen, wie man das Problem heterogener Ressourcen löst, d.h. diese Ressourcen einerseits zur Leistungsmaximierung und andererseits zur Definition von Algorithmen einsetzt, um die Arbeitsablauf-Orchestrierung von Datenbankprozessoren zu optimieren. Um dieser Herausforderung gerecht werden zu können, wurde ein mathematisches Modell zur Untersuchung des Leistungsverhaltens paralleler Datenbankoperationen in heterogenen Umgebungen, wie z.B. in Grids, basierend auf generalisierten Multiprozessor- Architekturen entwickelt. Es wurden dabei sowohl die Parameter und deren Einfluss auf die Leistung als auch das Verhalten der Algorithmen in heterogenen Umgebungen beobachtet. Dabei konnte man feststellen, dass kleine Anpassungen an den Algorithmen zur signifikanten Leistungsverbesserung heterogener Umgebungen führen. Weiters wurde eine graphische Darstellung der Knotenkonfiguration entwickelt und ein optimierter Algorithmus, mit dem ein optimaler Knoten zur Ausführung von Datenbankoperationen gefunden werden kann. Diese Ergebnisse zum neuen Algorithmus wurden durch die Implementierung in einer serviceorientierten Architektur (SODA) bestätigt. Durch diese Implementierung konnte die Gültigkeit des Modells und des neu entwickelten optimierten Algorithmus nachgewiesen werden. In dieser Arbeit werden auch die Möglichkeiten für eine brauchbare Erweiterung des vorgestellten Modells gezeigt, wie z.B. für den Einsatz von Leistungskennziffern für Algorithmen zur Findung optimaler Knoten, die Verlässlichkeit der Knoten oder Vorgehensweisen/Lösungsaufgaben zur dynamischen Optimierung von Arbeitsabläufen.In contrast to the traditional notion of a supercomputer, which has many processors connected by a local high-speed computer bus, heterogeneous computing environments rely on "complete" computer nodes (CPU, storage, network interface, etc.) connected to a private or public network by a conventional network interface. Computer networking has evolved over the past three decades, and, like many technologies, has grown exponentially in terms of performance, functionality and reliability. At the beginning of the twenty-first century, high-speed, highly reliable Internet connectivity has become as commonplace as electricity, and computing resources have become as standard in terms of availability and universal use as electrical power. To use heterogeneous Grids for various applications requiring high-processing power, researchers propose the notion of computational Grids where rules are defined relating to both services and hiding the complexity of the Grid organization from the users. Thus, users would find it as easy to use as electrical power. Generally, there is no widely accepted definition of Grids. Some researchers define it as a high-performance distributed environment. Some take into consideration its geographically distributed, multi-domain feature. Others define Grids based on the number of resources they unify. Parallel database systems gained an important role in database research over the past two decades due to the necessity of handling large distributed datasets for scientific computing such as bioinformatics, fluid dynamics and high energy physics (HEP). This was connected with the shift from the (actually failed) development of highly specialized database machines to the usage of conventional parallel hardware architectures. Generally, concurrent execution is employed either by database operator or data parallelism. The first is achieved through parallel execution of a partitioned query execution plan by different operators, while the latter is achieved through parallel execution of the same operation on the partitioned data among multiple processors. Parallel database operation algorithms have been well analyzed for sequential processors. A number of publications have covered this topic which proposed and analyzed these algorithms for parallel database machines. Until now, to the best knowledge of the author, no specific analysis has been done so far on parallel algorithms with a focus on the specific characteristics of a Grid infrastructure. The specific difference lies in the heterogeneous nature of Grid resources. In a "shared nothing architecture", which can be found in classical supercomputers and cluster systems, all resources such as processing nodes, disks and network interconnection have typically homogeneous characteristics as regards to performance, access time and bandwidth. In contrast, in a Grid architecture heterogeneous resources are found that show different performance characteristics. The challenge of this research is to discover the way how to cope with or to exploit this situation to maximize performance and to define algorithms that lead to a solution for an optimized workflow orchestration. To address this challenge, we developed a mathematical model to investigate the performance behavior of parallel database operations in heterogeneous environments, such as a Grid, based on generalized multiprocessor architecture. We also studied the parameters and their influence on the performance as well as the behavior of the algorithms in heterogeneous environments. We discovered that only a small adjustment on the algorithm is necessary to significantly improve the performance for heterogeneous environments. A graphical representation of the node configuration and an optimized algorithm for finding the optimal node configuration for the execution of the parallel binary merge sort have been developed. Finally, we have proved our findings of the new algorithm by implementing it on a service-orientated infrastructure (SODA). The model and our new developed modified algorithms have been verified with the implementation. We also give an outlook of useful extensions to our model e.g. using performance indices, reliability of the nodes and approaches for dynamic optimization of workflow

34th Midwest Symposium on Circuits and Systems-Final Program

Organized by the Naval Postgraduate School Monterey California. Cosponsored by the IEEE Circuits and Systems Society. Symposium Organizing Committee: General Chairman-Sherif Michael, Technical Program-Roberto Cristi, Publications-Michael Soderstrand, Special Sessions- Charles W. Therrien, Publicity: Jeffrey Burl, Finance: Ralph Hippenstiel, and Local Arrangements: Barbara Cristi