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

Experimental Benchmarks and Initial Evaluation of the Performance of the PASM System Prototype

The work reported here represents experiences with the PASM parallel processing system prototype during its first operational year. Most of the experiments were performed by students in the Fall semester of 1987. The first programming, and the first timing measurements, were made during the summer of 1987 by Sam Fineberg. The goal of the collection of experiments presented here was to undertake an Application-driven Architecture Study of the PASM system as a paradigm for parallel architecture evaluation in general. PASM was an excellent vehicle for experimenting with this evaluation technique due to its unique architectural features. Among these are: 1. A reconfigurable, partitionable multistage circuit-switched network. 2. Support for both SIMD and MIMD programs. 3. Ability to execute hybrid SIMD/MIMD programs. 4. An instruction queue which allows overlap of control-flow and data manipulation between micro-control (MC) units and processing elements (PE). It had been hypothesized that superlinear speed-up over the number of PEs could be attained with this feature, and experimental results verified this. 5. Support for barrier synchronization of MIMD tasks. This feature was exploited in some non-standard ways to show the ability to decouple variant length SIMD instructions into multiple MIMD streams for an overall performance benefit. This type of study is expected to continue in the future on PASM and other parallel machines at Purdue. This report should serve as a guide for this future work as well

An Application Perspective on High-Performance Computing and Communications

We review possible and probable industrial applications of HPCC focusing on the software and hardware issues. Thirty-three separate categories are illustrated by detailed descriptions of five areas -- computational chemistry; Monte Carlo methods from physics to economics; manufacturing; and computational fluid dynamics; command and control; or crisis management; and multimedia services to client computers and settop boxes. The hardware varies from tightly-coupled parallel supercomputers to heterogeneous distributed systems. The software models span HPF and data parallelism, to distributed information systems and object/data flow parallelism on the Web. We find that in each case, it is reasonably clear that HPCC works in principle, and postulate that this knowledge can be used in a new generation of software infrastructure based on the WebWindows approach, and discussed in an accompanying paper

Adaptive Parallel Iterative Deepening Search

Many of the artificial intelligence techniques developed to date rely on heuristic search through large spaces. Unfortunately, the size of these spaces and the corresponding computational effort reduce the applicability of otherwise novel and effective algorithms. A number of parallel and distributed approaches to search have considerably improved the performance of the search process. Our goal is to develop an architecture that automatically selects parallel search strategies for optimal performance on a variety of search problems. In this paper we describe one such architecture realized in the Eureka system, which combines the benefits of many different approaches to parallel heuristic search. Through empirical and theoretical analyses we observe that features of the problem space directly affect the choice of optimal parallel search strategy. We then employ machine learning techniques to select the optimal parallel search strategy for a given problem space. When a new search task is input to the system, Eureka uses features describing the search space and the chosen architecture to automatically select the appropriate search strategy. Eureka has been tested on a MIMD parallel processor, a distributed network of workstations, and a single workstation using multithreading. Results generated from fifteen puzzle problems, robot arm motion problems, artificial search spaces, and planning problems indicate that Eureka outperforms any of the tested strategies used exclusively for all problem instances and is able to greatly reduce the search time for these applications

A Survey of Parallel Data Mining

With the fast, continuous increase in the number and size of databases, parallel data mining is a natural and cost-effective approach to tackle the problem of scalability in data mining. Recently there has been a considerable research on parallel data mining. However, most projects focus on the parallelization of a single kind of data mining algorithm/paradigm. This paper surveys parallel data mining with a broader perspective. More precisely, we discuss the parallelization of data mining algorithms of four knowledge discovery paradigms, namely rule induction, instance-based learning, genetic algorithms and neural networks. Using the lessons learned from this discussion, we also derive a set of heuristic principles for designing efficient parallel data mining algorithms

Mapping of portable parallel programs

An efficient parallel program designed for a parallel architecture includes a detailed outline of accurate assignments of concurrent computations onto processors, and data transfers onto communication links, such that the overall execution time is minimized. This process may be complex depending on the application task and the target multiprocessor architecture. Furthermore, this process is to be repeated for every different architecture even though the application task may be the same. Consequently, this has a major impact on the ever increasing cost of software development for multiprocessor systems. A remedy for this problem would be to design portable parallel programs which can be mapped efficiently onto any computer system. In this dissertation, we present a portable programming tool called Cluster-M. The three components of Cluster-M are the Specification Module, the Representation Module, and the Mapping Module. In the Specification Module, for a given problem, a machine-independent program is generated and represented in the form of a clustered task graph called Spec graph. Similarly, in the Representation Module, for a given architecture or heterogeneous suite of computers, a clustered system graph called Rep graph is generated. The Mapping Module is responsible for efficient mapping of Spec graphs onto Rep graphs. As part of this module, we present the first algorithm which produces a near-optimal mapping of an arbitrary non-uniform machine-independent task graph with M modules, onto an arbitrary non-uniform task-independent system graph having N processors, in 0(M P) time, where P = max(M, N). Our experimental results indicate that Cluster-M produces better or similar mapping results compared to other leading techniques which work only for restricted task or system graphs

An Algorithmic Taxonomy of Production System Machines

This paper presents a survey of computer architectures designed to execute production systems. After a brief description of production systems and production system languages, the paper summarizes match algorithms, particularly the Rete algorithm, and outlines suggested parallelizations. Most parallel production system algorithms have as their unit of sequential computation a single production's left-hand side, activations of a single Rete node, a single activation of a Rete node, or a single comparison in a Rete node. The paper discusses a number of proposed production system machine architectures in terms of the parallel and sequential computations performed in the algorithms suggested for each machine. A taxonomy of parallel production system algorithms, describing in detail the distribution and replication of data and computations, concludes the paper