Proceedings 11th IEEE International Symposium on High Performance Distributed Computing

Performance models provide significant insight into the performance relationships between an application and the system used for execution. The major obstacle to developing performance models is the lack of knowledge about the performance relationships between the different functions that compose an application. This paper addresses the issue by using a coupling parameter, which quantifies the interaction between kernels, to develop performance predictions. The results, using three NAS parallel application benchmarks, indicate that the predictions using the coupling parameter were greatly improved over a traditional technique of summing the execution times of the individual kernels in an application. In one case the coupling predictor had less than 1% relative error in contrast the summation methodology that had over 20% relative error. Further, as the problem size and number of processors scale, the coupling values go through a finite number of major value changes that is dependent on the memory subsystem of the processor architecture.

In this paper, we argue that an exclusively inactive interface to directory services can hinder server scalability and indirectly restrict the behavior of potential applications. We propose to extend directory services' interfaces with a proactive mode by which clients can express their interest in (and be notified of) changes in the environment. These notification channels can be subsequently customized on a per-client basis through client-specified filters. Finally, in order to simplify the handling of client/server failures we adopt a leasing model for client registration to (and customization of) a notification channel. To validate our approach, we have designed and implemented the Proactive Directory Service (PDS).

While distributed, heterogeneous collections of computers ("Grids") can in principle be used as a computing platform, in practice the problems of first discovering and then organizing resources to meet application requirements are difficult. We present a general-purpose resource selection framework that addresses these problems by defining a resource selection service for locating Grid resources that match application requirements. At the heart of this framework is a simple, but powerful, declarative language based on a technique called set matching, which extends the Condor matchmaking framework to support both single-resource and multiple-resource selection. This framework also provides an open interface for loading application-specific mapping modules to personalize the resource selector. We present results obtained when this framework is applied in the context of a computational astrophysics application, Cactus. These results demonstrate the effectiveness of our technique.

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Contemporary computing systems, especially large-scale systems such as Grids promise ultra-fast ubiquitous utility computing, always available at the flip of a switch. A major unresolved issue is the organization and efficient usage of such infrastructure in a commercial context where several entities compete for shared resources. This has long been resolved for conventional utility resources such as gas and electricity through commoditization, a variety of market designs, customization, and decision support for the resulting portfolios of assets and commitments. The paper reviews the state of Grid commercialization and compares it to the commercialization of conventional resources. We draw specific lessons for commercialized Grids and detail them as architecture requirements at each level of the architecture stack. We provide an example to illustrate the benefits of commercialized resources in terms of the financial clarity it brings to decisions for different user groups, namely application users and IT managers.

In this paper we present a novel methodology for improving the performance and dependability of application-level messaging in Grid systems. Based on the Network Weather Service, our system uses nonparametric statistical forecasts of request-response times to automatically determine message timeouts. By choosing a timeout based on predicted network performance, the methodology improves application and Grid service performance as extraneous and overly-long timeouts are avoided. We describe the technique, the additional execution and programming overhead it introduces, and demonstrate the effectiveness using a wide-area test application.

Computational grids provide mechanisms for sharing and accessing large and heterogeneous collections of remote resources such as computers, online instruments, storage space, data, and applications. Resources are requested by specifying a set of desired attributes. Resource attributes have various degrees of dynamism, from mostly static attributes, such as operating system version, to highly dynamic ones, such as available network bandwidth or CPU load. Another dimension of dynamism is introduced by variable and highly diverse sharing policies: resources are made available to the grid community based on locally defined and potentially changing policies.

Summary form only given. AURA (Advanced Uncertain Reasoning Architecture) is a generic family of techniques and implementations intended for high-speed approximate search and match operations on large unstructured datasets. AURA technology is fast, economical, and offers unique advantages for finding near-matches not available with other methods. AURA is based upon a high-performance binary neural network called a correlation matrix memory (CMM). Typically, several CMM elements are used in combination to solve soft or fuzzy pattern-matching problems. AURA takes large volumes of data and constructs a special type of compressed index. AURA finds exact and near-matches between indexed records and a given query, where the query itself may have omissions and errors. The degree of nearness required during matching can be varied through thresholding techniques. The PCI-based PRESENCE (Parallel Structured Neural Computing Engine) card is a hardware-accelerator architecture for the core CMM computations needed in AURA-based applications. The card is designed for use in low-cost workstations and incorporates 128 MByte of low-cost DRAM for CMM storage. To investigate the scalability of the distributed AURA system, we implement a word-to-document index of an AURA-based information retrieval system, called MinerTaur, over a distributed PRESENCE CMM.

Presents the design of the Coven framework for construction of problem solving environments (PSEs) for parallel computers. PSEs are an integral part of modern high performance computing (HPC) and Coven attempts to simplify PSE construction. Coven targets Beowulf cluster parallel computers but independent of any particular domain for the PSE. Multithreaded parallel applications are created with Coven that are capable of supporting most of the constructs in a typical parallel programming language. Coven uses an agent-based front-end which allows multiple custom interfaces to be constructed. Examples of the use of Coven in the construction of prototype PSEs are shown, and the effectiveness of these PSEs is evaluated in terms of the performance of the applications they generate.

With the advent of Grid computing, scheduling strategies for distributed heterogeneous systems have either become irrelevant or have to be extended significantly to support Grid dynamics. In this paper, we describe a metascheduling architecture for a Grid system that takes into account both the application and system level considerations. Results are presented to demonstrate the usefulness of the metascheduler.