As Data Grids become more commonplace, large data sets are being replicated and distributed to multiple sites, leading to the problem of determining which replica can be accessed most efficiently. The answer to this question can depend on many factors, including physical characteristics of the resources and the load behavior on the CPUs, networks, and storage devices that are part of the end-to-end path linking possible sources and sinks.

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The POEMS project is creating an environment for end-to-end performance modeling of complex parallel and distributed systems, spanning the domains of application software, runtime and operating system software, and hardware architecture. Towards this end, the POEMS framework supports composition of component models from these different domains into an end-to-end system model. This composition can be specified using a generalized graph model of a parallel system, together with interface specifications that carry information about component behaviors and evaluation methods. The POEMS Specification Language compiler, under development, will generate an end-to-end system model automatically from such a specification. The components of the target system may be modeled using different modeling paradigms (analysis, simulation, or direct measurement) and may be modeled at various levels of detail. As a result, evaluation of a POEMS end-to-end system model may require a variety of eval...

Parallel algorithm designers need computational models that take first order system costs into account, but are also simple enough to use in practice. This paper introduces the LoPC model, which is inspired by the LogP model but accounts for contention for message processing resources in parallel algorithms on a multiprocessor or network of workstations. LoPC takes the , and parameters directly from the LogP model and uses them to predict the cost of contention, .

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The increasingly common practice of replicating datasets and using resources as distributed data stores in Grid environments has led to the problem of determining which replica can be accessed most efficiently. Because of diverse performance characteristics and load variations of several components in the end-to-end path linking these various locations, selecting a replica from among many requires accurate prediction information of the data transfer times between the sources and sinks. In this paper we present a prediction system that is based on combining end-to-end application throughput observations and network load variations, capturing wholesystem performance and variations in load patterns, respectively. We develop a set of regression models to derive predictions that characterize the effect of network load variations on file transfer times. We apply these techniques to the GridFTP data movement tool, part of the Globus Toolkit™, and observe performance gains of up to 10 % in prediction accuracy when compared with approaches based on past system behavior in isolation.

The POEMS project is creating an environment for endto -end performance modeling of complex parallel and distributed systems, spanning the domains of application software, runtime and operating system software, and hardware architecture. To enable end-to-end modeling of large-scale applications and systems, the POEMS framework is designed to compose models of system components from these different domains, to integrate multiple modeling paradigms (analytical modeling, simulation, and actual system execution), and to allow different components to be modeled at multiple levels of detail. The key components of the POEMS framework include a generalized task graph model for describing parallel computations, automatic generation of the task graph by a parallelizing compiler, a specification language for mapping the computation on models for operating system and hardware components, a library of analytical and simulation models for components from the different domains, and a knowledge base d...

In this paper, we consider analytical techniques for predicting detailed performance characteristics of a single shared memory parallel program for a particular input. Analytical models for parallel programs have been successful at providing simple qualitative insights and bounds on program scalability, but have been less successful in practice for providing detailed insights and metrics for program performance (leaving these to measurement or simulation). We develop a conceptually simple modeling technique called deterministic task graph analysis that provides detailed performance prediction for shared-memory programs with arbitrary task graphs, a wide variety of task scheduling policies, and significant communication and resource contention. Unlike many previous models that are stochastic models, our model assumes deterministic task execution times (while retaining the use of stochastic models for communication and resource contention). This assumption is supported by a previous study of the influence of non-deterministic delays in parallel programs. We evaluate our model in three ways. First, an experimental evaluation shows that our analysis technique is accurate and efficient for a variety of shared-memory programs, including programs with large and/or complex task graphs, sophisticated task scheduling, highly non-uniform task

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