M. D. Beynon, A. Sussman, U. Catalyurek, T. Kurc, and J. Saltz. Performance optimization for data intensive grid applications. In Proceedings of the Third Annual International Workshop on Active Middleware Services (AMS2001.  Home/Search   Document Not in Database   Summary   Related Articles   Check

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M. D. Beynon, A. Sussman, U. Catalyurek, T. Kurc, and J. Saltz. Performance optimization for data intensive grid applications. In Proceedings of the Third Annual International Workshop on Active Middleware Services (AMS2001.

....containing useful data, and the maximum size of the buffer. In the current prototype implementation we use TCP for stream communication, but any point to point communication library could be added. DataCutter provides several degrees of flexibility to improve application performance [7] 8] [9]. The choice of placement represents an important degree of freedom in affecting application performance by placing filters with affinity to data sources near the sources, minimizing communication volume on slow links, and placing filters to deal with heterogeneity. Parallelism in executing ....

M. D. Beynon, A. Sussman, U. Catalyurek, T. Kurc, and J. Saltz. Performance optimization for data intensive grid applications. In Proceedings of the Third Annual International Workshop on Active Middleware Services (AMS2001.

....storage systems) offers a powerful and flexible environment for developing and deploying applications that analyze large datasets. Component based frameworks and services have been gaining acceptance as a viable approach for application development and execution in distributed environments [1, 4, 11, 13, 14, 18, 23, 24, 27, 29]. Such models facilitate the implementation of applica p1 p2 p3 (a) b) c) Figure 1. An application server may use many different parallel configurations depending on what is most efficient for an application. a) shared memory, b) distributed shared memory, or (c) distributed ....

.... (GrADS) and support mechanisms for storing metainformation needed to control program execution the Grid Information System (GIS) Several research projects have investigated the design, implementation, and application of component based frameworks for application development and deployment [1, 11, 13, 14, 24, 27, 29]. The CommonComponent Architecture Project (CCA) 17] leads a standardization effort for building distributed software component systems for scientific and engineering applications. The Open Grid Services Architectures (OGSA) effort [21] draws from concepts and technologies that evolved ....

M. D. Beynon, A. Sussman, U. Catalyurek, T. Kurc, and J. Saltz. Performance optimization for data intensive grid applications. In Proceedings of the Third Annual International Workshop on Active Middleware Services (AMS2001.

....or (c) distributed memory. ever, such an environment requires distributed access to and processing of data in a heterogeneous setting. Componentbased frameworks and services have been gaining acceptance as a viable approach for application development and execution in distributed environments [1, 4, 9, 11, 12, 16, 19, 20, 23, 25]. Such models facilitate the implementations of applications and services that can accomodate the heterogeneous and dynamic nature of the Grid. In previous work [6] we have developed a framework for efficiently executing multiple query workloads from data analysis applications on SMP machines ....

.... (GRADS) and the support mechanism for storing metainformation needed to control that execution Grid Information System (GIS) Several research projects have investigated the design, implementation, and application of component based frameworks for application development and deployment [1, 9, 11, 12, 20, 23, 25]. The Common Component Architecture project by Bramley et al. 15] leads a standardization effort for building distributed software component systems for scientific and engineering applications. The Open Grid Services Architectures effort by Foster et al. 18] draws from concepts and technologies ....

M.D. Beynon, A. Sussman, U. Catalyurek, T. Kurc, and J. Saltz. Performance optimization for data intensive grid applications. In Proceedings of the Third Annual International Workshop on Active Middleware Senices (AMS2001.

....which we refer to as filters. The goal is to achieve performance improvements by providing the flexibility to (1) place components among storage and compute nodes in a system [11] and (2) instantiate and run multiple copies of a group of components or copies of individual components in parallel [13]. The middleware we have developed 19 provides two core services: an indexing service for subsetting of datasets via range queries, and a filtering service for instantiating and executing application components. In the following sections we briefly describe the framework and middleware, and ....

....to allow release of allocated resources such as scratch space. When a work cycle is completed, these interface functions may be called again by the runtime system to process another unit of work. 4. 3 Parallel Filters Parallel filters target reduction operations in a distributed environment [13]. A reduction operation can be realized by a filter group that implements transformation, mapping, and aggregation operations and encapsulates the accumulator data structure. We are developing support for parallel filters from two classes, 1 filter and n filter, which are differentiated based on ....

M. D. Beynon, A. Sussman, U. Catalyurek, T. Kurc, and J. Saltz. Performance optimization for data intensive grid applications. In Proceedings of the Third Annual International Workshop on Active Middleware Services (AMS

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Beynon, M. D., Sussman, A., Catalyurek, U., Kurc, T., and Saltz, J. Performance Optimization for Data Intensive Grid Applications. 2001.

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