H. Andrade, T. Kurc, A. Sussman, and J. Saltz. Efficient execution of multiple workloads in data analysis applications. In Proceedingsof the 2001.  Home/Search   Document Not in Database   Summary   Related Articles   Check

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H. Andrade, T. Kurc, A. Sussman, and J. Saltz. Efficient execution of multiple workloads in data analysis applications. In Proceedingsof the 2001.

....use of system resources can be achieved, and the overall performance of the data server can be improved. Multi query optimization is important in many application domains, such as relational databases [6, 14] deductive databases [5] decision support systems [19] and data analysis applications [2]. Several optimizations can be applied to speed up query execution when multi query workloads are presented to the server. Multiple query optimization techniques mainly rely on caching common subexpressions. Carefully scheduling queries also plays an important role, because the execution of ....

....to the server. Multiple query optimization techniques mainly rely on caching common subexpressions. Carefully scheduling queries also plays an important role, because the execution of queries can be ordered in a way to better exploit expressions that have been already cached. In an earlier work [2, 3], we investigated optimizations for multiple query workloads in an application for visualizing digitized microscopy images. Our results show that significant performance improvements can be achieved by data caching and carefully scheduling queries. In this section, we describe an approach for ....

H. Andrade, T. Kurc, A. Sussman, and J. Saltz. Efficient execution of multiple workloads in data analysis applications. In Proceedingsof the 2001.

....of temporary accumulators and extra overhead costs, such as increased memory usage and buffer copies. On the other hand, temporary results can potentially be used by other queries of the same type, and perhaps also by queries of other types, either directly or through ad hoc data transformations [6]. We argue that using primitive operations will often lead to improved system performance, since it exposes many potential optimization sites to the query planner. In the following sections, we elaborate on an approach for functionally decomposing data analysis queries to improve data and ....

....Therefore, we take a bottom up approach in which the high level operators responsible for the query processing chain are described in terms of low level primitives implemented by the application developer. The query processing system uses this information to infer points of reuse. In earlier work [5, 6, 7, 8, 9], we investigated frameworks, query scheduling, and cache replacement issues for data analysis applications. This paper differs from our previous work in that we look at the effect on data reuse of functional decomposition. There are a number of research projects that focus on component based ....

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H. Andrade, T. Kurc, A. Sussman, and J. Saltz. Efficient execution of multiple workloads in data analysis applications. In Proceedings of the 2001.

....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 memory. tions and services that can accommodate the heterogeneous and dynamic nature of the Grid. In previous work [7, 8], we have developed a framework for efficiently executing multiple query workloads from data analysis applications on SMP machines and clusters of distributed memory parallel machines. In this work, building on that framework, we develop a component based framework designed as a suite of services. ....

....integrated into a much larger infrastructure, by being compliant to the models that are going to become protocols, best practices, and, eventually, standards. Finally, for a discussion of the bulk of our work on the multiple query optimization problem, we refer the reader to our previous papers [6, 7, 9], in which we extensively discuss related research and compare it to the approach we have employed. 3 Query Processing and Data Reuse in Data Analysis Applications Although many data analysis applications differ greatly in terms of their input datasets and resulting data products, processing of ....

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H. Andrade, T. Kurc, A. Sussman, and J. Saltz. Efficient execution of multiple workloads in data analysis applications. In Proceedings of the 2001.

.... 1 Introduction Multi query optimization is becoming increasingly important in many application domains, such as relational databases [9, 14, 15, 20, 21, 22, 23] deductive databases [7] decision support systems [27] and data intensive analytical applications (or data analysis applications) [3]. In a collaborative environment, multiple clients may submit queries to the data server. There may be a large number of overlapping regions of interest and common processing requirements among the clients. Several optimizations can be applied to speed up query execution when multi query workloads ....

....to sort the queries for execution. We describe four different ranking strategies. We perform an experimental evaluation of the strategies using two versions of a microscopy visualization application that were deployed in a generic, multithreaded, multiple query workload aware runtime system [3]. Each of these versions of the application has different CPU and I O requirements, creating different application scenarios. We experimentally show how a dynamic query scheduling model that takes into consideration cached results and data transformation opportunities improves system performance ....

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Henrique Andrade, Tahsin Kurc, Alan Sussman, and Joel Saltz. Efficient execution of multiple workloads in data analysis applications. In Proceedings of the 2001.

....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 and distributedmemory parallel machines. In this work, building on that framework, we are developing a component based framework designed as a suite of services. This ....

....have to evolve in order to be integrated to a much larger infrastructure, by being compliant to the models that are eventually become standards, protocols and best practices. Finally, for the whole bulk of work in the multiple query optimization problem, we refer the reader to our previous works [5, 6, 7], in which we extensively discuss the related research and compare it to our own approach. 3 Query Processing and Data Reuse in Data Analysis Applications Although many data analysis applications seemingly differ greatly in terms of their input datasets and resulting data products, processing of ....

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H. Andrade, T. Kurc, A. Sussman, and J. Saltz. Efficient execution of multiple workloads in data analysis applications. In Proceedings of the 2001.

....of low cost storage systems, built from a cluster of PCs with a disk farm, allows many institutions to create data repositories and make them available for collaborative use. As a result, efficient handling of multiple query workloads is an important optimization in many application domains [2, 21, 38]. The query optimization and scheduling problem has been extensively investigated in the relational database community [15] Multiple query optimization techniques for relational databases traditionally rely on caching common subexpressions [25, 30, 33, 37] Nevertheless, deploying these ....

....that should be addressed to optimize use of available resources are (1) effectively scheduling incoming queries and (2) efficient cache replacement policies. We have previously developed an object oriented framework to support efficient utilization of common subexpressions and partial results [2]. The underlying runtime system implements an in memory semantic cache to maintain user defined data structures for intermediate results. In earlier work [4] we addressed the query scheduling problem, and in this paper we evaluate cache replacement policies. We describe the implementation of two ....

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H. Andrade, T. Kurc, A. Sussman, and J. Saltz. Efficient execution of multiple workloads in data analysis applications. In Proceedings of the

....create data repositories and make them available for collaborative use. In a collaborative setting, a data server may need to answer queries simultaneously submitted by multiple clients. Thus, efficient handling of multiple query workloads is an important optimization in many application domains [2, 9, 13]. The query optimization and scheduling problem has been extensively investigated in past surveys [7] Traditionally, multiple query optimization techniques for relational databases rely on caching common subexpressions [11] Cache space is limited by nature, and it is very well possi This ....

....and similar processing requirements (i.e. the same operations on data) Hence, several optimizations can be applied to improve system response time. These optimizations include reuse of intermediate and final results, data prefetching and caching, and scheduling to improve inter query locality [2, 3]. This paper investigates the use of SMP clusters to improve response times and overall system performance. In particular, we look at the effective use of aggregate processing power and I O bandwidth for executing single and multiple queries efficiently. Unlike previous work on query execution in ....

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H. Andrade, T. Kurc, A. Sussman, and J. Saltz. Efficient execution of multiple workloads in data analysis applications. In Proceedingsof the

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