GridBench: a tool for benchmarking grids

Tsouloupas, George; Dikaiakos, Marios D.

doi:10.1109/grid.2003.1261699

Cited by 31 publications

(19 citation statements)

References 14 publications

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“…This multi-layered structure of the Grid suggests that the observed performance of Grid services and applications is affected by a variety of factors, such as [27]: (i) the performance capacity of local Grid sites (this depends on the performance of hardware entities, such as CPUs, memory hierarchies, computing nodes, storage devices, local networking, and clusters); (ii) the performance of the wide-area networks connecting Grid sites into a Grid infrastructure; (iii) the performance and overhead of libraries and services providing Grid applications with support for communication, synchronization, bulk data transfer, database querying, and other higher-level Grid programming abstractions; (iv) the performance and overhead of Grid services supporting job submission and management, such as workload management systems, resource brokers, and Grid information services; and (v) the reliability and the robustness of Grid middleware and services.…”

Section: Performance Representationmentioning

confidence: 99%

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Grid benchmarking: vision, challenges, and current status

Dikaiakos

2006

Concurrency and Computation

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SUMMARYGrid benchmarking is an important and challenging topic of Grid computing research. In this paper, we present an overview of the key challenges that need to be addressed for the integration of benchmarking practices, techniques, and tools in emerging Grid computing infrastructures. We discuss the problems of performance representation, measurement, and interpretation in the context of Grid benchmarking, and propose the use of ontologies for organizing and describing benchmarking metrics. Finally, we present a survey of ongoing research efforts that develop benchmarks and benchmarking tools for the Grid.

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Section: Performance Representationmentioning

confidence: 99%

“…To this end, we need to design benchmarks that investigate the performance behavior of Grid entities belonging to different layers of the Grid architecture, from CPUs and memory hierarchies to whole sites and central Grid services ( Figure 5) [27,29]. In particular, we can use the following.…”

Section: Choosing the Benchmarksmentioning

confidence: 99%

Grid benchmarking: vision, challenges, and current status

Dikaiakos

2006

Concurrency and Computation

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“…Furthermore, in order to have fair evaluation, thus any benchmark would have to account for heterogeneity of resources, presence of virtual organizations and their diverse resource access policies, dynamicity due to inherent shared nature of the Grid. Such issues in turn have led to broader implications upon methodologies used behind evaluating middlewares as discussed in [1,11]. In our work, however, for the sake of simplicity we assume the benchmark are run on Grid nodes in isolation.…”

Section: Introductionmentioning

confidence: 99%

SuperQuant Financial Benchmark Suite for Performance Analysis of Grid Middlewares

Gaikwad

Doan

Bossy

et al. 2012

Modeling, Simulation and Optimization of Complex Processes

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: Pricing and hedging of higher order derivatives such as multidimensional (up to 100 underlying assets) European and first generation exotic options represent mathematically complex and computationally intensive problems. Grid computing promises to give the capability to handle such intense computations. With several Grid middleware solutions available for gridifying traditional applications, it is cumbersome to select an ideal candidate, to develop financial applications, that can cope up with time critical computational demand for complex pricing requests. In this paper we present SuperQuant Financial Benchmark Suite to evaluate and quantify the overhead imposed by a Grid middleware on throughput of the system and turnaround times for computation. This approach is a step towards producing a middleware independent, reproducible, comparable, self-sufficient and fair performance analysis of Grid middlewares. The result of such performance analysis can be used by middleware vendors to find the bottlenecks and problems in their design and implementation of the system and by financial application developers to verify implementation of their financial algorithms. In this paper we explain the motivation and the details of the proposed benchmark suite. As a proof of concept, we utilize the benchmarks in an International Grid Programming contest and demonstrate the result of initial experiments.

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“…One of them is GridBench [10], developed in the High Performance Computing Laboratory (HPCL) of Dept. of Computer Science at University of Cyprus.…”

Section: Fig 3 Horizontal View Of So2 Concentration At 300 Mmentioning

confidence: 99%

Air Pollution Modeling in the CrossGrid Project

Mouriño

Martín

González

et al. 2004

Computational Science - ICCS 2004

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Abstract. The CrossGrid project develops, implements and exploits new Grid components for interactive compute and data intensive applications like simulation and visualization for surgical procedures, flooding crisis team decision support systems, distributed data analysis in highenergy physics, and air pollution combined with weather forecasting. We present in this paper the integration of the air pollution application with other components of the project and show the benefits of using a Grid platform for running this application.

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GridBench: a tool for benchmarking grids

Cited by 31 publications

References 14 publications

Grid benchmarking: vision, challenges, and current status

Grid benchmarking: vision, challenges, and current status

SuperQuant Financial Benchmark Suite for Performance Analysis of Grid Middlewares

Air Pollution Modeling in the CrossGrid Project

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