Application Driven Grid Developments in the OpenMolGRID Project

Schuller, Bernd; Romberg, Mathilde; Kirtchakova, Lidia

doi:10.1007/11508380_4

Cited by 4 publications

(3 citation statements)

References 3 publications

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“…The workflow engine, called MetaPlugin, supports the user in dealing with complex workflows. It builds UNICORE jobs from XML based workflow descriptions, , where the UNICORE system specific details such as explicit file transfers, task dependencies, and resource allocation are taken care of automatically. Manual data conversions are eliminated by automatically adding suitable data transformation tasks between subsequent tasks that have different output and input formats.…”

Section: Openmolgrid Systemmentioning

confidence: 99%

Open Computing Grid for Molecular Science and Engineering

Sild

Maran

Lomaka

et al. 2006

J. Chem. Inf. Model.

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Grid is an emerging infrastructure for distributed computing that provides secure and scalable mechanisms for discovering and accessing remote software and data resources. Applications built on this infrastructure have great potential for addressing and solving large scale chemical, pharmaceutical, and material science problems. The article describes the concept behind grid computing and will present the OpenMolGRID system that is an open computing grid for molecular science and engineering. This system provides grid enabled components, such as a data warehouse for chemical data, software for building QSPR/QSAR models, and molecular engineering tools for generating compounds with predefined chemical properties or biological activities. The article also provides an overview about the availability of chemical applications in the grid.

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Section: Openmolgrid Systemmentioning

confidence: 99%

Open Computing Grid for Molecular Science and Engineering

Sild

Maran

Lomaka

et al. 2006

J. Chem. Inf. Model.

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“…Interest in modeling of complex systems using molecular dynamics (MD) simulation has increased dramatically [15][16][17], and the parallel implementation makes it possible the fully understanding interesting phenomena and events, which occurs on long timescale and impossible to get from real experiments. During last decade the usage of parallel computational resources and supercomputers leads to the significant progress in bio-systems modeling [18][19][20][21]. Increasing in system dimensions and simulation time became possible with the linear increase of computational resources of distributed computing infrastructures.…”

Section: Introductionmentioning

confidence: 99%

NAMD Package Benchmarking on the Base of Armenian Grid Infrastructure

Poghosyan¹,

Arsenyan²,

Astsatryan³

et al. 2012

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The parallel scaling (parallel performance up to 48 cores) of NAMD package has been investigated by estimation of the sensitivity of interconnection on speedup and benchmark results—testing the parallel performance of Myrinet, Infiniband and Gigabit Ethernet networks. The system of ApoA1 of 92 K atoms, as well as 1000 K, 330 K, 210 K, 110 K, 54 K, 27 K and 16 K has been used as testing systems. The Armenian grid infrastructure (ArmGrid) has been used as a main platform for series of benchmarks. According to the results, due to the high performance of Myrinet and Infiniband networks, the ArmCluster system and the cluster located in the Yerevan State University show reasonable values, meanwhile the scaling of clusters with various types of Gigabit Ethernet interconnections breaks down when interconnection is activated. However, the clusters equipped by Gigabit Ethernet network are sensitive to change of system, particularly for 1000 K systems no breakdown in scaling is observed. The infiniband supports in comparison with Myrinet, make it possible to receive almost ideally results regardless of system size. In addition, a benchmarking formula is suggested, which provides the computational throughput depending on the number of processors. These results should be important, for instance, to choose most appropriate amount of processors for studied system

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“…To facilitate the development of prediction model and prediction workflows, the OpenMolGRID project developed abstraction layers to easily integrate application software and to access different publicly available relevant databases (e.g., ECOTOX [51], NTP [52]), and built a data warehouse from that data [53]. It includes automated workflow support that simplifies the task of the user by including support steps such as data conversion and data transfer into the workflow, by automatically assigning appropriate execution servers, and by exploiting parallelism [54,55].The general architecture underlying the OpenMolGRID system is depicted in Fig. 1.1.…”

mentioning

confidence: 99%