High performance cDNA sequence analysis using grid technology

Trombetti, Gabriele; Merelli, Ivan; Milanesi, Luciano

doi:10.1016/j.jpdc.2006.08.009

Cited by 5 publications

(3 citation statements)

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“…A parallel implementation of BLAST (http://www.mpiblast.org/) is available and some experiments demonstrate a sensible performance enhancement in grid environment (Trombetti et al 2006).…”

Section: Discovery Methodsmentioning

confidence: 99%

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Evaluation of computer methods for biomarker discovery on computational grids

Tonini¹,

Siqueira²

2013

Electron. J. Biotechnol.

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Background: Discovering biomarkers is a fundamental step to understand and deal with genetic diseases. Methods using classic Computer Science algorithms have been adapted in order to support processing large biological data sets, aiming to find useful information to understand causing conditions of diseases such as cancer. Results: This paper describes some promising biomarker discovery methods based on several grid architectures. Each technique has some features that make it more suitable for a particular grid architecture. This matching depends on the parallelizing capabilities of the method and the resource availability in each processing/storage node. Conclusion: The study described in this paper analyzed the performance of biomarker discovery methods in different grid architectures. We have found some methods are more suited for certain grid architectures, resulting in significant performance improvement and producing more accurate results.

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“…A parallel implementation of BLAST (http://www.mpiblast.org/) is available and some experiments demonstrate a sensible performance enhancement in grid environment (Trombetti et al 2006).…”

Section: Discovery Methodsmentioning

confidence: 99%

“…Unlike recursive methods, alignment algorithms, such as BLAST can be divided in different ways, as can be seen in Trombetti et al (2006). One experiment got 19 times of processing time improvement.…”

Section: Perfomance Analysismentioning

confidence: 99%

Evaluation of computer methods for biomarker discovery on computational grids

Tonini¹,

Siqueira²

2013

Electron. J. Biotechnol.

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“…Existe uma grande classe de aplicações que necessitam de um alto poder computacional e trabalham com grandes quantidades de dados. Estas aplicações incluem o seqüenciamento de genes [115,133], enovelamento de proteínas [93,134], análise de sinais (ex: SETI [7]), análises financeiras [79], mineração de dados [47], física de partículas [13] e simulações em engenharia. Mas cientistas, pesquisadores, analistas e engenheiros muitas vezes não têm acesso a uma infra-estrutura computacional que lhes permita a execução destas aplicações, normalmente por trabalharem em instituições que dispõem de recursos limitados.…”

Section: Capítulo 1 Introduçãounclassified

"Armazenamento distribuído de dados e checkpointing de aplicações paralelas em grades oportunistas"

Camargo

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Opportunistic computational grids use idle resources from shared machines to execute applications that need large amounts of computational power and/or deal with large amounts of data. But executing computationally intensive parallel applications in dynamic and heterogeneous environments, such as opportunistic grids, is a daunting task. Machines may fail, become inaccessible, or change from idle to occupied unexpectedly, compromising the application execution. A fault tolerance mechanism that supports heterogeneous architectures is an important requisite for such systems. In this work, we analyze, implement and evaluate a checkpointing-based fault tolerance mechanism for parallel applications running on opportunistic grids. The mechanism monitors application execution and allows the migration of applications between heterogeneous nodes of the grid. But besides application execution, it is necessary to manage data generated and used by those applications. We want a low cost data storage infrastructure that utilizes the unused disk space of grid shared machines. The system should use the machines to store and recover data only during their idle periods, requiring the system to be redundant and fault-tolerant. To solve the data storage problem in opportunistic grids, we designed, implemented and evaluated the OppStore middleware. This middleware provides reliable distributed storage for application data, which can be accessed from any machine in the grid. The machines are organized in clusters, connected by a self-organizing and fault-tolerant peer-to-peer network. During storage, data is codified into redundant fragments, allowing the reconstruction of the original file using only a subset of those fragments. Finally, to deal with resource heterogeneity, we developed an extension to the Pastry peer-to-peer routing substrate, enabling heterogeneity-aware load-balancing message routing.

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