Performance evaluation of parallel strategies in public clouds: A study with phylogenomic workflows

Oliveira, Daniel de; Ocaña, Kary; Ogasawara, Eduardo; Dias, Jonas; Gonçalves, Juliana; Baião, Fernanda; Mattoso, Marta

doi:10.1016/j.future.2012.12.019

Cited by 30 publications

(15 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…SciDock results provide evidence that large computations involving MD experiments can benefit from SciCumulus in HPC clouds as verified in previous publications [17,[47][48][49].…”

Section: Final Remarks and Future Worksupporting

confidence: 77%

See 1 more Smart Citation

Exploring Large Scale Receptor-Ligand Pairs in Molecular Docking Workflows in HPC Clouds

Ocaña

Benza

Oliveira

et al. 2014

2014 IEEE International Parallel &Amp; Distributed Processing Symposium Workshops

Self Cite

View full text Add to dashboard Cite

Computer-aided drug design techniques are important assets in pharmaceutical industry because of their support for research and development of new drugs. Molecular docking (MD) predicts specific compound's binding modes within the active site of target proteins. Since MD is a timeconsuming process, existing approaches reduce the number of receptors or ligands in docking by evaluating only small sets of compounds. This restriction in the search space reduces the chances to uniformly cover the diverse space of compounds and misses opportunities to recognize whether new drugs can be identified. Another difficulty with large-scale is analyzing the results, e.g. browsing all directories manually to find which pairs were docked successfully. To address these issues we explored the potential of data provenance analysis and parallel processing of SciCumulus, a cloud Scientific Workflow Management System. We present SciDock, a molecular docking-based virtual screening workflow and evaluate its execution using 10,000 receptor-ligand pairs related to proteases enzymes of protozoan genomes. The overall performance of SciDock using 32 cores, in cloud virtual machines, reaches improvements up to 95.4% when running SciDock with AutoDock and 96.1% when running SciDock with Vina. We show how data provenance improved the result analysis and how it may indicate potential proteases drug targets for protozoan treatments.

show abstract

“…SciDock results provide evidence that large computations involving MD experiments can benefit from SciCumulus in HPC clouds as verified in previous publications [17,[47][48][49].…”

Section: Final Remarks and Future Worksupporting

confidence: 77%

“…SciCumulus was our choice since its unique features have already demonstrated to be essential in several HPC bioinformatics workflows [17,[47][48][49].…”

Section: B Using Scicumulus: Benefits and Challengesmentioning

confidence: 99%

Exploring Large Scale Receptor-Ligand Pairs in Molecular Docking Workflows in HPC Clouds

Ocaña

Benza

Oliveira

et al. 2014

2014 IEEE International Parallel &Amp; Distributed Processing Symposium Workshops

Self Cite

View full text Add to dashboard Cite

show abstract

“…The need to utilize cloud computing to run scientific workflows has been widely recognized by the scientific community Zhao et al (2011);Deelman (2015); Zhao et al (2015); de Oliveira et al (2012). Many researchers studied and confirmed the feasibility of using cloud computing for e-science from both cost Deelman et al (2008) and performance perspectives Iosup et al (2011);de Oliveira et al (2013).…”

Section: Related Workmentioning

confidence: 99%

Big Data Workflows: A Reference Architecture and the DATAVIEW System

Kashlev¹,

Lu²,

Mohan³

2017

STBD

View full text Add to dashboard Cite

The big data era is here, a natural result of the digital revolution of the last few decades. The emergence of big data in virtually all areas of life raises a fundamental question -how can we turn large volumes of bits and bytes into insights and possibly values? The answer to this question is often hindered by three big data challenges: volume, velocity, and variety. While scientific workflows have been used extensively in structuring complex scientific data analysis processes, they fall short in meeting the three big data challenges on the one hand, and in leveraging the dynamic resource provisioning capability of cloud computing on the other hand. To address such limitations, we propose and develop the concept of big data workflow as the next generation of data-centric workflow technologies. In this paper we: 1) identify the key challenges for running big data workflows in the cloud; 2) propose a reference architecture for big data workflow management systems (BDWFMSs) that addresses these challenges, 3) develop DATAVIEW, a big data workflow management system, to validate our proposed reference architecture, 4) design and run two big data workflows in the automotive and astronomy domains to showcase applications of our DATAVIEW system.

show abstract

“…Notable examples of bioinformatics workflow execution in the cloud include [11,33] and an interesting review on bioinformatics workflow optimization in the cloud can be found in [15]. In the past few years, there is a significant trend in integrating existing tools into unified platforms featuring an abundance of ready to use tools, with particular emphasis on ease of deployment and efficient use of resources of the cloud.…”

Section: Related Workmentioning

confidence: 99%