Comparison of Cloud and Local HPC Approach for MUSCLE-based Multiscale Simulations

Rycerz, Katarzyna; Nowak, Marcin; Pierzchała, P.; Ciepiela, Eryk; Harężlak, Daniel; Bubak, Marian

doi:10.1109/esciencew.2011.21

Cited by 3 publications

(2 citation statements)

References 18 publications

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“…MUSCLE-based multiscale simulations [72] have been presented in the two types of infrastructures: local HPC cluster and Amazon AWS cloud solutions. And presented solution has been integrated with Grid Space virtual laboratory that enables users to develop and execute virtual experiments on the underlying computational and storage resources through its website based interface.…”

Section: Mafft Series:-mentioning

confidence: 99%

A Survey of Multiple Sequence Alignment Parallel Tools.

Aly¹,

Reda²,

Alsalem³

et al. 2016

IJAR

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Motivation: Multiple sequence alignment is a key problem to most bioinformatics applications, from evolutionary studies to prediction of protein structure, molecular function, intermolecular interactions, gene finding and phylogenitic analysis. The last ten years have witnessed a big improvement to existing multiple alignment tools and the development of new ones. Varieties of parallel architectures have been experimented such as supercomputer, cluster, grid, cloud, and multi-core machine for the purpose of reaching the highest level of accuracy and speed. Results: This paper surveys most popular tools to clarify how parallelism accelerates the processing of large biological data set and improve alignment accuracy. It also introduces a comparative study that aims at guiding biologists to choose the appropriate software depending on their requirements and their hardware potentials.

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Section: Mafft Series:-mentioning

confidence: 99%

A Survey of Multiple Sequence Alignment Parallel Tools.

Aly¹,

Reda²,

Alsalem³

et al. 2016

IJAR

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“…The MUSCLE's first parallel attempt was on SMP system then multiscale simulations for HPC cluster and Amazon AWS cloud have been presented [4]. Booth T-Coffee and MUSCLE achieves high accuracy and fast speed, but cannot handle large sized dataset.…”

Section: Related Workmentioning

confidence: 99%

HAMSA: Highly Accelerated Multiple Sequence Aligner

Reda¹,

Al-Neama²,

Ghaleb³

2016

ijacsa

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Abstract-For biologists, the existence of an efficient tool for multiple sequence alignment is essential. This work presents a new parallel aligner called HAMSA. HAMSA is a bioinformatics application designed for highly accelerated alignment of multiple sequences of proteins and DNA/RNA on a multi-core cluster system. The design of HAMSA is based on a combination of our new optimized algorithms proposed recently of vectorization, partitioning, and scheduling. It mainly operates on a distance vector instead of a distance matrix. It accomplishes similarity computations and generates the guide tree in a highly accelerated and accurate manner. HAMSA outperforms MSAProbs with 21.9-fold speedup, and ClustalW-MPI of 11-fold speedup. It can be considered as an essential tool for structure prediction, protein classification, motive finding and drug design studies.

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Performance Monitoring and Analysis System for MUSCLE-based Applications in PL-Grid

Funika

Janczykowski

Dudek

et al. 2011

2011 IEEE Seventh International Conference on E-Science Workshops

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show abstract

Comparison of Cloud and Local HPC Approach for MUSCLE-based Multiscale Simulations

Cited by 3 publications

References 18 publications

A Survey of Multiple Sequence Alignment Parallel Tools.

A Survey of Multiple Sequence Alignment Parallel Tools.

HAMSA: Highly Accelerated Multiple Sequence Aligner

Performance Monitoring and Analysis System for MUSCLE-based Applications in PL-Grid

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