An agent-based solution for dynamic multi-node wavefront balancing in biological sequence comparison

Sandes, Edans F. de O.; Ralha, Célia Ghedini; Melo, Alba Cristina Magalhães Alves de

doi:10.1016/j.eswa.2014.01.030

Cited by 8 publications

(8 citation statements)

References 21 publications

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“…In contrast, in the cases of massive exposure to radiation, hundreds of subjects must be treated as soon as possible. In addition to these approaches, an interesting multi-agent system is also proposed by E. F. de O. Sandes et al in which agents must balance the charge of the different nodes working in parallel [33]. Both this approach and ours treat the system load balancing by means of a multi-agent system (MAS), but in two different manners.…”

Section: Discussion and Related Workmentioning

confidence: 99%

A Self-Adaptable Distributed CBR Version of the Equivox System

Henriet

Lang

Pottayya

et al. 2016

Biomed. Eng. Appl. Basis Commun.

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Three dimensional (3D) voxel phantoms are numerical representations of human bodies, used by physicians in very different contexts. In the controlled context of hospitals, where from 2 to 10 subjects may arrive per day, phantoms are used to verify computations before therapeutic exposure to radiation of cancerous tumors. In addition, 3D phantoms are used to diagnose the gravity of accidental exposure to radiation. In such cases, there may be from 10 to more than 1000 subjects to be diagnosed simultaneously. In all of these cases, computation accuracy depends on a single such representation. In this paper, we present EquiVox which is a tool composed of several distributed functions and enables to create, as quickly and as accurately as possible, 3D numerical phantoms that fit anyone, whatever the context. It is based on a multi-agent system. Agents are convenient for this kind of structure, they can interact together and they may have individual capacities. In EquiVox, the phantoms adaptation is a key phase based on artificial neural network (ANN) interpolations. Thus, ANNs must be trained regularly in order to take into account newly capitalized subjects and to increase interpolation accuracy. However, ANN training is a time-consuming process. Consequently, we have built Equivox to optimize this process. Thus, in this paper, we present our architecture, based on agents and ANN, and we put the stress on the adaptation module. We propose, next, some experimentations in order to show the efficiency of the EquiVox architecture.

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Section: Discussion and Related Workmentioning

confidence: 99%

A Self-Adaptable Distributed CBR Version of the Equivox System

Henriet

Lang

Pottayya

et al. 2016

Biomed. Eng. Appl. Basis Commun.

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“…We also want to produce sub-optimal alignments to increase the alignment coverage of homologous chromosomes, as well as to produce alignments in the reverse complement strands. Additionally, the static column distribution presented in this paper is suited for dedicated environments, but it may be improved with dynamic load balancing [31] for non-dedicated environments.…”

Section: Discussionmentioning

confidence: 99%

CUDAlign 4.0: Incremental Speculative Traceback for Exact Chromosome-Wide Alignment in GPU Clusters

Sandes

Miranda

Martorell

et al. 2016

IEEE Trans. Parallel Distrib. Syst.

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Abstract-This paper proposes and evaluates CUDAlign 4.0, a parallel strategy to obtain the optimal alignment of huge DNA sequences in multi-GPU platforms, using the exact Smith-Waterman (SW) algorithm. In the first phase of CUDAlign 4.0, a huge Dynamic Programming (DP) matrix is computed by multiple GPUs, which asynchronously communicate border elements to the right neighbor in order to find the optimal score. After that, the traceback phase of SW is executed. The efficient parallelization of the traceback phase is very challenging because of the high amount of data dependency, which particularly impacts the performance and limits the application scalability. In order to obtain a multi-GPU highly parallel traceback phase, we propose and evaluate a new parallel traceback algorithm called Incremental Speculative Traceback (IST), which pipelines the traceback phase, speculating incrementally over the values calculated so far, producing results in advance. With CUDAlign 4.0, we were able to calculate SW matrices with up to 60 Peta cells, obtaining the optimal local alignments of all Human and Chimpanzee homologous chromosomes, whose sizes range from 26 Millions of Base Pairs (MBP) up to 249 MBP. As far as we know, this is the first time such comparison was made with the SW exact method. We also show that the IST algorithm is able to reduce the traceback time from 2.15⇥ up to 21.03⇥, when compared with the baseline traceback algorithm. The human⇥chimpanzee chromosome 5 comparison (180 MBP⇥183 MBP) attained 10,370.00 GCUPS (Billions of Cells Updated per Second) using 384 GPUs, with a speculation hit ratio of 98.2%.

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“…• Análise teórica da otimização Block Pruning: a análise matemática do Block Pruning foi feita para identificar quais os aspectos que contribuem ou prejudicam a eficácia deste método; • Método de balanceamento dinâmico de carga [Sandes et al 2014c A Tabela 1 apresenta um resumo com o desempenho máximo obtido nos testes de cada uma das versões. Com os resultados apresentados, conseguiu-se evoluir o estado da arte em dois aspectos: a) o maior desempenho conhecido para alinhamentoótimo de sequências biológicas tornou-se 10,37 TCUPS com 384 GPUs; b) até onde sabemos, a maior sequência alinhada com métodos exatos passou a ser de 249 MBP.…”

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