Scalability Analysis of Progressive Alignment on a Multicore

Isaza, Sebastian; Sánchez, Friman; Gaydadjiev, Georgi; Ramírez, Alex; Valero, Mateo

doi:10.1109/cisis.2010.149

Cited by 6 publications

(4 citation statements)

References 14 publications

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“…The main performance bottleneck is the long computation time needed for each PW alignment because of the use of sequential codes running on a rather old processor (Pentium III). More recently, other works [52,53,80,93] have studied ClustalW's performance on the Cell BE [55]. In [93] ClustalW is parallelized for Cell BE.…”

Section: Clustalw Implementationsmentioning

confidence: 99%

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◾ Multicore Architectures

Levesque¹

2010

High Performance Computing

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In this dissertation, we address the challenges of performance scaling for bioinformatics applications on multicore architectures. In particular, we focus on sequence alignment, one of the fundamental tasks in bioinformatics. Due to the exponential growth of biological databases and the computational complexity of the algorithms used, high performance computing systems are required. Recently, computer architecture has shifted towards the multicore paradigm in an attempt to sustain the performance scalability that single-core processors did provide in the past. Although multicore architectures have the potential of exploiting the task-level and data-level parallelism found in bioinformatics workloads, efficiently harnessing systems with hundreds of cores requires deep understanding of the applications and the architecture specifics. This thesis presents a study of two sequence alignment applications that are modeled, characterized, mapped and optimized targeting two multicore architectures. More precisely, we use the Cell BE and the SARC architectures, the latter developed within the project this thesis was part of. The targeted applications, i.e., HMMER and ClustalW, are used for pairwise alignment and multiple sequence alignment. We first propose an analytical model to predict the performance of applications parallelized under the master-worker scheme. We use HMMER and the Cell BE processor for our experimental case study and for validation of our model. Results show the high accuracy of the model and the scaling behavior of the application phases. Next we investigate the optimal mapping of ClustalW on the Cell BE, identify a number of limitations in the architecture and propose few instruction-set extensions to accelerate the main ClustalW kernel. Last, we study ClustalW and HMMER scalability on the SARC architecture with up to one thousand cores. We also investigate the impact of different input types on ClustalW performance.

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Section: Clustalw Implementationsmentioning

confidence: 99%

“…Various inputs with different characteristics are used in our simulations (see Table 6.1). First, we take the three input data sets (A, B and C) provided in BioPerf [22] that have been used in some of the related work [52,53,80,93].…”

Section: Clustalwmentioning

confidence: 99%

◾ Multicore Architectures

Levesque¹

2010

High Performance Computing

View full text Add to dashboard Cite

show abstract

Section: Clustalw Implementationsmentioning

confidence: 99%

“…First, we take the three input data sets (A, B and C) provided in BioPerf [22] that have been used in some of the related work [52,53,80,93]. Although not clearly stated by the BioPerf authors, the sequences seem to be randomly selected from the databases.…”

Section: Clustalwmentioning

confidence: 99%