Parallel Smith-Waterman Comparison on Multicore and Manycore Computing Platforms with BSP++

Hamidouche, Khaled; Mendonca, Fernando Machado; Falcou, Joel; Melo, Alba Cristina Magalhães Alves de; Etiemble, Daniel

doi:10.1007/s10766-012-0209-6

Cited by 13 publications

(6 citation statements)

References 22 publications

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“…If you are thinking of [17,29] or [18], that is surely not the case. Mostly because only memory blocks are communicated and they dot not have particular meaning.…”

Section: Related Workmentioning

confidence: 99%

Axiomatization and Imperative Characterization of Multi-BSP Algorithms: A Q&A on a Partial Solution

Gava

Marquer

2020

Int J Parallel Prog

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multi-bsp is a new bridging model which takes into account hierarchical architectures. We discuss two questions about multi-bsp algorithms and their programming: (1) How do we get a formal characterization of the class of multi-bsp algorithms? (2) How can a programming language be proven algorithmically complete for such a class? Our solution is based on an extension of a bsp version of sequential abstract state machines (asms).

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“…If you are thinking of [17,29] or [18], that is surely not the case. Mostly because only memory blocks are communicated and they dot not have particular meaning.…”

Section: Related Workmentioning

confidence: 99%

Axiomatization and Imperative Characterization of Multi-BSP Algorithms: A Q&A on a Partial Solution

Gava

Marquer

2020

Int J Parallel Prog

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“…Both scenarios have been parallelized in the literature [ 26 , 27 ], but fine-grained parallelism applies better to the first scenario due to the amount of data and computation involved, and therefore fits better into many-core platforms. Among them, we find Intel Xeon Phis [ 28 ], Nvidia GPUs using CUDA [ 29 ], and even multi-GPU using CUDAlign 4.0 [ 30 ], which is our departure point to analyze cost, performance and power efficiency along this work.…”

Section: Related Workmentioning

confidence: 99%

DNA sequences alignment in multi-GPUs: acceleration and energy payoff

et al. 2018

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BackgroundWe present a performance per watt analysis of CUDAlign 4.0, a parallel strategy to obtain the optimal pairwise alignment of huge DNA sequences in multi-GPU platforms using the exact Smith-Waterman method.ResultsOur study includes acceleration factors, performance, scalability, power efficiency and energy costs. We also quantify the influence of the contents of the compared sequences, identify potential scenarios for energy savings on speculative executions, and calculate performance and energy usage differences among distinct GPU generations and models. For a sequence alignment on chromosome-wide scale (around 2 Petacells), we are able to reduce execution times from 9.5 h on a Kepler GPU to just 2.5 h on a Pascal counterpart, with energy costs cut by 60%.ConclusionsWe find GPUs to be an order of magnitude ahead in performance per watt compared to Xeon Phis. Finally, versus typical low-power devices like FPGAs, GPUs keep similar GFLOPS/w ratios in 2017 on a five times faster execution.

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“…Another direction of research, also aimed at speeding sequence alignment, has been the development of parallel algorithms. Parallel algorithms for sequence alignment may be found in [11]- [19], for example.…”

Section: Zhao Et Al / Advances In Sciencementioning

confidence: 99%