Proposal of Smith-Waterman algorithm on FPGA to accelerate the forward and backtracking steps

Oliveira, Fabio Fonseca de; Dias, Leonardo Alves; Fernandes, Marcelo A. C.

doi:10.1371/journal.pone.0254736

Cited by 3 publications

(2 citation statements)

References 58 publications

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“…9. This is 23x higher than OSWALD [17] – the most recent FPGA-based implementation for SW protein database scanning [34].…”

Section: Resultsmentioning

confidence: 99%

CUDASW++4.0: Ultra-fast GPU-based Smith-Waterman Protein Sequence Database Search

Schmidt,

Kallenborn,

Chacon

et al. 2023

Preprint

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Background: The maximal sensitivity for local pairwise alignment makes the Smith-Waterman algorithm a popular choice forprotein sequence database search. However, its quadratic time complexity makes it compute-intensive. Unfortunately, current state-of-the-art software tools are not able to leverage the massively parallel processing capabilities of modern GPUs with close-to-peak performance. This motivates the need for more efficient implementations. Results: CUDASW++4.0 is a fast software tool for scanning protein sequence databases with the Smith-Waterman algorithm on CUDA-enabled GPUs. Our approach achieves high efficiency for dynamic programming-based alignment computation by minimizing memory accesses and instructions. We provide both efficient matrixtiling and sequence database partitioning schemes, and exploit next generation floating point arithmetic and novel DPX instructions. This leads to close-to-peak performance on modern GPU generations (Ampere, Ada, Hopper) with throughput rates of up to 1.94 TCUPS, 5.01 TCUPS, 5.71 TCUPS on an A100, L40S, and H100, respectively. Evaluation on the Swiss-Prot, UniRef50, and TrEMBL databases shows that CUDASW++4.0 gains over an order-of-magnitude performance improvements over previous GPU-based approaches (CUDASW++3.0, ADEPT). In addition, our algorithm demonstrates significant speedups over top-performing CPU-based tools (BLASTP, SWIPE, SWIMM2.0), can exploit multi-GPU nodes with linear scaling, and features an impressive energy efficiency of up to 15.7 GCUPS/Watt. Conclusion: CUDASW++4.0 changes the standing of GPUs in protein sequence database search with Smith-Waterman alignment by providing close-to-peak performance on modern GPUs. It is freely available at https://github.com/asbschmidt/CUDASW4.

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“…9. This is 23x higher than OSWALD [17] – the most recent FPGA-based implementation for SW protein database scanning [34].…”

Section: Resultsmentioning

confidence: 99%

CUDASW++4.0: Ultra-fast GPU-based Smith-Waterman Protein Sequence Database Search

Schmidt,

Kallenborn,

Chacon

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…These studies also serve as the basis for other applications. The tertiary analysis encompasses a variety of applications, including genomic identification and the development of a vaccine or medication [17]. The NN extracts the feature of the user for generating a rating matrix.…”

Section: Introductionmentioning

confidence: 99%

Enhanced Plagiarism Detection Through Advanced Natural Language Processing and E-BERT Framework of the Smith-Waterman Algorithm

Antonius,

Orosoo,

et al. 2023

IJACSA

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Effective detection has been extremely difficult due to plagiarism's pervasiveness throughout a variety of fields, including academia and research. Increasingly complex plagiarism detection strategies are being used by people, making traditional approaches ineffective. The assessment of plagiarism involves a comprehensive examination encompassing syntactic, lexical, semantic, and structural facets. In contrast to traditional string-matching techniques, this investigation adopts a sophisticated Natural Language Processing (NLP) framework. The preprocessing phase entails a series of intricate steps ultimately refining the raw text data. The crux of this methodology lies in the integration of two distinct metrics within the Encoder Representation from Transformers (E-BERT) approach, effectively facilitating a granular exploration of textual similarity. Within the realm of NLP, the amalgamation of Deep and Shallow approaches serves as a lens to delve into the intricate nuances of the text, uncovering underlying layers of meaning. The discerning outcomes of this research unveil the remarkable proficiency of Deep NLP in promptly identifying substantial revisions. Integral to this innovation is the novel utilization of the Waterman algorithm and an English-Spanish dictionary, which contribute to the selection of optimal attributes. Comparative evaluations against alternative models employing distinct encoding methodologies, along with logistic regression as a classifier underscore the potency of the proposed implementation. The culmination of extensive experimentation substantiates the system's prowess, boasting an impressive 99.5% accuracy rate in extracting instances of plagiarism. This research serves as a pivotal advancement in the domain of plagiarism detection, ushering in effective and sophisticated methods to combat the growing spectre of unoriginal content.

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CUDASW++4.0: ultra-fast GPU-based Smith–Waterman protein sequence database search

Schmidt,

Kallenborn,

Chacon

et al. 2024

BMC Bioinformatics

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Background The maximal sensitivity for local pairwise alignment makes the Smith-Waterman algorithm a popular choice for protein sequence database search. However, its quadratic time complexity makes it compute-intensive. Unfortunately, current state-of-the-art software tools are not able to leverage the massively parallel processing capabilities of modern GPUs with close-to-peak performance. This motivates the need for more efficient implementations. Results CUDASW++4.0 is a fast software tool for scanning protein sequence databases with the Smith-Waterman algorithm on CUDA-enabled GPUs. Our approach achieves high efficiency for dynamic programming-based alignment computation by minimizing memory accesses and instructions. We provide both efficient matrix tiling, and sequence database partitioning schemes, and exploit next generation floating point arithmetic and novel DPX instructions. This leads to close-to-peak performance on modern GPU generations (Ampere, Ada, Hopper) with throughput rates of up to 1.94 TCUPS, 5.01 TCUPS, 5.71 TCUPS on an A100, L40S, and H100, respectively. Evaluation on the Swiss-Prot, UniRef50, and TrEMBL databases shows that CUDASW++4.0 gains over an order-of-magnitude performance improvements over previous GPU-based approaches (CUDASW++3.0, ADEPT, SW#DB). In addition, our algorithm demonstrates significant speedups over top-performing CPU-based tools (BLASTP, SWIPE, SWIMM2.0), can exploit multi-GPU nodes with linear scaling, and features an impressive energy efficiency of up to 15.7 GCUPS/Watt. Conclusion CUDASW++4.0 changes the standing of GPUs in protein sequence database search with Smith-Waterman alignment by providing close-to-peak performance on modern GPUs. It is freely available at https://github.com/asbschmidt/CUDASW4.

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Proposal of Smith-Waterman algorithm on FPGA to accelerate the forward and backtracking steps

Cited by 3 publications

References 58 publications

CUDASW++4.0: Ultra-fast GPU-based Smith-Waterman Protein Sequence Database Search

CUDASW++4.0: Ultra-fast GPU-based Smith-Waterman Protein Sequence Database Search

Enhanced Plagiarism Detection Through Advanced Natural Language Processing and E-BERT Framework of the Smith-Waterman Algorithm

CUDASW++4.0: ultra-fast GPU-based Smith–Waterman protein sequence database search

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