RabbitFX: Efficient Framework for FASTA/Q File Parsing on Modern Multi-Core Platforms

Zhang, Hao; Song, He-Shan; Xu, Xiao–Ming; Chang, Qixin; Wang, Mingkai; Wei, Yanjie; Yin, Zekun; Schmidt, Bertil; Liu, Weiguo

doi:10.1109/tcbb.2022.3219114

Cited by 7 publications

(3 citation statements)

References 25 publications

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“…We classify genome input files as ‘big’ and ‘small’ for balanced task partition across multi-threading sketching. RabbitFX is a single-produce-multiple-consumer sequence parsing tool and supports parsing one genome file with multiple threads ( Zhang et al 2023 ). RabbitKSSD parses relatively large genome files with RabbitFX, using multiple threads per file.…”

Section: Methodsmentioning

confidence: 99%

RabbitKSSD: accelerating genome distance estimation on modern multi-core architectures

Xu,

Yin,

Yan

et al. 2023

Bioinformatics

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Summary We propose RabbitKSSD, a high-speed genome distance estimation tool. Specifically, we leverage load-balanced task partitioning, fast I/O, efficient intermediate result accesses, and high-performance data structures to improve overall efficiency. Our performance evaluation demonstrates that RabbitKSSD achieves speedups ranging from 5.7x to 19.8x over Kssd for the time-consuming sketch generation and distance computation on commonly used workstations. In addition, it significantly outperforms Mash, BinDash, and Dashing2. Moreover, RabbitKSSD can efficiently perform all-vs-all distance computation for all RefSeq complete bacterial genomes (455 GB in FASTA format) in just 2 minutes on a 64-core workstation. Availability RabbitKSSD is available at https://github.com/RabbitBio/RabbitKSSD. Supplementary information Supplementary data are available at Bioinformatics online.

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Section: Methodsmentioning

confidence: 99%

RabbitKSSD: accelerating genome distance estimation on modern multi-core architectures

Xu,

Yin,

Yan

et al. 2023

Bioinformatics

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“…Although libraries for parsing of FASTA files exist [27], this task can still take significant time for reading databases like UniRef or TrEMBL. Thus, CU-DASW++4.0 includes a preprocessor called makeDB (Stage 0) that creates a local database from protein FASTA files.…”

Section: Advanced Architectural Gpu Features For Dpmentioning

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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“…21 Subsequently, G-quadruplex-based DNAzyme networks achieved the dynamic assembly and disassembly of structures through fuel strand activation and enzyme assistance. 22 It can be seen that DNA chemical reaction networks have shown tremendous potential in dynamic control and logical computing and have been widely applied in intelligent biosensing, 23,24 DNA storage, 25,26 information processing, 27 and other applications.…”

Section: Introductionmentioning

confidence: 99%

A nicking enzyme-assisted allosteric strategy for self-resetting DNA switching circuits

Wang,

Zhang,

Liu

et al. 2024

Analyst

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RabbitFX: Efficient Framework for FASTA/Q File Parsing on Modern Multi-Core Platforms

Cited by 7 publications

References 25 publications

RabbitKSSD: accelerating genome distance estimation on modern multi-core architectures

RabbitKSSD: accelerating genome distance estimation on modern multi-core architectures

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

A nicking enzyme-assisted allosteric strategy for self-resetting DNA switching circuits

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