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Accelerating Genome Analysis: A Primer on an Ongoing Journey [article]

Mohammed Alser, Zülal Bingöl, Damla Senol Cali, Jeremie Kim, Saugata Ghose, Can Alkan, Onur Mutlu
2020 arXiv   pre-print
Genome analysis fundamentally starts with a process known as read mapping, where sequenced fragments of an organism's genome are compared against a reference genome.  ...  Read mapping is currently a major bottleneck in the entire genome analysis pipeline, because state-of-the-art genome sequencing technologies are able to sequence a genome much faster than the computational  ...  ASAP [44] accelerates Levenshtein distance calculation by up to 63.3× using FPGAs compared to its CPU implementation.  ... 
arXiv:2008.00961v2 fatcat:kekhq5ohmng6pdlzayjkqifu64

Parallel computing for genome sequence processing

You Zou, Yuejie Zhu, Yaohang Li, Fang-Xiang Wu, Jianxin Wang
2021 Briefings in Bioinformatics  
design and parallel computing.  ...  Then, the parallel computing for genome sequence processing is discussed with four common applications: genome sequence alignment, single nucleotide polymorphism calling, genome sequence preprocessing,  ...  Xeon Phi is mainly used to accelerate the Bayesian model computing in mSNP.  ... 
doi:10.1093/bib/bbab070 pmid:33822883 fatcat:a4hj2fhybrc6zlsq6xyiu6snmy

Hardware Accelerated Alignment Algorithm for Optical Labeled Genomes

Pingfan Meng, Matthew Jacobsen, Motoki Kimura, Vladimir Dergachev, Thomas Anantharaman, Michael Requa, Ryan Kastner
2016 ACM Transactions on Reconfigurable Technology and Systems  
Therefore, in order to practically apply this new technology in genome research, accelerated approaches are desirable.  ...  De novo assembly is a widely used methodology in bioinformatics.  ...  We compare the performances and prices of the hardware accelerators.  ... 
doi:10.1145/2840811 fatcat:i4gucx63unafrmd6x5urd46d6y

Fine-Grained Parallel Genomic Sequence Comparison [chapter]

Dominique Lavenier
2010 Parallel and Distributed Computing  
Speedup from 3 to 10 have been measured compared to the SSEARCH program, depending of the length of the sequences. Long sequences favor the use of GPU accelerators.  ...  However, in PLAST, a parallel BLAST-like version for comparing two large databases, SIMD instructions are efficiently used to speedup the computation of the ungap step which represents an important fraction  ...  Fine-Grained Parallel Genomic Sequence Comparison, Parallel and Distributed Computing, Alberto Ros (Ed.), ISBN: 978-953-307-057-5, InTech, Available from: http://www.intechopen.com/books/parallel-and-distributed-computing  ... 
doi:10.5772/9449 fatcat:ns22vyct3rdjre24cs33bjr7ue

Variant Calling Parallelization on Processor-in-Memory Architecture [article]

Dominique LAVENIER, Romaric Jodin, Remy Cimadomo
2020 biorxiv/medrxiv   pre-print
In this paper, we introduce a new combination of software and hardware PIM (Process-in-Memory) architecture to accelerate the variant calling genomic process.  ...  The PIM solution also compared nicely to FPGA or GPU based acceleration bringing similar to twice the processing speed but most importantly being 5 to 8 times cheaper to deploy with up to 6 times less  ...  Several methods have been proposed to accelerate variant calling by the means of parallel and distributed computing techniques: HugeSeq [6] , MegaSeq [7] , Churchill [8] and Halvade [9] support variant  ... 
doi:10.1101/2020.11.03.366237 fatcat:e4vid6ixr5h25dnutnhtvkxxzm

Heterogeneous Cloud Framework for Big Data Genome Sequencing

Chao Wang, Xi Li, Peng Chen, Aili Wang, Xuehai Zhou, Hong Yu
2015 IEEE/ACM Transactions on Computational Biology & Bioinformatics  
The combination of hardware acceleration and MapReduce execution flow could greatly accelerate the task of aligning short length reads to a known reference genome.  ...  In this paper, we propose a novel FPGA-based acceleration solution with MapReduce framework on multiple hardware accelerators.  ...  FPGA Based Accelerations Nevertheless, numerous attempts to accelerate short read mapping on FPGAs tried to use a brute-force approach to compare short sequences in parallel to a reference genome.  ... 
doi:10.1109/tcbb.2014.2351800 pmid:26357087 fatcat:52gw5e6o6jc5tefrr3xau3kdcm

Hardware accelerated novel optical de novo assembly for large-scale genomes

Pingfan Meng, Matthew Jacobsen, Motoki Kimura, Vladimir Dergachev, Thomas Anantharaman, Michael Requa, Ryan Kastner
2014 2014 24th International Conference on Field Programmable Logic and Applications (FPL)  
Therefore, in order to practically apply this new technology in genome research, accelerated approaches are desirable.  ...  De novo assembly is a widely used methodology in bioinformatics.  ...  We parallelized the score element computations using N × 4 threads in each thread-block.  ... 
doi:10.1109/fpl.2014.6927499 dblp:conf/fpl/MengJKDARK14 fatcat:ydumfe4dhze73k3h77rzikfh2y

Accelerating Genome Sequence Analysis via Efficient Hardware/Algorithm Co-Design [article]

Damla Senol Cali
2021 arXiv   pre-print
However, the analysis of genome sequencing data is currently bottlenecked by the computational power and memory bandwidth limitations of existing systems.  ...  We co-design our highly-parallel, scalable and memory-efficient algorithms with low-power and area-efficient hardware accelerators.  ...  using specialized compute units that we design to exploit data locality, and (3) scales linearly in performance with the number of parallel compute units that we add to the system.  ... 
arXiv:2111.01916v1 fatcat:lbwk74jcbjgqzeqk7gnb77asja

GenASM: A High-Performance, Low-Power Approximate String Matching Acceleration Framework for Genome Sequence Analysis

Damla Senol Cali, Gurpreet S. Kalsi, Zulal Bingol, Can Firtina, Lavanya Subramanian, Jeremie S. Kim, Rachata Ausavarungnirun, Mohammed Alser, Juan Gomez-Luna, Amirali Boroumand, Anant Norion, Allison Scibisz (+4 others)
2020 2020 53rd Annual IEEE/ACM International Symposium on Microarchitecture (MICRO)  
an e cient design whose performance scales linearly as we increase the number of compute units working in parallel.  ...  Using this modi ed algorithm, we design the rst hardware accelerator for Bitap.  ...  by using specialized compute units that we design to exploit data locality, and (3) scales linearly in performance with the number of parallel compute units that we add to the system.  ... 
doi:10.1109/micro50266.2020.00081 dblp:conf/micro/CaliKBFSKAAGBNS20 fatcat:q2mvhrltnfgczjso5q457go55i

An Efficient GPUAccelerated Implementation of Genomic Short Read Mapping with BWAMEM

Ernst Joachim Houtgast, VladMihai Sima, Koen Bertels, Zaid AlArs
2017 SIGARCH Computer Architecture News  
Here, a GPU-accelerated implementation of BWA-MEM is proposed.  ...  The mapping stage of such genomics pipelines, which maps the short reads onto a reference genome, takes up a significant portion of execution time.  ...  used tool for the mapping stage of genomics pipelines.  ... 
doi:10.1145/3039902.3039910 fatcat:2g3mx7acczd4fa6yg4axvmpbei

GPU-Accelerated BWA-MEM Genomic Mapping Algorithm Using Adaptive Load Balancing [chapter]

Ernst Joachim Houtgast, Vlad-Mihai Sima, Koen Bertels, Zaid Al-Ars
2016 Lecture Notes in Computer Science  
Genomic sequencing is rapidly becoming a premier generator of Big Data, posing great computational challenges. Hence, acceleration of the algorithms used is of utmost importance.  ...  This paper presents a GPU-accelerated implementation of BWA-MEM, a widely used algorithm to map genomic sequences onto a reference genome.  ...  The authors would like to thank the people at the Neuroscience Department of the Erasmus Medical Center for kindly granting access to their computing facilities for performance tests.  ... 
doi:10.1007/978-3-319-30695-7_10 fatcat:jkvry5jqtrgzzcxe4uwkwe3qyq

Neo-hetergeneous Programming and Parallelized Optimization of a Human Genome Re-sequencing Analysis Software Pipeline on TH-2 Supercomputer

2015 Supercomputing Frontiers and Innovations  
At the most large scale, the whole process takes 8.37 hours using 8192 nodes to finish the analysis of a 300TB dataset of whole genome sequences from 2,000 human beings, which can take as long as 8 months  ...  The amount of genomic data has been explosively accumulating, which calls for an enormous amount of computing power, while current computation methods cannot scale out with the data explosion.  ...  This paper is distributed under the terms of the Creative Commons Attribution-Non Commercial 3.0 License which permits non-commercial use, reproduction and distribution of the work without further permission  ... 
doi:10.14529/jsfi150104 fatcat:szbrtrwqj5ebjiwglsiplxg7li

Supercomputing for the parallelization of whole genome analysis

M. J. Puckelwartz, L. L. Pesce, V. Nelakuditi, L. Dellefave-Castillo, J. R. Golbus, S. M. Day, T. P. Cappola, G. W. Dorn, I. T. Foster, E. M. McNally
2014 Bioinformatics  
Results: We now adapted a Cray XE6 supercomputer to achieve the parallelization required for concurrent multiple genome analysis.  ...  This approach not only markedly speeds computational time but also results in increased usable sequence per genome.  ...  Beagle uses a parallel computation environment and a parallel file system (Lustre) based on shared storage.  ... 
doi:10.1093/bioinformatics/btu071 pmid:24526712 pmcid:PMC4029034 fatcat:zfbyftzrr5cjdnzqrtzxtprlti

GraphSeq: Accelerating String Graph Construction for De Novo Assembly on Spark [article]

Chung-Tsai Su, Ming-Tai Chang, Yun-Chian Cheng, Yun-Lung Li, Yao-Ting Wang
2018 bioRxiv   pre-print
However, string graph construction is computational intensive. We propose GraphSeq to accelerate string graph construction by leveraging the distributed computing framework.  ...  De novo genome assembly is an important application on both uncharacterized genome assembly and variant identification in a reference-unbiased way.  ...  ADAM is an open source project that enables the use of Apache Spark to parallelize genomic data analysis across cluster/cloud computing environments.  ... 
doi:10.1101/321729 fatcat:jfjun5zperdixly67qontc4l5i

GenASM: A High-Performance, Low-Power Approximate String Matching Acceleration Framework for Genome Sequence Analysis [article]

Damla Senol Cali, Gurpreet S. Kalsi, Zülal Bingöl, Can Firtina, Lavanya Subramanian, Jeremie S. Kim, Rachata Ausavarungnirun, Mohammed Alser, Juan Gomez-Luna, Amirali Boroumand, Anant Nori, Allison Scibisz (+4 others)
2020 arXiv   pre-print
Our hardware accelerator consists of specialized compute units and on-chip SRAMs that are designed to match the rate of computation with memory capacity and bandwidth.  ...  We propose GenASM, the first ASM acceleration framework for genome sequence analysis.  ...  by using specialized compute units that we design to exploit data locality, and (3) scales linearly in performance with the number of parallel compute units that we add to the system.  ... 
arXiv:2009.07692v1 fatcat:kfjcuhpx2bahzavc3hmer6wkky
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