Accelerating the Next Generation Long Read Mapping with the FPGA-Based System

Chen, Peng; Wang, Chao; Xi, Li; Zhou, Xuehai

doi:10.1109/tcbb.2014.2326876

Cited by 51 publications

(24 citation statements)

References 35 publications

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“…Whereas existing PC based algorithms process the complete data after the detection has finished, so they would effectively take longer for the whole process of DNA sequencing. The same situation applies to current FPGA acceleration methods where no real-time comparison can be executed [41].…”

Section: F Hardware Resource Usagementioning

confidence: 90%

A Real-Time <italic>de novo</italic> DNA Sequencing Assembly Platform Based on an FPGA Implementation

Georgiou

2016

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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This paper presents an FPGA based DNA comparison platform which can be run concurrently with the sensing phase of DNA sequencing and shortens the overall time needed for de novo DNA assembly. A hybrid overlap searching algorithm is applied which is scalable and can deal with incremental detection of new bases. To handle the incomplete data set which gradually increases during sequencing time, all-against-all comparisons are broken down into successive window-against-window comparison phases and executed using a novel dynamic suffix comparison algorithm combined with a partitioned dynamic programming method. The complete system has been designed to facilitate parallel processing in hardware, which allows real-time comparison and full scalability as well as a decrease in the number of computations required. A base pair comparison rate of 51.2 G/s is achieved when implemented on an FPGA with successful DNA comparison when using data sets from real genomes.

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Section: F Hardware Resource Usagementioning

confidence: 90%

A Real-Time <italic>de novo</italic> DNA Sequencing Assembly Platform Based on an FPGA Implementation

Georgiou

2016

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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“…A great number of papers have been published that either use FPGAs [16][17][18][19][20][21]55], or GPUs [28][29][30].…”

Section: Related Workmentioning

confidence: 99%

3D-Stacked Many-Core Architecture for Biological Sequence Analysis Problems

Liu

Hemani

Paul

et al. 2017

Int J Parallel Prog

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Sequence analysis plays extremely important role in bioinformatics, and most applications of which have compute intensive kernels consuming over 70% of total execution time. By exploiting the compute intensive execution stages of popular sequence analysis applications, we present and evaluate a VLSI architecture with a focus on those that target at biological sequences directly, including pairwise sequence alignment, multiple sequence alignment, database search, and short read sequence mappings. Based on coarse grained reconfigurable array we propose the use of many-core and 3D-stacked technologies to gain further improvement over memory subsystem, which gives another order of magnitude speedup from high bandwidth and low access latency. We analyze our approach in terms of its throughput and efficiency for different application mappings. Initial experimental results are evaluated from a stripped down implementation in a commodity FPGA, and then we scale the results to estimate the performance of our architecture with 9 layers of 70 mm 2 stacked wafers in 45-nm process. We demonstrate numerous estimated speedups better than corresponding existed hardware accelerator platforms for at least 40 times for the entire range of applications and datasets of interest. In comparison, the alternative FPGA based accelerators deliver only improvement for single application, while GPGPUs perform not well enough on accelerating program kernel with random memory access and integer addition/comparison operations.

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“…A significant number of papers have been published that either use FPGAs (see, e.g., [1], [9], [24], [25], [25], [26], [27], [28], [29], [30], [31]) or GPUs (see, e.g., [32], [33]). For instance in [24], the authors propose a hybrid system for short read mapping utilizing both FPGA-based hardware and CPU-based software: the hardware implements parallel block-wise alignment structure to approximate the conventional dynamic programming algorithm.…”

Section: Related Workmentioning

confidence: 99%

“…In [27], the speedup of their accelerator over a six-cores CPU ranges from 22.2x to 42.9x. In [28], the author focus on long reads mapping: their FPGA-based platform achieves a 1.8x-3.2x speedup versus the BWA-SW aligner. In [29], the actors claim that their FPGA tool to be up to 293 times faster than BWA (single-threaded) on an Intel X5650 CPU and 134 times faster than SOAP3 on an NVIDIA GTX 580 GPU.…”

Section: Related Workmentioning

confidence: 99%

<sc>FHAST</sc>: FPGA-Based Acceleration of <sc>Bowtie</sc> in Hardware

Fernandez

Villarreal

Lonardi

et al. 2015

IEEE/ACM Trans. Comput. Biol. and Bioinf.

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Abstract-While the sequencing capability of modern instruments continues to increase exponentially, the computational problem of mapping short sequenced reads to a reference genome still constitutes a bottleneck in the analysis pipeline. A variety of mapping tools (e.g., BOWTIE, BWA) is available for general-purpose computer architectures. These tools can take many hours or even days to deliver mapping results, depending on the number of input reads, the size of the reference genome and the number of allowed mismatches or insertion/deletions, making the mapping problem an ideal candidate for hardware acceleration. In this paper, we present FHAST (FPGA hardware accelerated sequence-matching tool), a drop-in replacement for BOWTIE that uses a hardware design based on field programmable gate arrays (FPGA). Our architecture masks memory latency by executing multiple concurrent hardware threads accessing memory simultaneously. FHAST is composed by multiple parallel engines to exploit the parallelism available to us on an FPGA. We have implemented and tested FHAST on the Convey HC-1 and later ported on the Convey HC-2ex, taking advantage of the large memory bandwidth available to these systems and the shared memory image between hardware and software. A preliminary version of FHASTrunning on the Convey HC-1 achieved up to 70x speedup compared to BOWTIE (single-threaded). An improved version of FHAST running on the Convey HC-2ex FPGAs achieved up to 12x fold speed gain compared to BOWTIE running eight threads on an eight-core conventional architecture, while maintaining almost identical mapping accuracy. FHAST is a drop-in replacement for BOWTIE, so it can be incorporated in any analysis pipeline that uses BOWTIE (e.g., TOPHAT).

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Accelerating the Next Generation Long Read Mapping with the FPGA-Based System

Cited by 51 publications

References 35 publications

A Real-Time <italic>de novo</italic> DNA Sequencing Assembly Platform Based on an FPGA Implementation

A Real-Time <italic>de novo</italic> DNA Sequencing Assembly Platform Based on an FPGA Implementation

3D-Stacked Many-Core Architecture for Biological Sequence Analysis Problems

<sc>FHAST</sc>: FPGA-Based Acceleration of <sc>Bowtie</sc> in Hardware

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