HBLast: An Open-Source FPGA Library for DNA Sequencing Acceleration

Bekbolat, Marzhan; Kairatova, Sabina; Shymyrbay, Ayan; Vipin, Kizheppatt

doi:10.1109/ipdpsw.2019.00022

Cited by 5 publications

(2 citation statements)

References 8 publications

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“…The second is the usage of either high-performance compute or consumer-grade GPUs as accelerators [20,21]. The last approach is the usage of FPGA-accelerators which, although they are not as fast as systems based on high-performance GPUs, tend to be more energy-efficient [22][23][24]. However, these systems have started to reach their limits in terms of performance, therefore, current research focuses on heterogeneous and cluster-based systems, e.g., multi-GPU clusters [25] or CPU-GPU co-systems [26].…”

Section: Related Workmentioning

confidence: 99%

Accelerating Binary String Comparisons with a Scalable, Streaming-Based System Architecture Based on FPGAs

et al. 2020

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This paper is concerned with Field Programmable Gate Arrays (FPGA)-based systems for energy-efficient high-throughput string comparison. Modern applications which involve comparisons across large data sets—such as large sequence sets in molecular biology—are by their nature computationally intensive. In this work, we present a scalable FPGA-based system architecture to accelerate the comparison of binary strings. The current architecture supports arbitrary lengths in the range 16 to 2048-bit, covering a wide range of possible applications. In our example application, we consider DNA sequences embedded in a binary vector space through Locality Sensitive Hashing (LSH) one of several possible encodings that enable us to avoid more costly character-based operations. Here the resulting encoding is a 512-bit binary signature with comparisons based on the Hamming distance. In this approach, most of the load arises from the calculation of the O ( m ∗ n ) Hamming distances between the signatures, where m is the number of queries and n is the number of signatures contained in the database. Signature generation only needs to be performed once, and we do not consider it further, focusing instead on accelerating the signature comparisons. The proposed FPGA-based architecture is optimized for high-throughput using hundreds of computing elements, arranged in a systolic array. These core computing elements can be adapted to support other string comparison algorithms with little effort, while the other infrastructure stays the same. On a Xilinx Virtex UltraScale+ FPGA (XCVU9P-2), a peak throughput of 75.4 billion comparisons per second—of 512-bit signatures—was achieved, using a design with 384 parallel processing elements and a clock frequency of 200 MHz. This makes our FPGA design 86 times faster than a highly optimized CPU implementation. Compared to a GPU design, executed on an NVIDIA GTX1060, it performs nearly five times faster.

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Section: Related Workmentioning

confidence: 99%

Accelerating Binary String Comparisons with a Scalable, Streaming-Based System Architecture Based on FPGAs

et al. 2020

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“…Work in [78] proposed an open-source FPGA-based accel- erator that runs BLAST algorithm for queries with 256 bases and reference genomes with 2 billion base pairs. The work in [79] extends the acceleration to multi-FPGA platforms.…”

Section: Hash Tablesmentioning

confidence: 99%

FPGA Acceleration of Sequence Alignment: A Survey

Salamat,

Rosing

2020

Preprint

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Genomics is changing our understanding of humans, evolution, diseases, and medicines to name but a few. As sequencing technology is developed collecting DNA sequences takes less time thereby generating more genetic data every day. Today the rate of generating genetic data is outpacing the rate of computation power growth. Current sequencing machines can sequence 50 humans genome per day; however, aligning the read sequences against a reference genome and assembling the genome will take 1300 CPU hours [1]. The main step in constructing the genome is aligning the reads against a reference genome. Numerous accelerators have been proposed to accelerate the DNA alignment process. Providing massive parallelism, FPGA-based accelerators have shown great performance in accelerating DNA alignment algorithms. Additionally, FPGA-based accelerators provide better energy efficiency than general-purpose processors. In this survey, we introduce three main DNA alignment algorithms and FPGAbased implementation of these algorithms to accelerate the DNA alignment. We also, compare these three alignment categories and show how accelerators are developing during the time.

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