FPGA acceleration of the pair-HMMs forward algorithm for DNA sequence analysis

Ren, Siying; Sima, Vlad-Mihai; Al-Ars, Zaid

doi:10.1109/bibm.2015.7359892

Cited by 22 publications

(16 citation statements)

References 13 publications

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“…However, no source code is given. Other FPGA implementations were explored in [15], [16], [17], [18], [19], and [20]. GPU solutions include [21] and [22].…”

Section: B Hardware Accelerationmentioning

confidence: 99%

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Acceleration of the Pair-HMM forward algorithm on FPGA with cloud integration for GATK

Wertenbroek

Thoma

2019

2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

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The Pair-HMM forward-algorithm is an essential algorithm found in many genomic related analyses. The high number of floating point operations in the algorithm makes it one of the main contributors to the compute time of analysis pipelines. To speed-up computations we propose an FPGA based hardware accelerator for the Amazon AWS F1 Cloud platform. The accelerator is open source and has been tested within the popular Genomic Analysis Toolkit (GATK) pipeline. The accelerator achieved up to 15× speed-up against the software implementation when used in-pipeline. The accelerator has also been tested in the experimental Spark (distributed) version of the GATK HaplotypeCaller tool. An in-depth analysis of the compute time contributions allowed to point out the main bottlenecks for accelerators in the GATK pipeline, resulting in a hybrid CPU-FPGA solution to best exploit both resources.

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“…However, no source code is given. Other FPGA implementations were explored in [15], [16], [17], [18], [19], and [20]. GPU solutions include [21] and [22].…”

Section: B Hardware Accelerationmentioning

confidence: 99%

“…5) Compute Engine pipeline against parallel Processing Engines: Several other solutions [15], [17], [18], [20] use parallel arrays of PEs to compute partial diagonals of the matrix. While this solution may seem faster, this is not necessarily the case, and has some other drawbacks as well.…”

Section: A Hardware Accelerator Overviewmentioning

confidence: 99%

Acceleration of the Pair-HMM forward algorithm on FPGA with cloud integration for GATK

Wertenbroek

Thoma

2019

2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

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Section: B Related Workmentioning

confidence: 99%

“…Research on increasing the speed of PFA by optimizations and acceleration can be found in In addition, [13] and [14] propose FPGA-based implementations of PFA on the Convey Computer platform and the IBM POWER8 platform, respectively. [13] employs a systolic array to map the algorithm on FPGAs. [14] proposes pipelined PEs (processing elements) of the systolic array, in addition to using the CAPI interface of the IBM POWER8 platform, which makes the data transfer more efficient.…”

Section: B Related Workmentioning

confidence: 99%

Exploration of alternative GPU implementations of the pair-HMMs forward algorithm

Ren

Koen

Al-Ars

2016

2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

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In order to handle the massive raw data generated by next generation sequencing (NGS) platforms, GPUs are widely used by many genetic analysis tools to speed up the used algorithms. In this paper, we use GPUs to accelerate the pair-HMMs forward algorithm, which is used to calculate the overall alignment probability in many genomics analysis tools. We firstly evaluate two different implementation methods to accelerate the pair-HMMs forward algorithm according to their effectiveness on GPU platforms. Based on these two methods, we present several implementations of the pair-HMMs forward algorithm. We execute these implementations on the NVIDIA Tesla K40 card using different datasets to compare the performance. Experimental results show that the intra-task implementation has the highest throughput in most cases, achieving pure computational throughput as high as 23.56 GCUPS for synthetic datasets. On a real dataset, the inter-task implementation achieves 4.82x speedup compared with a vectorized implementation executed on a 20-core POWER8 system.

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“…The Systolic Array (SA) is a parallel architecture that can be applied to a wide range of problems in a modular manner to simplify the design complexity. For the design of FPGA-based Pair-HMM forward algorithm accelerators, the one-dimensional SA [17,21,22] is a common approach and has some improved variants. The main structure of the SA is a pipeline, where the data flow among the Processing Elements (PEs).…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of FPGA-Based Pair-HMM Accelerator Structures

2019

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As one of the most important and computationally-intensive parts in bioinformatics analysis, the Pair Hidden Markov Model (Pair-HMM) forward algorithm is widely recognized and has great potential. Therefore, it is important to accelerate the process of this algorithm. There are various approaches to accelerate Pair-HMM, especially the accelerators stemmed from the Field Programmable Gate Array (FPGA) due to the highly-customizable on-chip resources and deep pipeline potential available to the designer. In this paper, we focus on the FPGA-based accelerators for the Pair-HMM forward algorithm proposed in recent years. The non-cooperation structure, which was proposed in our previous work, is compared with the Systolic Array (SA) structure and PE ring structure in the structure characteristics, calculation mode, computational efficiency, and storage requirement. We build an analysis model to evaluate the performance of the ring structure and our non-cooperative structure. Furthermore, based on this, we provide a detailed analysis of the characteristics of structures of different accelerators and of the selection of a suitable structure for different scenarios. Based on the non-cooperative PE structure, we design a new chain topology for the accelerator. Experimental results show that our non-cooperation structure is superior to the other structures in performance and execution efficiency, and our new topology improves the performance of the accelerator. Finally, we propose some ideas about the improvement of the non-cooperative structure accelerator for future work.

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FPGA acceleration of the pair-HMMs forward algorithm for DNA sequence analysis

Cited by 22 publications

References 13 publications

Acceleration of the Pair-HMM forward algorithm on FPGA with cloud integration for GATK

Acceleration of the Pair-HMM forward algorithm on FPGA with cloud integration for GATK

Exploration of alternative GPU implementations of the pair-HMMs forward algorithm

Comparative Analysis of FPGA-Based Pair-HMM Accelerator Structures

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