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

Liu, Pei; Hemani, Ahmed; Paul, Kolin; Weis, Christian; Jung, Matthias; Wehn, Norbert

doi:10.1007/s10766-017-0495-0

Cited by 14 publications

(8 citation statements)

References 51 publications

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“…Accelerating Sequence Alignment. Another very recent prior work [65] exploits the high memory bandwidth and the reconfigurable logic layer of 3D-stacked memory to implement an accelerator for sequence alignment (among other basic algorithms within the sequence analysis pipeline). Many prior works (e.g., [13-16, 26, 35, 41, 70, 81, 82, 96]) use FPGAs to also accelerate sequence alignment.…”

Section: Related Workmentioning

confidence: 99%

“…We have shown that GRIM-Filter significantly reduces the execution time of read mappers by reducing the number of unnecessary sequence alignments and by taking advantage of processing-in-memory using 3D-stacked DRAM technology. We believe there are many other possible applications for employing 3D-stacked DRAM technology within the genome sequence analysis pipeline (as initially explored in [65]), and significant additional performance improvements can be obtained by combining future techniques with GRIM-Filter. Because GRIM-Filter is essentially a seed location filter to be employed before sequence alignment during read mapping, it can be used in any other read mapper along with any other acceleration mechanisms in the genome sequence analysis pipeline.…”

Section: Future Workmentioning

confidence: 99%

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GRIM-Filter: Fast seed location filtering in DNA read mapping using processing-in-memory technologies

et al. 2018

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BackgroundSeed location filtering is critical in DNA read mapping, a process where billions of DNA fragments (reads) sampled from a donor are mapped onto a reference genome to identify genomic variants of the donor. State-of-the-art read mappers 1) quickly generate possible mapping locations for seeds (i.e., smaller segments) within each read, 2) extract reference sequences at each of the mapping locations, and 3) check similarity between each read and its associated reference sequences with a computationally-expensive algorithm (i.e., sequence alignment) to determine the origin of the read. A seed location filter comes into play before alignment, discarding seed locations that alignment would deem a poor match. The ideal seed location filter would discard all poor match locations prior to alignment such that there is no wasted computation on unnecessary alignments.ResultsWe propose a novel seed location filtering algorithm, GRIM-Filter, optimized to exploit 3D-stacked memory systems that integrate computation within a logic layer stacked under memory layers, to perform processing-in-memory (PIM). GRIM-Filter quickly filters seed locations by 1) introducing a new representation of coarse-grained segments of the reference genome, and 2) using massively-parallel in-memory operations to identify read presence within each coarse-grained segment. Our evaluations show that for a sequence alignment error tolerance of 0.05, GRIM-Filter 1) reduces the false negative rate of filtering by 5.59x–6.41x, and 2) provides an end-to-end read mapper speedup of 1.81x–3.65x, compared to a state-of-the-art read mapper employing the best previous seed location filtering algorithm.ConclusionGRIM-Filter exploits 3D-stacked memory, which enables the efficient use of processing-in-memory, to overcome the memory bandwidth bottleneck in seed location filtering. We show that GRIM-Filter significantly improves the performance of a state-of-the-art read mapper. GRIM-Filter is a universal seed location filter that can be applied to any read mapper. We hope that our results provide inspiration for new works to design other bioinformatics algorithms that take advantage of emerging technologies and new processing paradigms, such as processing-in-memory using 3D-stacked memory devices.

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Section: Related Workmentioning

confidence: 99%

Section: Future Workmentioning

confidence: 99%

GRIM-Filter: Fast seed location filtering in DNA read mapping using processing-in-memory technologies

et al. 2018

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“…(2019) argue the need for co-design; the design of the hardware acceleration and SRA software in parallel. This is especially true at scale, with more varied computational blocks included within the system ( Liu et al , 2017 ). Implementations such as ASAP ( Banerjee et al , 2019 ) and AligneR ( Zokaee et al , 2018 ) sufficiently illustrate this requirement in which edit distance computation is executed as a systolic array in parallel within dedicated electronic hardware rather than sequentially on CPUs.…”

Section: Discussionmentioning

confidence: 99%

“…Efforts to increase speed through closely coupling memory and computation, i.e. physically stacking computational blocks used for alignment with dedicated RAM has resulted in decreased accuracy, high energy consumption and high implementation costs ( Liu et al , 2017 ). As such, further scale in this regard produces diminishing returns.…”

Section: Opportunities In Short Read Alignment Accelerationmentioning

confidence: 99%

“…Thus, a search usually extends the full reference genome for each read resulting in billions of searches, making it computationally intensive ( McVicar et al , 2016 ). Previous attempts to increase read alignment throughput have included multistage alignment algorithms ( McVicar et al , 2016 ), pre-alignment filters ( Kaplan et al , 2019 ) and many-core processing ( Liu et al , 2017 ).…”

Section: Introductionmentioning

confidence: 99%

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Hardware acceleration of genomics data analysis: challenges and opportunities

2021

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The significant decline in the cost of genome sequencing has dramatically changed the typical bioinformatics pipeline for analysing sequencing data. Where traditionally, the computational challenge of sequencing is now secondary to genomic data analysis. Short read alignment (SRA) is a ubiquitous process within every modern bioinformatics pipeline in the field of genomics and is often regarded as the principal computational bottleneck. Many hardware and software approaches have been provided to solve the challenge of acceleration. However, previous attempts to increase throughput using many-core processing strategies have enjoyed limited success, mainly due to a dependence on global memory for each computational block. The limited scalability and high energy costs of many-core SRA implementations pose a significant constraint in maintaining acceleration. The Networks-On-Chip (NoC) hardware interconnect mechanism has advanced the scalability of many-core computing systems and, more recently, has demonstrated potential in SRA implementations by integrating multiple computational blocks such as pre-alignment filtering and sequence alignment efficiently, while minimising memory latency and global memory access. This paper provides a state of the art review on current hardware acceleration strategies for genomic data analysis, and it establishes the challenges and opportunities of utilising NoCs as a critical building block in next-generation sequencing (NGS) technologies for advancing the speed of analysis.

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