NoC-enabled software/hardware co-design framework for accelerating
            <i>k-mer</i>
            counting

Joardar, Biresh Kumar; Ghosh, Priyanka; Pande, Partha Pratim; Kalyanaraman, Ananth; Krishnamoorthy, Sriram

doi:10.1145/3313231.3352367

Cited by 9 publications

(4 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“… Alser et al. (2017) , Kim et al (2018) and Joardar et al. (2019) argue the need for co-design; the design of the hardware acceleration and SRA software in parallel.…”

Section: Discussionmentioning

confidence: 99%

“…This is further illustrated by Das and Ghosal (2018) who suggest that the NoC network topology, particularly those relying exclusively on mesh topologies, result in higher latency (slower performance) at higher network dimensions. Joardar et al (2019) among others, identified significant routing constraints with NoCs for genomic data analysis, and add that read alignment algorithms require repeated memory accesses resulting in idle computation units and high latency times ( Joardar et al , 2019 ). As such, the traffic patterns produced are highly irregular.…”

Section: Computational Challenges Of Short Read Alignmentmentioning

confidence: 99%

“…As such, the traffic patterns produced are highly irregular. Many ‘off-the-shelf’ routing algorithms assume uniform traffic patterns and therefore are not suitable for k -mer counting or sequence alignment at large ( Joardar et al , 2019 ; Subbulakshmi and Balamurugan, 2014 ). This is echoed by Turakhia et al (2017) who advocate the importance of co-design of software and hardware ( Turakhia et al , 2017 , 2019 ).…”

Section: Computational Challenges Of Short Read Alignmentmentioning

confidence: 99%

“… Wang and Wang (2019) further demonstrated the acceleration of popular computational algorithms using a novel NoC-based accelerator. Thus, the NoC paradigm provides a practical interconnection mechanism for enabling high integration of many-core designs with a high degree of modularity and explicit data-parallelism ( Das and Ghosal, 2018 ; Joardar et al , 2019 ; Sarkar et al , 2010 ; Wang and Wang, 2019 ).…”

Section: Opportunities In Short Read Alignment Accelerationmentioning

confidence: 99%

See 3 more Smart Citations

Hardware acceleration of genomics data analysis: challenges and opportunities

2021

View full text Add to dashboard Cite

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.

show abstract

“… Alser et al. (2017) , Kim et al (2018) and Joardar et al. (2019) argue the need for co-design; the design of the hardware acceleration and SRA software in parallel.…”

Section: Discussionmentioning

confidence: 99%