Asap

Banerjee, Subho S.; El-Hadedy, Mohamed; Lim, Jongwoo; Chen, Daniel; Kalbarczyk, Zbigniew; Chen, Deming; Iyer, Ravishankar K.

doi:10.1145/3020078.3021796

Cited by 3 publications

(9 citation statements)

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“…We also compare GenASM with ASAP [22], which is the state-of-the-art FPGA-based accelerator for computing the edit distance between two short reads. We estimate the performance of ASAP using data reported by the original work [22].…”

Section: Evaluation Methodologymentioning

confidence: 99%

“…We also compare GenASM with ASAP [22], the state-ofthe-art FPGA-based accelerator for edit distance calculation. While we are unable to reimplement ASAP, the execution time and power consumption analysis of ASAP provided in [22] allows us to provide a comparison between GenASM and ASAP. ASAP is optimized for shorter sequences and reports execution time only for sequences of length 64bp-320bp [22].…”

Section: Use Case 3: Edit Distance Calculationmentioning

confidence: 99%

“…While we are unable to reimplement ASAP, the execution time and power consumption analysis of ASAP provided in [22] allows us to provide a comparison between GenASM and ASAP. ASAP is optimized for shorter sequences and reports execution time only for sequences of length 64bp-320bp [22]. Based on [22], the execution time of one ASAP accelerator increases from 6.8 µs to 18.8 µs when the sequence length increases from 64bp to 320bp, while consuming 6.8 W of power.…”

Section: Use Case 3: Edit Distance Calculationmentioning

confidence: 99%

“…Our goal is to design a fast, e cient, and exible framework for both short and long reads, which can be used to accelerate multiple steps of the genome sequence analysis pipeline. To avoid implementing more complex hardware for the dynamic programming based algorithm [22,33,49,65,87,88,147,162], we base GenASM upon the Bitap algorithm [21,174]. Bitap uses only fast and simple bitwise operations to perform approximate string matching, making it amenable to e cient hardware acceleration.…”

Section: Introductionmentioning

confidence: 99%

“…(3) For edit distance calculation, we compare GenASM with a state-of-the-art software library, Edlib [155], and FPGAbased accelerator, ASAP [22]. Compared to Edlib, GenASM provides 22-12501× speedup, for varying sequence lengths and similarity values, while reducing power consumption by 548-582×.…”

Section: Introductionmentioning

confidence: 99%

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GenASM: A High-Performance, Low-Power Approximate String Matching Acceleration Framework for Genome Sequence Analysis

Cali¹,

Kalsi²,

Bingöl³

et al. 2020

Preprint

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Genome sequence analysis has enabled signi cant advancements in medical and scienti c areas such as personalized medicine, outbreak tracing, and the understanding of evolution. To perform genome sequencing, devices extract small random fragments of an organism's DNA sequence (known as reads). The rst step of genome sequence analysis is a computational process known as read mapping. In read mapping, each fragment is matched to its potential location in the reference genome with the goal of identifying the original location of each read in the genome. Unfortunately, rapid genome sequencing is currently bottlenecked by the computational power and memory bandwidth limitations of existing systems, as many of the steps in genome sequence analysis must process a large amount of data. A major contributor to this bottleneck is approximate string matching (ASM), which is used at multiple points during the mapping process. ASM enables read mapping to account for sequencing errors and genetic variations in the reads.We propose GenASM, the rst ASM acceleration framework for genome sequence analysis. GenASM performs bitvectorbased ASM, which can e ciently accelerate multiple steps of genome sequence analysis. We modify the underlying ASM algorithm (Bitap) to signi cantly increase its parallelism and reduce its memory footprint. Using this modi ed algorithm, we design the rst hardware accelerator for Bitap. Our hardware accelerator consists of specialized systolic-array-based compute units and on-chip SRAMs that are designed to match the rate of computation with memory capacity and bandwidth, resulting in an e cient design whose performance scales linearly as we increase the number of compute units working in parallel.We demonstrate that GenASM provides signi cant performance and power bene ts for three di erent use cases in genome sequence analysis. First, GenASM accelerates read alignment for both long reads and short reads. For long reads, GenASM outperforms state-of-the-art software and hardware accelerators by 116× and 3.9×, respectively, while reducing power consumption by 37× and 2.7×. For short reads, GenASM outperforms state-of-the-art software and hardware accelerators by 111× and 1.9×. Second, GenASM accelerates pre-alignment ltering for short reads, with 3.7× the performance of a state-of-theart pre-alignment lter, while reducing power consumption by 1.7× and signi cantly improving the ltering accuracy. Third, GenASM accelerates edit distance calculation, with 22-12501× and 9.3-400× speedups over the state-of-the-art software library and FPGA-based accelerator, respectively, while reducing power consumption by 548-582× and 67×. We conclude that GenASM is a exible, high-performance, and low-power framework, and we brie y discuss four other use cases that can bene t from GenASM.

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Section: Evaluation Methodologymentioning

confidence: 99%

Section: Use Case 3: Edit Distance Calculationmentioning

confidence: 99%

Section: Use Case 3: Edit Distance Calculationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

GenASM: A High-Performance, Low-Power Approximate String Matching Acceleration Framework for Genome Sequence Analysis

Cali¹,

Kalsi²,

Bingöl³

et al. 2020

Preprint

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show abstract

A ML-based Runtime System for Executing Dataflow Graphs on Heterogeneous Processors

Banerjee

Athreya

Kalbarczyk

et al. 2018

Proceedings of the ACM Symposium on Cloud Computing

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SeGraM: A Universal Hardware Accelerator for Genomic Sequence-to-Graph and Sequence-to-Sequence Mapping

Cali,

Kanellopoulos,

Lindegger

et al. 2022

Preprint

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A critical step of genome sequence analysis is the mapping of sequenced DNA fragments (i.e., reads) collected from an individual to a known linear reference genome sequence (i.e., sequence-tosequence mapping). Recent works replace the linear reference sequence with a graph-based representation of the reference genome, which captures the genetic variations and diversity across many individuals in a population. Mapping reads to the graph-based reference genome (i.e., sequence-to-graph mapping) results in notable quality improvements in genome analysis. Unfortunately, while sequence-to-sequence mapping is well studied with many available tools and accelerators, sequence-to-graph mapping is a more difficult computational problem, with a much smaller number of practical software tools currently available.We analyze two state-of-the-art sequence-to-graph mapping tools and reveal four key issues. We find that there is a pressing need to have a specialized, high-performance, scalable, and low-cost algorithm/hardware co-design that alleviates bottlenecks in both the seeding and alignment steps of sequence-to-graph mapping. Since sequence-to-sequence mapping can be treated as a special case of sequence-to-graph mapping, we aim to design an accelerator that is efficient for both linear and graph-based read mapping.To this end, we propose SeGraM, a universal algorithm/hardware co-designed genomic mapping accelerator that can effectively and efficiently support both sequence-to-graph mapping and sequenceto-sequence mapping, for both short and long reads. To our knowledge, SeGraM is the first algorithm/hardware co-design for accelerating sequence-to-graph mapping. SeGraM consists of two main components: (1) MinSeed, the first minimizer-based seeding accelerator, which finds the candidate locations in a given genome graph; and (2) BitAlign, the first bitvector-based sequence-to-graph alignment accelerator, which performs alignment between a given read and the subgraph identified by MinSeed. We couple SeGraM with high-bandwidth memory to exploit low latency and highlyparallel memory access, which alleviates the memory bottleneck.

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Asap

Cited by 3 publications

References 0 publications

GenASM: A High-Performance, Low-Power Approximate String Matching Acceleration Framework for Genome Sequence Analysis

GenASM: A High-Performance, Low-Power Approximate String Matching Acceleration Framework for Genome Sequence Analysis

A ML-based Runtime System for Executing Dataflow Graphs on Heterogeneous Processors

SeGraM: A Universal Hardware Accelerator for Genomic Sequence-to-Graph and Sequence-to-Sequence Mapping

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