Innovations in genome sequencing techniques are enabling remarkably fast and low cost production of raw genome data. As Moore's law tapers off, bottlenecks in genome sequencing are shifting to computational resources for mapping reads to reference DNA. This paper presents SeedEx, a read-alignment accelerator focused on the seed-extension step. SeedEx is based on the observation that only a small fraction of reads require large edit distance for alignment, hence an area efficient narrow-band seed-extension accelerator can suffice in practice. However, due to the highly error-sensitive nature of genomic workloads, guaranteeing optimality of alignment result is of cardinal importance. Towards this end, we propose a speculation-and-test based framework by using strict but powerful optimality checking mechanisms. We demonstrate SeedEx by an implementation on a cloud FPGA. SeedEx achieves 6.0× iso-area throughput speedup when compared to a banded Smith-Waterman baseline, and achieving 43.9 M seed extentions/s on AWS f1.2xlarge instance. Integration with BWA-MEM2 improves the execution time by 2.3×.

SeedEx: A Genome Sequencing Accelerator for Optimal Alignments in Subminimal Space

Read mapping is a fundamental step in many genomics applications. It is used to identify potential matches and differences between fragments (called reads) of a sequenced genome and an already known genome (called a reference genome). Read mapping is costly because it needs to perform approximate string matching (ASM) on large amounts of data. To address the computational challenges in genome analysis, many prior works propose various approaches such as accurate filters that select the reads within a dataset of genomic reads (called a read set) that must undergo expensive computation, efficient heuristics, and hardware acceleration. While effective at reducing the amount of expensive computation, all such approaches still require the costly movement of a large amount of data from storage to the rest of the system, which can significantly lower the end-to-end performance of read mapping in conventional and emerging genomics systems. We propose GenStore, the first in-storage processing system designed for genome sequence analysis that greatly reduces both data movement and computational overheads of genome sequence analysis by exploiting low-cost and accurate in-storage filters. GenStore leverages hardware/software co-design to address the challenges of in-storage processing, supporting reads with 1) different properties such as read lengths and error rates, which highly depend on the sequencing technology, and 2) different degrees of genetic variation compared to the reference genome, which highly depends on the genomes that are being compared. Through rigorous analysis of read mapping processes of reads with different properties and degrees of genetic variation, we meticulously design low-cost hardware accelerators and data/computation flows inside a NAND flash-based solid-state drive (SSD). Our evaluation using a wide range of real genomic datasets shows that GenStore, when implemented in three modern NAND flash-based SSDs, significantly improves the read mapping performance of state-of-the-art software (hardware) baselines by 2.07-6.05× (1.52-3.32×) for read sets with high similarity to the reference genome and 1.45-33.63× (2.70-19.2×) for read sets with low similarity to the reference genome.

GenStore: a high-performance in-storage processing system for genome sequence analysis

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-to-sequence 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 sequence-to-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 highly-parallel memory access, which alleviates the memory bottleneck. We demonstrate that SeGraM provides significant improvements for multiple steps of the sequence-to-graph (i.e., S2G) and sequence-to-sequence (i.e., S2S) mapping pipelines. First, SeGraM outperforms state-of-the-art S2G mapping tools by 5.9×/3.9× and 106×/- 742× for long and short reads, respectively, while reducing power consumption by 4.1×/4.4× and 3.0×/3.2×. Second, BitAlign outperforms a state-of-the-art S2G alignment tool by 41×-539× and three S2S alignment accelerators by 1.2×-4.8×. We conclude that SeGraM is a high-performance and low-cost universal genomics mapping accelerator that efficiently supports both sequence-to-graph and sequence-to-sequence mapping pipelines.

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SeGraM: a universal hardware accelerator for genomic sequence-to-graph and sequence-to-sequence mapping

The year 2011 marked an important transition for FPGA high-level synthesis (HLS), as it went from prototyping to deployment. A decade later, in this article, we assess the progress of the deployment of HLS technology and highlight the successes in several application domains, including deep learning, video transcoding, graph processing, and genome sequencing. We also discuss the challenges faced by today’s HLS technology and the opportunities for further research and development, especially in the areas of achieving high clock frequency, coping with complex pragmas and system integration, legacy code transformation, building on open source HLS infrastructures, supporting domain-specific languages, and standardization. It is our hope that this article will inspire more research on FPGA HLS and bring it to a new height.

FPGA HLS Today: Successes, Challenges, and Opportunities

In the last years, advances in next-generation sequencing technologies have enabled the proliferation of genomic applications that guide personalized medicine. These applications have an enormous computational cost due to the large amount of genomic data they process. The first step in many of these applications consists in aligning reads against a reference genome. Very recently, the wavefront alignment algorithm has been introduced, significantly reducing the execution time of the read alignment process. This paper presents the first FPGA-based hardware/software co-designed accelerator of such relevant algorithm. Compared to the reference WFA CPU-only implementation, the proposed FPGA accelerator achieves performance speedups of up to 13.5 × while consuming up to 14.6 × less energy.

An FPGA Accelerator of the Wavefront Algorithm for Genomics Pairwise Alignment

Read alignment is a time-consuming step in genome sequencing analysis. The most widely used software for read alignment, BWA-MEM, and the recently published faster version BWA-MEM2 are based on the seed-and-extend paradigm for read alignment. The seeding step of read alignment is a major bottleneck contributing ~40% to the overall execution time of BWA-MEM2 when aligning whole human genome reads from the Platinum Genomes dataset. This is because both BWA-MEM and BWA-MEM2 use a compressed index structure called the FMD-Index, which results in high bandwidth requirements, primarily due to its character-by-character processing of reads. For instance, to seed each read (101 DNA base-pairs stored in 37.8 bytes), the FMD-Index solution in BWA-MEM2 requires ~68.5 KB of index data. We propose a novel indexing data structure named Enumerated Radix Tree (ERT) and design a custom seeding accelerator based on it. ERT improves bandwidth efficiency of BWA-MEM2 by 4.5X while guaranteeing 100% identical output to the original software, and still fitting in 64 GB DRAM. Overall, the proposed seeding accelerator implemented on AWS F1 FPGA (f1.4xlarge) improves seeding throughput of BWA-MEM2 by 3.3X. When combined with seed-extension accelerators, we observe a 2.1X improvement in overall read alignment throughput over BWA-MEM2. The software implementation of ERT is integrated into BWA-MEM2 (ert branch: https://github.com/bwa-mem2/bwa-mem2/tree/ert) and is open sourced for the benefit of the research community.

Accelerated seeding for genome sequence alignment with enumerated radix trees

Motivation Read alignment is a time-consuming step in genome sequence analysis. In the read alignment software BWA-MEM and the recently published faster version BWA-MEM2, the seeding step is a major bottleneck, for instance, contributing 38% to the overall execution time in BWA-MEM2 when aligning single-end whole human genome reads from the Platinum Genomes dataset. This is because both BWA-MEM and BWA-MEM2 use a compressed index structure called the FMD-Index, which results in high memory bandwidth requirements for seeding, primarily due to its character-by-character processing of reads. Results We propose a memory bandwidth-aware data structure for maximal-exact-match seeding called Enumerated Radix Tree (ERT). ERT trades off memory capacity to improve seeding performance (∼60 GB index for human genome). Together with optimizations to the seeding algorithm and mate-rescue step, ERT when integrated into BWA-MEM2 speeds up overall read alignment by 1.28× and provides up to 2.1× higher seeding performance while guaranteeing identical output to the original software. Furthermore, we prototype an FPGA implementation of ERT on Amazon EC2 F1 cloud and observe 1.6× higher seeding throughput over a 48-thread optimized CPU-ERT implementation. Availability and implementation https://github.com/arun-sub/bwa-mem2 Contact arunsub@umich.edu, reetudas@umich.edu

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Accelerating Maximal-Exact-Match Seeding with Enumerated Radix Trees

We present the design, characterization, and production of a signal packet router for the upgrade of the ATLAS forward muon detector. The router serves as a packet switch in one of the primary trigger paths. It handles up to 12 serial inputs at 4.8 Gbps/input from on-detector electronics and outputs four 4.8 Gbps to the trigger processing circuits. In the 12-to-4 packet switching process, the input streams are decoded and synchronized to a common clock domain for data packets forwarding and NULL packets suppression. Challenges in the design include operations of multi-giga hertz serial links with low and constant latency (<100 ns) for packet routing, as well as mitigation of radiation-induced effects in the harsh radiation environment over 10 years of detector operation. The Router design has been fully characterized and demonstrated to meet all specifications. In total 256 Routers were installed in the detector. Its design concept can be used in other similar applications, particularly in a radiation environment. 

Nathan Ozog

Papers

SeedEx: A Genome Sequencing Accelerator for Optimal Alignments in Subminimal Space

Accelerated seeding for genome sequence alignment with enumerated radix trees

Accelerating Maximal-Exact-Match Seeding with Enumerated Radix Trees

A signal packet router for the upgrade of the Muon spectrometer at the ATLAS experiment