11 research outputs found
Hardware Accelerated DNA Sequencing
DNA sequencing technology is quickly evolving. The latest developments ex-
ploit nanopore sensing and microelectronics to realize real-time, hand-held devices.
A critical limitation in these portable sequencing machines is the requirement of
powerful data processing consoles, a need incompatible with portability and wide
deployment. This thesis proposes a rst step towards addressing this problem, the
construction of specialized computing modules { hardware accelerators { that can
execute the required computations in real-time, within a small footprint, and at a
fraction of the power needed by conventional computers. Such a hardware accel-
erator, in FPGA form, is introduced and optimized specically for the basecalling
function of the DNA sequencing pipeline. Key basecalling computations are identi-
ed and ported to custom FPGA hardware. Remaining basecalling operations are
maintained in a traditional CPU which maintains constant communications with
its FPGA accelerator over the PCIe bus. Measured results demonstrated a 137X
basecalling speed improvement over CPU-only methods while consuming 17X less
power than a CPU-only method
GenPIP: In-Memory Acceleration of Genome Analysis via Tight Integration of Basecalling and Read Mapping
Nanopore sequencing is a widely-used high-throughput genome sequencing
technology that can sequence long fragments of a genome into raw electrical
signals at low cost. Nanopore sequencing requires two computationally-costly
processing steps for accurate downstream genome analysis. The first step,
basecalling, translates the raw electrical signals into nucleotide bases (i.e.,
A, C, G, T). The second step, read mapping, finds the correct location of a
read in a reference genome. In existing genome analysis pipelines, basecalling
and read mapping are executed separately. We observe in this work that such
separate execution of the two most time-consuming steps inherently leads to (1)
significant data movement and (2) redundant computations on the data, slowing
down the genome analysis pipeline. This paper proposes GenPIP, an in-memory
genome analysis accelerator that tightly integrates basecalling and read
mapping. GenPIP improves the performance of the genome analysis pipeline with
two key mechanisms: (1) in-memory fine-grained collaborative execution of the
major genome analysis steps in parallel; (2) a new technique for
early-rejection of low-quality and unmapped reads to timely stop the execution
of genome analysis for such reads, reducing inefficient computation. Our
experiments show that, for the execution of the genome analysis pipeline,
GenPIP provides 41.6X (8.4X) speedup and 32.8X (20.8X) energy savings with
negligible accuracy loss compared to the state-of-the-art software genome
analysis tools executed on a state-of-the-art CPU (GPU). Compared to a design
that combines state-of-the-art in-memory basecalling and read mapping
accelerators, GenPIP provides 1.39X speedup and 1.37X energy savings.Comment: 17 pages, 13 figure
RawHash: Enabling Fast and Accurate Real-Time Analysis of Raw Nanopore Signals for Large Genomes
Nanopore sequencers generate electrical raw signals in real-time while
sequencing long genomic strands. These raw signals can be analyzed as they are
generated, providing an opportunity for real-time genome analysis. An important
feature of nanopore sequencing, Read Until, can eject strands from sequencers
without fully sequencing them, which provides opportunities to computationally
reduce the sequencing time and cost. However, existing works utilizing Read
Until either 1) require powerful computational resources that may not be
available for portable sequencers or 2) lack scalability for large genomes,
rendering them inaccurate or ineffective.
We propose RawHash, the first mechanism that can accurately and efficiently
perform real-time analysis of nanopore raw signals for large genomes using a
hash-based similarity search. To enable this, RawHash ensures the signals
corresponding to the same DNA content lead to the same hash value, regardless
of the slight variations in these signals. RawHash achieves an accurate
hash-based similarity search via an effective quantization of the raw signals
such that signals corresponding to the same DNA content have the same quantized
value and, subsequently, the same hash value.
We evaluate RawHash on three applications: 1) read mapping, 2) relative
abundance estimation, and 3) contamination analysis. Our evaluations show that
RawHash is the only tool that can provide high accuracy and high throughput for
analyzing large genomes in real-time. When compared to the state-of-the-art
techniques, UNCALLED and Sigmap, RawHash provides 1) 25.8x and 3.4x better
average throughput and 2) an average speedup of 32.1x and 2.1x in the mapping
time, respectively.
Source code is available at https://github.com/CMU-SAFARI/RawHash
A Framework for Designing Efficient Deep Learning-Based Genomic Basecallers
Nanopore sequencing generates noisy electrical signals that need to be
converted into a standard string of DNA nucleotide bases using a computational
step called basecalling. The accuracy and speed of basecalling have critical
implications for all later steps in genome analysis. Many researchers adopt
complex deep learning-based models to perform basecalling without considering
the compute demands of such models, which leads to slow, inefficient, and
memory-hungry basecallers. Therefore, there is a need to reduce the computation
and memory cost of basecalling while maintaining accuracy. Our goal is to
develop a comprehensive framework for creating deep learning-based basecallers
that provide high efficiency and performance. We introduce RUBICON, a framework
to develop hardware-optimized basecallers. RUBICON consists of two novel
machine-learning techniques that are specifically designed for basecalling.
First, we introduce the first quantization-aware basecalling neural
architecture search (QABAS) framework to specialize the basecalling neural
network architecture for a given hardware acceleration platform while jointly
exploring and finding the best bit-width precision for each neural network
layer. Second, we develop SkipClip, the first technique to remove the skip
connections present in modern basecallers to greatly reduce resource and
storage requirements without any loss in basecalling accuracy. We demonstrate
the benefits of RUBICON by developing RUBICALL, the first hardware-optimized
basecaller that performs fast and accurate basecalling. Compared to the fastest
state-of-the-art basecaller, RUBICALL provides a 3.96x speedup with 2.97%
higher accuracy. We show that RUBICON helps researchers develop
hardware-optimized basecallers that are superior to expert-designed models
ApHMM: Accelerating Profile Hidden Markov Models for Fast and Energy-Efficient Genome Analysis
Profile hidden Markov models (pHMMs) are widely employed in various
bioinformatics applications to identify similarities between biological
sequences, such as DNA or protein sequences. In pHMMs, sequences are
represented as graph structures. These probabilities are subsequently used to
compute the similarity score between a sequence and a pHMM graph. The
Baum-Welch algorithm, a prevalent and highly accurate method, utilizes these
probabilities to optimize and compute similarity scores. However, the
Baum-Welch algorithm is computationally intensive, and existing solutions offer
either software-only or hardware-only approaches with fixed pHMM designs. We
identify an urgent need for a flexible, high-performance, and energy-efficient
HW/SW co-design to address the major inefficiencies in the Baum-Welch algorithm
for pHMMs.
We introduce ApHMM, the first flexible acceleration framework designed to
significantly reduce both computational and energy overheads associated with
the Baum-Welch algorithm for pHMMs. ApHMM tackles the major inefficiencies in
the Baum-Welch algorithm by 1) designing flexible hardware to accommodate
various pHMM designs, 2) exploiting predictable data dependency patterns
through on-chip memory with memoization techniques, 3) rapidly filtering out
negligible computations using a hardware-based filter, and 4) minimizing
redundant computations.
ApHMM achieves substantial speedups of 15.55x - 260.03x, 1.83x - 5.34x, and
27.97x when compared to CPU, GPU, and FPGA implementations of the Baum-Welch
algorithm, respectively. ApHMM outperforms state-of-the-art CPU implementations
in three key bioinformatics applications: 1) error correction, 2) protein
family search, and 3) multiple sequence alignment, by 1.29x - 59.94x, 1.03x -
1.75x, and 1.03x - 1.95x, respectively, while improving their energy efficiency
by 64.24x - 115.46x, 1.75x, 1.96x.Comment: Accepted to ACM TAC
Tailor: Altering Skip Connections for Resource-Efficient Inference
Deep neural networks use skip connections to improve training convergence.
However, these skip connections are costly in hardware, requiring extra buffers
and increasing on- and off-chip memory utilization and bandwidth requirements.
In this paper, we show that skip connections can be optimized for hardware when
tackled with a hardware-software codesign approach. We argue that while a
network's skip connections are needed for the network to learn, they can later
be removed or shortened to provide a more hardware efficient implementation
with minimal to no accuracy loss. We introduce Tailor, a codesign tool whose
hardware-aware training algorithm gradually removes or shortens a fully trained
network's skip connections to lower their hardware cost. Tailor improves
resource utilization by up to 34% for BRAMs, 13% for FFs, and 16% for LUTs for
on-chip, dataflow-style architectures. Tailor increases performance by 30% and
reduces memory bandwidth by 45% for a 2D processing element array architecture
An In-Memory Architecture for High-Performance Long-Read Pre-Alignment Filtering
With the recent move towards sequencing of accurate long reads, finding
solutions that support efficient analysis of these reads becomes more
necessary. The long execution time required for sequence alignment of long
reads negatively affects genomic studies relying on sequence alignment.
Although pre-alignment filtering as an extra step before alignment was recently
introduced to mitigate sequence alignment for short reads, these filters do not
work as efficiently for long reads. Moreover, even with efficient pre-alignment
filters, the overall end-to-end (i.e., filtering + original alignment)
execution time of alignment for long reads remains high, while the filtering
step is now a major portion of the end-to-end execution time.
Our paper makes three contributions. First, it identifies data movement of
sequences between memory units and computing units as the main source of
inefficiency for pre-alignment filters of long reads. This is because although
filters reject many of these long sequencing pairs before they get to the
alignment stage, they still require a huge cost regarding time and energy
consumption for the large data transferred between memory and processor.
Second, this paper introduces an adaptation of a short-read pre-alignment
filtering algorithm suitable for long reads. We call this LongGeneGuardian.
Finally, it presents Filter-Fuse as an architecture that supports
LongGeneGuardian inside the memory. FilterFuse exploits the
Computation-In-Memory computing paradigm, eliminating the cost of data movement
in LongGeneGuardian.
Our evaluations show that FilterFuse improves the execution time of filtering
by 120.47x for long reads compared to State-of-the-Art (SoTA) filter,
SneakySnake. FilterFuse also improves the end-to-end execution time of sequence
alignment by up to 49.14x and 5207.63x compared to SneakySnake with SoTA
aligner and only SoTA aligner, respectively
Accelerating Time Series Analysis via Processing using Non-Volatile Memories
Time Series Analysis (TSA) is a critical workload for consumer-facing
devices. Accelerating TSA is vital for many domains as it enables the
extraction of valuable information and predict future events. The
state-of-the-art algorithm in TSA is the subsequence Dynamic Time Warping
(sDTW) algorithm. However, sDTW's computation complexity increases
quadratically with the time series' length, resulting in two performance
implications. First, the amount of data parallelism available is significantly
higher than the small number of processing units enabled by commodity systems
(e.g., CPUs). Second, sDTW is bottlenecked by memory because it 1) has low
arithmetic intensity and 2) incurs a large memory footprint. To tackle these
two challenges, we leverage Processing-using-Memory (PuM) by performing in-situ
computation where data resides, using the memory cells. PuM provides a
promising solution to alleviate data movement bottlenecks and exposes immense
parallelism.
In this work, we present MATSA, the first MRAM-based Accelerator for Time
Series Analysis. The key idea is to exploit magneto-resistive memory crossbars
to enable energy-efficient and fast time series computation in memory. MATSA
provides the following key benefits: 1) it leverages high levels of parallelism
in the memory substrate by exploiting column-wise arithmetic operations, and 2)
it significantly reduces the data movement costs performing computation using
the memory cells. We evaluate three versions of MATSA to match the requirements
of different environments (e.g., embedded, desktop, or HPC computing) based on
MRAM technology trends. We perform a design space exploration and demonstrate
that our HPC version of MATSA can improve performance by 7.35x/6.15x/6.31x and
energy efficiency by 11.29x/4.21x/2.65x over server CPU, GPU and PNM
architectures, respectively
Utilising Nanopore technology for interactive real-time metagenomics
Nanopore sequencing technology has the potential to revolutionise metagenomics by providing long reads, which can improve taxonomic classification and assembly contiguity, near real-time analysis, enabling rapid results and improved sequencing efficiency, and portability, allowing sequencing in the field. However, the full potential of these features is largely unrealised due to the lack of available tools and methods. In this thesis, we report on tools and analysis methods that facilitate the use of nanopore sequencing technology for metagenomics and real-time analysis.
Applying metagenomics to samples containing a mix of eukaryote species, such as bee-collected pollen, is challenging due to lack of available reference genomes. This thesis presents a new method, RevMet (Reverse Metagenomics), for semi-quantitative characterisation of mixed eukaryote samples without the need for complete reference genomes. Instead, each reference species is represented by a low-cost genome skim. The short-read reference skims are mapped to the long nanopore query reads to individually classify them, which is the reverse of the standard metagenomic approach of mapping reads to (assembled) references.
Recognising the need for an open-source software tool for real-time analysis and visualisation of metagenomic sequencing data, we developed MARTi (Metagenomic Analysis in Real-Time). MARTi provides a rapid, lightweight web interface that allows users to view community composition and identify antimicrobial resistance genes in real time. MARTi consists of two main parts, the Engine and the GUI, and can be configured in multiple ways to suit the needs of the user. We demonstrate MARTi on live nanopore sequencing runs - firstly, using a mock gut community and, secondly, using clinical faecal gut microbiome samples taken from patients suffering from liver disease
Accelerating pairwise sequence alignment on GPUs using the Wavefront Algorithm
Advances in genomics and sequencing technologies demand faster and more scalable analysis methods that can process longer sequences with higher accuracy. However, classical pairwise alignment methods, based on dynamic programming (DP), impose impractical computational requirements to align long and noisy sequences like those produced by PacBio, and Nanopore technologies. The recently proposed Wavefront Alignment (WFA) algorithm paves the way for more efficient alignment tools, improving time and memory complexity over previous methods. Notwithstanding the advantages of the WFA algorithm, modern high performance computing (HPC) platforms rely on accelerator-based architectures that exploit parallel computing resources to improve over classical computing CPUs. Hence, a GPU-enabled implementation of the WFA could exploit the hardware resources of modern GPUs and further accelerate sequence alignment in current genome analysis pipelines. This thesis presents two GPU-accelerated implementations based on the WFA for fast pairwise DNA sequence alignment: eWFA-GPU and WFA-GPU. Our first proposal, eWFA-GPU, computes the exact edit-distance alignment between two short sequences (up to a few thousand bases), taking full advantage of the massive parallel capabilities of modern GPUs. We propose a succinct representation of the alignment data that successfully reduces the overall amount of memory required, allowing the exploitation of the fast on-chip memory of a GPU. Our results show that eWFA-GPU outperforms by 3-9X the edit-distance WFA implementation running on a 20 core machine. Compared to other state-of-the-art tools computing the edit-distance, eWFA-GPU is up to 265X faster than CPU tools and up to 56 times faster than other GPU-enabled implementations. Our second contribution, the WFA-GPU tool, extends the work of eWFA-GPU to compute the exact gap-affine distance (i.e., a more general alignment problem) between arbitrary long sequences. In this work, we propose a CPU-GPU co-design capable of performing inter and intra-sequence parallel alignment of multiple sequences, combining a succinct WFA-data representation with an efficient GPU implementation. As a result, we demonstrate that our implementation outperforms the original WFA implementation between 1.5-7.7X times when computing the alignment path, and between 2.6-16X when computing only the alignment score. Moreover, compared to other state-of-the-art tools, the WFA-GPU is up to 26.7X faster than other GPU implementations and up to four orders of magnitude faster than other CPU implementations