64 research outputs found
FAssem : FPGA based Acceleration of De Novo Genome Assembly
International audienceNext generation sequencing technologies produce large amounts of data at very low cost. They produce short reads of DNA fragments. These fragments have many overlaps, lots of repeats and may also include sequencing errors. The assembly process involves merging these sequences to form the original sequences. In recent years many software programs have been developed for this purpose. All of them take significant amount of time to execute. Velvet is a commonly used de novo assembly program. We propose a method to reduce the overall time for assembly by using pre-processing of the short read data on FPGAs and processing its output using Velvet. We show significant speed-ups with slight or no compromise on the quality of the assembled output
The Parallelism Motifs of Genomic Data Analysis
Genomic data sets are growing dramatically as the cost of sequencing
continues to decline and small sequencing devices become available. Enormous
community databases store and share this data with the research community, but
some of these genomic data analysis problems require large scale computational
platforms to meet both the memory and computational requirements. These
applications differ from scientific simulations that dominate the workload on
high end parallel systems today and place different requirements on programming
support, software libraries, and parallel architectural design. For example,
they involve irregular communication patterns such as asynchronous updates to
shared data structures. We consider several problems in high performance
genomics analysis, including alignment, profiling, clustering, and assembly for
both single genomes and metagenomes. We identify some of the common
computational patterns or motifs that help inform parallelization strategies
and compare our motifs to some of the established lists, arguing that at least
two key patterns, sorting and hashing, are missing
Considerations in using OpenCL on GPUs and FPGAs for throughput-oriented genomics workloads
The recent upsurge in the available amount of health data and the advances in next-generation sequencing are setting the ground for the long-awaited precision medicine. To process this deluge of data, bioinformatics workloads are becoming more complex and more computationally demanding. For this reasons they have been extended to support different computing architectures, such as GPUs and FPGAs, to leverage the form of parallelism typical of each of such architectures. The paper describes how a genomic workload such as k-mer frequency counting that takes advantage of a GPU can be offloaded to one or even more FPGAs. Moreover, it performs a comprehensive analysis of the FPGA acceleration comparing its performance to a non-accelerated configuration and when using a GPU. Lastly, the paper focuses on how, when using accelerators with a throughput-oriented workload, one should also take into consideration both kernel execution time and how well each accelerator board overlaps kernels and PCIe transferred. Results show that acceleration with two FPGAs can improve both time- and energy-to-solution for the entire accelerated part by a factor of 1.32x. Per contra, acceleration with one GPU delivers an improvement of 1.77x in time-to-solution but of a lower 1.49x in energy-to-solution due to persistently higher power consumption. The paper also evaluates how future FPGA boards with components (i.e., off-chip memory and PCIe) on par with those of the GPU board could provide an energy-efficient alternative to GPUs.Peer ReviewedPostprint (published version
Computing Platforms for Big Biological Data Analytics: Perspectives and Challenges.
The last decade has witnessed an explosion in the amount of available biological sequence data, due to the rapid progress of high-throughput sequencing projects. However, the biological data amount is becoming so great that traditional data analysis platforms and methods can no longer meet the need to rapidly perform data analysis tasks in life sciences. As a result, both biologists and computer scientists are facing the challenge of gaining a profound insight into the deepest biological functions from big biological data. This in turn requires massive computational resources. Therefore, high performance computing (HPC) platforms are highly needed as well as efficient and scalable algorithms that can take advantage of these platforms. In this paper, we survey the state-of-the-art HPC platforms for big biological data analytics. We first list the characteristics of big biological data and popular computing platforms. Then we provide a taxonomy of different biological data analysis applications and a survey of the way they have been mapped onto various computing platforms. After that, we present a case study to compare the efficiency of different computing platforms for handling the classical biological sequence alignment problem. At last we discuss the open issues in big biological data analytics
Algorithm-Hardware Co-Design for Performance-driven Embedded Genomics
PhD ThesisGenomics includes development of techniques for diagnosis, prognosis and therapy of
over 6000 known genetic disorders. It is a major driver in the transformation of medicine
from the reactive form to the personalized, predictive, preventive and participatory (P4)
form. The availability of genome is an essential prerequisite to genomics and is obtained
from the sequencing and analysis pipelines of the whole genome sequencing (WGS).
The advent of second generation sequencing (SGS), significantly, reduced the sequencing
costs leading to voluminous research in genomics. SGS technologies, however, generate
massive volumes of data in the form of reads, which are fragmentations of the real
genome. The performance requirements associated with mapping reads to the reference
genome (RG), in order to reassemble the original genome, now, stands disproportionate
to the available computational capabilities. Conventionally, the hardware resources used
are made of homogeneous many-core architecture employing complex general-purpose
CPU cores. Although these cores provide high-performance, a data-centric approach
is required to identify alternate hardware systems more suitable for affordable and
sustainable genome analysis.
Most state-of-the-art genomic tools are performance oriented and do not address
the crucial aspect of energy consumption. Although algorithmic innovations have
reduced runtime on conventional hardware, the energy consumption has scaled poorly.
The associated monetary and environmental costs have made it a major bottleneck to
translational genomics. This thesis is concerned with the development and validation
of read mappers for embedded genomics paradigm, aiming to provide a portable and
energy-efficient hardware solution to the reassembly pipeline. It applies the algorithmhardware co-design approach to bridge the saturation point arrived in algorithmic
innovations with emerging low-power/energy heterogeneous embedded platforms.
Essential to embedded paradigm is the ability to use heterogeneous hardware
resources. Graphical processing units (GPU) are, often, available in most modern devices
alongside CPU but, conventionally, state-of-the-art read mappers are not tuned to use
both together. The first part of the thesis develops a Cross-platfOrm Read mApper
using opencL (CORAL) that can distribute workload on all available devices for high
performance. OpenCL framework mitigates the need for designing separate kernels for
CPU and GPU. It implements a verification-aware filtration algorithm for rapid pruning
and identification of candidate locations for mapping reads to the RG.
Mapping reads on embedded platforms decreases performance due to architectural
differences such as limited on-chip/off-chip memory, smaller bandwidths and simpler
cores. To mitigate performance degradation, in second part of the thesis, we propose a
REad maPper for heterogeneoUs sysTEms (REPUTE) which uses an efficient dynamic
programming (DP) based filtration methodology. Using algorithm-hardware co-design
and kernel level optimizations to reduce its memory footprint, REPUTE demonstrated
significant energy savings on HiKey970 embedded platform with acceptable performance.
The third part of the thesis concentrates on mapping the whole genome on an
embedded platform. We propose a Pyopencl based tooL for gEnomic workloaDs
tarGeting Embedded platfoRms (PLEDGER) which includes two novel contributions.
The first one proposes a novel preprocessing strategy to generate low-memory footprint
(LMF) data structure to fit all human chromosomes at the cost of performance. Second
contribution is LMF DP-based filtration method to work in conjunction with the
proposed data structures. To mitigate performance degradation, the kernel employs
several optimisations including extensive usage of bit-vector operations. Extensive
experiments using real human reads were carried out with state-of-the-art read mappers
on 5 different platforms for CORAL, REPUTE and PLEDGER. The results show that
embedded genomics provides significant energy savings with similar performance
compared to conventional CPU-based platforms
QuASeR -- Quantum Accelerated De Novo DNA Sequence Reconstruction
In this article, we present QuASeR, a reference-free DNA sequence
reconstruction implementation via de novo assembly on both gate-based and
quantum annealing platforms. Each one of the four steps of the implementation
(TSP, QUBO, Hamiltonians and QAOA) is explained with simple proof-of-concept
examples to target both the genomics research community and quantum application
developers in a self-contained manner. The details of the implementation are
discussed for the various layers of the quantum full-stack accelerator design.
We also highlight the limitations of current classical simulation and available
quantum hardware systems. The implementation is open-source and can be found on
https://github.com/prince-ph0en1x/QuASeR.Comment: 24 page
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