GPU-DAEMON: GPU algorithm design, data management &amp; optimization template for array based big omics data

Awan, Muaaz Gul; Eslami, Taban; Saeed, Fahad

doi:10.1016/j.compbiomed.2018.08.015

Cited by 11 publications

(5 citation statements)

References 20 publications

(33 reference statements)

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“…The register file size for each SM is 256 KB. A better use of a GPU's memory hierarchy can yield considerably better application performance [29].…”

Section: Graphics Processing Unitsmentioning

confidence: 99%

ADEPT: a domain independent sequence alignment strategy for gpu architectures

et al. 2020

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Background Bioinformatic workflows frequently make use of automated genome assembly and protein clustering tools. At the core of most of these tools, a significant portion of execution time is spent in determining optimal local alignment between two sequences. This task is performed with the Smith-Waterman algorithm, which is a dynamic programming based method. With the advent of modern sequencing technologies and increasing size of both genome and protein databases, a need for faster Smith-Waterman implementations has emerged. Multiple SIMD strategies for the Smith-Waterman algorithm are available for CPUs. However, with the move of HPC facilities towards accelerator based architectures, a need for an efficient GPU accelerated strategy has emerged. Existing GPU based strategies have either been optimized for a specific type of characters (Nucleotides or Amino Acids) or for only a handful of application use-cases. Results In this paper, we present ADEPT, a new sequence alignment strategy for GPU architectures that is domain independent, supporting alignment of sequences from both genomes and proteins. Our proposed strategy uses GPU specific optimizations that do not rely on the nature of sequence. We demonstrate the feasibility of this strategy by implementing the Smith-Waterman algorithm and comparing it to similar CPU strategies as well as the fastest known GPU methods for each domain. ADEPT’s driver enables it to scale across multiple GPUs and allows easy integration into software pipelines which utilize large scale computational systems. We have shown that the ADEPT based Smith-Waterman algorithm demonstrates a peak performance of 360 GCUPS and 497 GCUPs for protein based and DNA based datasets respectively on a single GPU node (8 GPUs) of the Cori Supercomputer. Overall ADEPT shows 10x faster performance in a node-to-node comparison against a corresponding SIMD CPU implementation. Conclusions ADEPT demonstrates a performance that is either comparable or better than existing GPU strategies. We demonstrated the efficacy of ADEPT in supporting existing bionformatics software pipelines by integrating ADEPT in MetaHipMer a high-performance denovo metagenome assembler and PASTIS a high-performance protein similarity graph construction pipeline. Our results show 10% and 30% boost of performance in MetaHipMer and PASTIS respectively.

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“…The register file size for each SM is 256 KB. A better use of a GPU's memory hierarchy can yield considerably better application performance [29].…”

Section: Graphics Processing Unitsmentioning

confidence: 99%

ADEPT: a domain independent sequence alignment strategy for gpu architectures

et al. 2020

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“…The term “big data” often indicates a high number of instances as well as features [ 1 , 2 ]. Typical big data comprise a few million images (photos) each composed of several million pixels.…”

Section: Introductionmentioning

confidence: 99%

Tensor-Decomposition-Based Unsupervised Feature Extraction Applied to Prostate Cancer Multiomics Data

Taguchi

Turki

2020

Genes

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The large p small n problem is a challenge without a de facto standard method available to it. In this study, we propose a tensor-decomposition (TD)-based unsupervised feature extraction (FE) formalism applied to multiomics datasets, in which the number of features is more than 100,000 whereas the number of samples is as small as about 100, hence constituting a typical large p small n problem. The proposed TD-based unsupervised FE outperformed other conventional supervised feature selection methods, random forest, categorical regression (also known as analysis of variance, or ANOVA), penalized linear discriminant analysis, and two unsupervised methods, multiple non-negative matrix factorization and principal component analysis (PCA) based unsupervised FE when applied to synthetic datasets and four methods other than PCA based unsupervised FE when applied to multiomics datasets. The genes selected by TD-based unsupervised FE were enriched in genes known to be related to tissues and transcription factors measured. TD-based unsupervised FE was demonstrated to be not only the superior feature selection method but also the method that can select biologically reliable genes. To our knowledge, this is the first study in which TD-based unsupervised FE has been successfully applied to the integration of this variety of multiomics measurements.

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“…The term “big data” often indicates a high number of instances as well as features (Richter and Khoshgoftaar, 2019; Awan et al, 2018). Typical big data comprise a few million images (photos) each composed of several million pixels.…”

Section: Introductionmentioning

confidence: 99%

Tensor-decomposition-based unsupervised feature extraction applied to prostate cancer multiomics data

Taguchi

Turki

2020

Preprint

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Large p small n problem is a challenging problem in big data analytics. There are no de facto standard methods available to it. In this study, we propose a tensor decomposition (TD) based unsupervised feature extraction (FE) formalism applied to multiomics datasets, where the number of features is more than 100000 while the number of instances is as small as about 100. The proposed TD based unsupervised FE outperformed other conventional supervised feature selection methods, such as random forest, categorical regression (also known as analysis of variance, ANOVA), and penalized linear discriminant analysis when they are applied to not only multiomics datasets but also synthetic datasets. Genes selected by TD based unsupervised FE were biologically reliable. TD based unsupervised FE turned out to be not only the superior feature selection method but also the method that can select biologically reliable genes.

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GPU-DAEMON: GPU algorithm design, data management & optimization template for array based big omics data

Cited by 11 publications

References 20 publications

ADEPT: a domain independent sequence alignment strategy for gpu architectures

ADEPT: a domain independent sequence alignment strategy for gpu architectures

Tensor-Decomposition-Based Unsupervised Feature Extraction Applied to Prostate Cancer Multiomics Data

Tensor-decomposition-based unsupervised feature extraction applied to prostate cancer multiomics data

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