Analyzing Large-Scale DNA Sequences on Multi-core Architectures

Memeti, Suejb; Pllana, Sabri

doi:10.1109/cse.2015.25

Cited by 7 publications

(9 citation statements)

References 30 publications

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“…The process of finding the PIS is shown in G E T _ P I S function of Algorithm 1 (for details, please refer to ). The matching process (see Algorithm 1 function M A T C H ) is straight forward; the automaton moves from one state to another when a single character of the input is consumed.…”

Section: Methodsmentioning

confidence: 99%

“…It removes the states that do not contain input and output transitions with the corresponding characters on input split. Thereafter, for the first state in the set of possible initial states (PIS), it performs a complete regular expression matching, whereas for the remaining states we use the converging point . Researchers have proposed different approaches to find the initial state, for example, a speculation approach based on the most visited states is proposed by Luchaup et al .…”

Section: Methodsmentioning

confidence: 99%

“…Let us consider that the optimal system configuration is determined by a set of parameters C = { c 1 , c 2 ,.., c m }, where each parameter c i has a value range

R_{c_{i}}

. The number of possible system configurations is a product of parameter value ranges,

\prod_{i = 1}^{m} R_{c_{i}} = R_{c_{1}} \times R_{c_{2}} \times . . \times R_{c_{m}} .

Preliminary results of our algorithm for parallel DNA sequence analysis on homogeneous multi‐core architectures are discussed in . In this paper, we have extended our parallel algorithm for DNA sequence analysis to efficiently use all resources of host and device of heterogeneous systems.…”

Section: Introductionmentioning

confidence: 99%

“…Preliminary results of our algorithm for parallel DNA sequence analysis on homogeneous multicore architectures are discussed in [21]. In this paper, we have extended our parallel algorithm for DNA sequence analysis to efficiently use all resources of host and device of heterogeneous systems.…”

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confidence: 99%

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Combinatorial optimization of DNA sequence analysis on heterogeneous systems

Memeti

Pllana

2016

Concurrency and Computation

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Summary Analysis of DNA sequences is a data and computational intensive problem, and therefore, it requires suitable parallel computing resources and algorithms. In this paper, we describe our parallel algorithm for DNA sequence analysis that determines how many times a pattern appears in the DNA sequence. The algorithm is engineered for heterogeneous platforms that comprise a host with multi‐core processors and one or more many‐core devices. For combinatorial optimization, we use the simulated annealing algorithm. The optimization goal is to determine the number of threads, thread affinities, and DNA sequence fractions for host and device, such that the overall execution time of DNA sequence analysis is minimized. We evaluate our approach experimentally using real‐world DNA sequences of various organisms on a heterogeneous platform that comprises two Intel Xeon E5 processors and an Intel Xeon Phi 7120P co‐processing device. By running only about 5% of possible experiments, our optimization method finds a near‐optimal system configuration for DNA sequence analysis that yields with average speedup of 1.6 × and 2 × compared with the host‐only and device‐only execution. Copyright © 2016 John Wiley & Sons, Ltd.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

“…Let us consider that the optimal system configuration is determined by a set of parameters C = { c 1 , c 2 ,.., c m }, where each parameter c i has a value range

R_{c_{i}}

. The number of possible system configurations is a product of parameter value ranges,

\prod_{i = 1}^{m} R_{c_{i}} = R_{c_{1}} \times R_{c_{2}} \times . . \times R_{c_{m}} .

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Combinatorial optimization of DNA sequence analysis on heterogeneous systems

Memeti

Pllana

2016

Concurrency and Computation

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“…Nowadays, a huge amount of data is generated throughout various mechanisms, such as scientific measurement and experiments (including genetics, physics, and astronomy), social media (including Facebook, and Twitter), and health-care [6,10]. The current challenges of big data include storing and processing very large files.…”

Section: Introductionmentioning

confidence: 99%

HSTREAM: A Directive-Based Language Extension for Heterogeneous Stream Computing

Memeti

Pllana

2018

2018 IEEE International Conference on Computational Science and Engineering (CSE)

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Big data streaming applications require utilization of heterogeneous parallel computing systems, which may comprise multiple multi-core CPUs and many-core accelerating devices such as NVIDIA GPUs and Intel Xeon Phis. Programming such systems require advanced knowledge of several hardware architectures and device-specific programming models, including OpenMP and CUDA. In this paper, we present HSTREAM, a compiler directive-based language extension to support programming stream computing applications for heterogeneous parallel computing systems. HSTREAM source-to-source compiler aims to increase the programming productivity by enabling programmers to annotate the parallel regions for heterogeneous execution and generate target specific code. The HSTREAM runtime automatically distributes the workload across CPUs and accelerating devices. We demonstrate the usefulness of HSTREAM language extension with various applications from the STREAM benchmark. Experimental evaluation results show that HSTREAM can keep the same programming simplicity as OpenMP, and the generated code can deliver performance beyond what CPUs-only and GPUs-only executions can deliver.

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