Optimal Worksharing of DNA Sequence Analysis on Accelerated Platforms

Memeti, Suejb; Pllana, Sabri; Kołodziej, Joanna

doi:10.1007/978-3-319-44881-7_14

Cited by 4 publications

(6 citation statements)

References 40 publications

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“…The activities associated with conducting the literature review include: (1) identifying Final set of scientific publications (65) Literature Search and Seleciton Process Fig. 3 The process of searching and selecting the relevant literature the research, (2) literature selection, (3) data extraction and synthesis.…”

Section: Methodsmentioning

confidence: 99%

“…Selecting the most suitable heuristic for a specific problem is important to reach a near-optimal solution more quickly. However, this process requires consideration of various factors, such as the domain type, search space, computational time, and solution quality [12,65]. In the context of software optimization, the commonly used meta-heuristics include Genetic Algorithms, Simulated Annealing, Ant Colony Optimization, Local Search, Tabu Search, and Particle Swarm Optimization (see Fig.…”

Section: Meta-heuristicsmentioning

confidence: 99%

“…With regards to static scheduling, the attention of recent research that use machine learning and meta-heuristics is in the following optimization objectives: mapping program parallelism to multi-core architectures [98], mapping applications to the most appropriate processing device [40,71], mapping threads to specific cores [15], and determining workload distribution on heterogeneous parallel computing systems [62][63][64][65].…”

Section: Rq1: Software Optimization Goals For Run-time Static Schedulmentioning

confidence: 99%

Section: Meta-heuristicsmentioning

confidence: 99%

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Using meta-heuristics and machine learning for software optimization of parallel computing systems: a systematic literature review

et al. 2018

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While modern parallel computing systems offer high performance, utilizing these powerful computing resources to the highest possible extent demands advanced knowledge of various hardware architectures and parallel programming models. Furthermore, optimized software execution on parallel computing systems demands consideration of many parameters at compile-time and run-time. Determining the optimal set of parameters in a given execution context is a complex task, and therefore to address this issue researchers have proposed different approaches that use heuristic search or machine learning. In this paper, we undertake a systematic literature review to aggregate, analyze and classify the existing software optimization methods for parallel computing systems. We review approaches that use machine learning or meta-heuristics for software optimization at compile-time and run-time. we discuss challenges and future research directions. The results of this study may help to better understand the state-of-the-art techniques that use machine learning and meta-heuristics to deal with the complexity of software optimization for parallel computing systems. Furthermore, it may aid in understanding the limitations of existing approaches and identification of areas for improvement.

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Section: Methodsmentioning

confidence: 99%

Section: Meta-heuristicsmentioning

confidence: 99%

Section: Rq1: Software Optimization Goals For Run-time Static Schedulmentioning

confidence: 99%

Section: Meta-heuristicsmentioning

confidence: 99%

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Using meta-heuristics and machine learning for software optimization of parallel computing systems: a systematic literature review

et al. 2018

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“…In contrast to the related work, we focus on performance modeling of training deep convolutional neural networks on the Intel Xeon Phi many-core processor. In our previous work [35] we used machine learning for performance prediction of DNA sequence analysis [36], [37] on Intel Xeon Phi .…”

Section: Related Workmentioning

confidence: 99%

Performance Modelling of Deep Learning on Intel Many Integrated Core Architectures

Viebke¹,

Pllana²,

Memeti³

et al. 2019

Preprint

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Many complex problems, such as natural language processing or visual object detection, are solved using deep learning. However, efficient training of complex deep convolutional neural networks for large data sets is computationally demanding and requires parallel computing resources. In this paper, we present two parameterized performance models for estimation of execution time of training convolutional neural networks on the Intel many integrated core architecture. While for the first performance model we minimally use measurement techniques for parameter value estimation, in the second model we estimate more parameters based on measurements. We evaluate the prediction accuracy of performance models in the context of training three different convolutional neural network architectures on the Intel Xeon Phi. The achieved average performance prediction accuracy is about 15% for the first model and 11% for second model.

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Stochastic Bounds for Markov Chains on Intel Xeon Phi Coprocessor

Bylina

2018

Parallel Processing and Applied Mathematics

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Optimal Worksharing of DNA Sequence Analysis on Accelerated Platforms

Cited by 4 publications

References 40 publications

Using meta-heuristics and machine learning for software optimization of parallel computing systems: a systematic literature review

Using meta-heuristics and machine learning for software optimization of parallel computing systems: a systematic literature review

Performance Modelling of Deep Learning on Intel Many Integrated Core Architectures

Stochastic Bounds for Markov Chains on Intel Xeon Phi Coprocessor

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