GPU Computation in Bioinspired Algorithms: A Review

Arenas, M. G.; Mora, Antonio M.; Romero, Gustavo; Castillo, Pedro

doi:10.1007/978-3-642-21501-8_54

Cited by 12 publications

(6 citation statements)

References 16 publications

(16 reference statements)

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“…One example HoMCP architecture is found in GPUs, which are designed as special‐purpose processors for visual processing [81]. Modern GPUs may incorporate hundreds of cores, which are carbon copies of one another, in order to achieve parallel processing by handling thousands of threads simultaneously [82].…”

Section: Heterogeneous Multi‐core Realitymentioning

confidence: 99%

Amdahl's law in the context of heterogeneous many‐core systems – a survey

Al-hayanni

Xia

Rafiev

et al. 2020

IET Computers & Digital Techniques

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For over 50 years, Amdahl's Law has been the hallmark model for reasoning about performance bounds for homogeneous parallel computing resources. As heterogeneous, many-core parallel resources continue to permeate into the modern server and embedded domains, there has been growing interest in promulgating realistic extensions and assumptions in keeping with newer use cases. This study aims to provide a comprehensive review of the purviews and insights provided by the extensive body of work related to Amdahl's law to date, focusing on computation speedup. The authors show that a significant portion of these studies has looked into analysing the scalability of the model considering both workload and system heterogeneity in real-world applications. The focus has been to improve the definition and semantic power of the two key parameters in the original model: the parallel fraction (f) and the computation capability improvement index (n). More recently, researchers have shown normal-form and multi-fraction extensions that can account for wider ranges of heterogeneity, validated on many-core systems running realistic workloads. Speedup models from Amdahl's law onwards have seen a wide range of uses, such as the optimisation of system execution, and these uses are even more important with the advent of the heterogeneous many-core era.

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Section: Heterogeneous Multi‐core Realitymentioning

confidence: 99%

Amdahl's law in the context of heterogeneous many‐core systems – a survey

Al-hayanni

Xia

Rafiev

et al. 2020

IET Computers & Digital Techniques

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“…Giải thuật di truyền cũng được sử dụng để giải quyết các vấn đề khác nhau trong khoa học và kỹ thuật 7 . Theo nghiên cứu của Arenas và cộng sự 8 GAMIC_distributed gọi thủ tục GA_Evolution() và các hàm di truyền khác (Crossover, Evaluation, Selection, Mutation). Các hàm gồm khởi tạo nhiễm sắc thể, đánh giá về fitness, lai chéo, đột biến và lựa chọn sẽ được thực hiện trên KNC.…”

Section: Giải Thuật DI Truyềnunclassified

Parallel approaches of genetic algorithm in the MIC architecture of the Intel Xeon Phi

Hưng

Nguyen

Thoai

2020

Sci. Tech. Dev. J.- Eng. Tech.

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Today, genetic algorithms are widely used in many fields such as bioinformatics, computer science, artificial intelligence, finance ... Genetic algorithms are applied to create high quality solutions for complex optimization problems in the above industries. There have been many studies based on the proposed new hardware architecture that aims to speed up the execution of genetic algorithms as quickly as possible. Some studies suggest parallel genetic algorithms on systems with multicore CPUs and / or graphics processing units (GPUs). However, very few solutions propose a genetic algorithm that can be run on systems that use the new Intel Xeon Phi co-processor (Intel Many-Integrated Core (MIC) architecture). For that reason, we propose and develop the study of the genetic algorithm on high-performance computing systems with Intel Xeon Phi co-processors. This study will present the results of parallel approaches of genetic algorithm on one and more Intel Xeon Phi co-processors by the following methods: (i) Intel Xeon Phi programming model Offload and Native; and (ii) a combined model of MPI and OpenMP. The proposed genetic algorithm can find the optimal schedule for the energy-efficient scheduling problem of virtual machines on physical machines with the goal of minimization total energy consumption. The results of the simulations show the feasibility of implementing a genetic algorithm on one or many Intel Xeon Phi. Genetic algorithm on one or more distributed Intel Xeon Phi always results in faster algorithm execution time than sequential genetic algorithm and the ability to find better solutions using more Intel Xeon Phi. This research result can be applied to other meta-heuristic like TABU search, Ant Colony Optimization.

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“…Finally, GPUs were created to process graphics, especially for gaming and design. Some researchers programmed GPUs using API, but this was a difficult task [ 33 ]. In 2007, NVIDIA published “Compute Unified Device Architecture” (CUDA) [ 34 ], a programming language based on C to optimize GPGPU application.…”

Section: Introductionmentioning

confidence: 99%

Deep Artificial Neural Networks and Neuromorphic Chips for Big Data Analysis: Pharmaceutical and Bioinformatics Applications

Pastur-Romay

Cedrón

Pazos

et al. 2016

IJMS

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Over the past decade, Deep Artificial Neural Networks (DNNs) have become the state-of-the-art algorithms in Machine Learning (ML), speech recognition, computer vision, natural language processing and many other tasks. This was made possible by the advancement in Big Data, Deep Learning (DL) and drastically increased chip processing abilities, especially general-purpose graphical processing units (GPGPUs). All this has created a growing interest in making the most of the potential offered by DNNs in almost every field. An overview of the main architectures of DNNs, and their usefulness in Pharmacology and Bioinformatics are presented in this work. The featured applications are: drug design, virtual screening (VS), Quantitative Structure–Activity Relationship (QSAR) research, protein structure prediction and genomics (and other omics) data mining. The future need of neuromorphic hardware for DNNs is also discussed, and the two most advanced chips are reviewed: IBM TrueNorth and SpiNNaker. In addition, this review points out the importance of considering not only neurons, as DNNs and neuromorphic chips should also include glial cells, given the proven importance of astrocytes, a type of glial cell which contributes to information processing in the brain. The Deep Artificial Neuron–Astrocyte Networks (DANAN) could overcome the difficulties in architecture design, learning process and scalability of the current ML methods.

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GPU Computation in Bioinspired Algorithms: A Review

Cited by 12 publications

References 16 publications

Amdahl's law in the context of heterogeneous many‐core systems – a survey

Amdahl's law in the context of heterogeneous many‐core systems – a survey

Parallel approaches of genetic algorithm in the MIC architecture of the Intel Xeon Phi

Deep Artificial Neural Networks and Neuromorphic Chips for Big Data Analysis: Pharmaceutical and Bioinformatics Applications

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