Parallel Implementation of a Machine Learning Algorithm on GPU

Cuomo, Salvatore; Michele, Pasquale De; Nardo, Emanuel Di; Marcellino, Livia

doi:10.1007/s10766-017-0554-6

Cited by 5 publications

(3 citation statements)

References 36 publications

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“…It is the most indispensable and important technology in the field of AI. From a professional perspective, ML has highly developed perception and parallel information processing ability [23]. Some traditional and advanced ML algorithms are summarized as follows (Fig.…”

Section: Machine Learningmentioning

confidence: 99%

Intelligent urbanism with artificial intelligence in shaping tomorrow’s smart cities: current developments, trends, and future directions

Yan,

Jiang,

Huang

et al. 2023

J Cloud Comp

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Abstract21st century has witnessed a profound metamorphosis in human civilization, primarily driven by the confluence of advanced network technologies and industrial modernization. This transformative period has expanded our understanding of the world, paving the way for innovative concepts such as the “smart city”. At its essence, a smart city harnesses the power of artificial intelligence (AI) to revolutionize urban living, presenting a paradigm shift towards more efficient service models and an elevated standard of living for its inhabitants. Integrating AI into the fabric of urban infrastructure marks a monumental leap in societal evolution, underscoring the imperative to cultivate and advance AI technologies. This paper endeavors to elucidate the multifaceted applications of AI within the domains of smart cities, illuminating its pivotal role in shaping and advancing our contemporary era. From intelligent transportation systems and energy management to public safety and healthcare, AI permeates various aspects of urban life, ushering in unprecedented efficiencies and novel solutions to age-old challenges. The symbiotic relationship between AI and smart cities is explored in detail, showcasing how AI technologies are instrumental in optimizing resource allocation, improving decision-making processes, and ultimately enhancing the overall quality of life. Furthermore, this paper delves into the imperative of fostering the development and advancement of AI technologies within the context of smart cities. It underscores the interconnectedness of technological progress and urban development, emphasizing how a concerted effort to cultivate AI capabilities can propel cities into a future marked by sustainable growth, resilience, and innovation. The exploration of challenges and opportunities in deploying AI within urban environments adds a critical dimension to the discourse, encouraging a balanced consideration of ethical, regulatory, and societal implications. In conclusion, this paper seeks to contribute to the ongoing dialogue surrounding smart cities and the transformative impact of AI. By shedding light on the diverse applications of AI within urban landscapes and emphasizing its pivotal role in shaping the trajectory of our era, it underscores the critical importance of advancing AI technology development for the continued progress of smart cities and, by extension, the broader global community.

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Section: Machine Learningmentioning

confidence: 99%

Intelligent urbanism with artificial intelligence in shaping tomorrow’s smart cities: current developments, trends, and future directions

Yan,

Jiang,

Huang

et al. 2023

J Cloud Comp

View full text Add to dashboard Cite

show abstract

“…[19] algorithm used a two-list method to solved problems on a GPU using CUDA (Compute Unified Device Architecture) and [10] parallelized the Doolittle algorithm on multicores and achieved a performance better than the serial version of the Doolittle algorithm. [20] also implemented a parallel version of self-organizing maps algorithm (SOM) on a parallel architecture and achieved good performance against the CPU version.…”

Section: Some Parallel Scheduling Attemptsmentioning

confidence: 99%

Parallel Scheduling of Grid Jobs on Quadcore Systems using Grouping Methods

Abraham

Osaisai

Dienagha

2021

AJRCoS

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As Grid computing continues to make inroads into different spheres of our lives and multicore computers become ubiquitous, the need to leverage the gains of multicore computers for the scheduling of Grid jobs becomes a necessity. Most Grid schedulers remain sequential in nature and are inadequate in meeting up with the growing data and processing need of the Grid. Also, the leakage of Moore’s dividend continues as most computing platforms still depend on the underlying hardware for increased performance. Leveraging the Grid for the data challenge of the future requires a shift away from the traditional sequential method. This work extends the work of [1] on a quadcore system. A random method was used to group machines and the total processing power of machines in each group was computed, a size proportional to speed method is then used to estimates the size of jobs for allocation to machine groups. The MinMin scheduling algorithm was implemented within the groups to schedule a range of jobs while varying the number of groups and threads. The experiment was executed on a single processor system and on a quadcore system. Significant improvement was achieved using the group method on the quadcore system compared to the ordinary MinMin on the quadcore. We also find significant performance improvement with increasing groups. Thirdly, we find that the MinMin algorithm also gained marginally from the quadcore system meaning that it is also scalable.

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“…The parallel programming models using GPUs include CUDA (Compute Unified Device Architecture), OpenCL (Open Computing Language), DirectCompute and many other approaches. Cuomo et al [10] proposed a novel parallel implementation of self-organization map (SOM) neural networks on CUDA-GPU architectures. This approach uses the latest cuBLAS library from NVIDIA to achieve fast accelerated execution of standard linear algebra subroutines.…”

Section: Introductionmentioning

confidence: 99%

A novel parallel learning algorithm for pattern classification

Wang

Wei

2019

SN Appl. Sci.

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In today's data-intensive applications, machine learning constructs algorithms that are capable of learning and making predications on the data. Margin setting algorithm (MSA) is a novel machine learning algorithm for pattern classification. It employs an artificial immune system approach to generates prototype regions as the classification boundaries. However, its computation time limited its applications in real-world application. When the datasets grow in size and algorithm complexity increases, it is necessary to spread the work among multiple cores and processors. To reduce the execution time during classification, a parallel implementation of MSA, called PMSA is proposed for multicore and multiprocessor system. It is the first work to scale up the classification time of MSA using parallel implementation. To evaluate the proposed PMSA algorithm, we used standard image datasets of 512 × 512 pixels and 321 × 481 pixels. Besides, benchmark datasets from University of California, Irvine Machine Learning Repository are also used. They are 768 data samples from dataset Pima Indian Diabetes, 683 data samples from dataset Wisconsin Breast Cancer, 690 data samples from dataset Australian Credit Approval, 178 data samples from dataset Wine and 391 data samples from dataset Svmguide2. The classification performance is compared with another two state-of-the-art classification algorithms: the artificial neural network and the support vector machine. The results show the proposed PMSA gains significant improvements in terms of execution time, with a promising speedup compared to the single-threaded CPU counterpart.

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Parallel Implementation of a Machine Learning Algorithm on GPU

Cited by 5 publications

References 36 publications

Intelligent urbanism with artificial intelligence in shaping tomorrow’s smart cities: current developments, trends, and future directions

Intelligent urbanism with artificial intelligence in shaping tomorrow’s smart cities: current developments, trends, and future directions

Parallel Scheduling of Grid Jobs on Quadcore Systems using Grouping Methods

A novel parallel learning algorithm for pattern classification

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