High-Performance Parallel Computing with Cloud and Cloud Technologies

Ekanayake, Jaliya; Qiu, Xiaohong; Gunarathne, Thilina; Beason, Scott; Fox, Geoffrey

doi:10.1201/ebk1439803158-c12

Cited by 21 publications

(6 citation statements)

References 26 publications

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“…Therefore, this application primarily concerned to develop an algorithm to forecast the Cryptocurrencies price prediction accuracy. Numerous research experts discussed cloud computing [15,17], Deep learning algorithms [18], cryptocurrency price prediction, Bitcoin [6,7,9] and data parallelism [19,20] separately as three different topics. Thus, it has a potential and significant correlation between these three approaches and can be experimented together to explain precise model for the Cryptocurrencies price prediction.…”

Section: Discussionmentioning

confidence: 99%

“…The experimental results confirmed that the GPU4with data parallelism and the GPU8with data parallelism models can reduce the computation time which is approximately within 30 minutes for the large batch sizes. Few authors applied the Deep Learning approach with the parallel neural network [17], data parallelism [15] and Parallel Consensual Neural Networks [20] to reduce the computation time. Similarly, [19] has discussed the effect of traffic flow in cloud computing for the computation time using different types of parallel architectures.…”

Section: Discussionmentioning

confidence: 99%

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Real-Time Cryptocurrency Price Prediction by Exploiting IoT Concept and Beyond: Cloud Computing, Data Parallelism and Deep Learning

Premarathne¹,

Halgamuge²,

Samarakody³

et al. 2020

IJACSA

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Cryptocurrency has as of late pulled in extensive consideration in the fields of economics, cryptography, and computer science due to it is an encrypted digital currency, peerto-peer virtual forex produced using codes, and it is much the same as another medium of the trade like real cash. This study mainly focuses to combine the Deep Learning with Data parallelism and Cloud Computing Machine learning engine as "hybrid architecture" to predict new Cryptocurrency prices by using historical Cryptocurrency data. The study has exploited 266,776 of Cryptocurrency prices values from the pilot experiment, and Deep Learning algorithm used for the price prediction. The four hybrid architecture models, namely, (i) standalone PC, (ii) Cloud computing without data parallelism (GPU-1), (iii) Cloud computing with data parallelism (GPU-4), and (iv) Cloud computing with data parallelism (GPU-8) introduced and utilized for the analysis. The performance of each model is evaluated using different performance evaluation parameters. Then, the efficiency of each model was compared using different batch sizes. An experimental result reveals that Cloud computing technology exposes new era by performing parallel computing in IoT to reduce computation time up to 90% of the Deep Learning algorithm-based Cryptocurrencies price prediction model and many other IoT applications such as character recognition, biomedical field, industrial automation, and natural disaster prediction.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Real-Time Cryptocurrency Price Prediction by Exploiting IoT Concept and Beyond: Cloud Computing, Data Parallelism and Deep Learning

Premarathne¹,

Halgamuge²,

Samarakody³

et al. 2020

IJACSA

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“…We measured the performance and virtualization overhead of several MPI applications on the virtual environments in an earlier study [24]. Here we present extended performance results of using Apache Hadoop implementation of SW-G and Cap3 in a cloud environment by comparing Hadoop on Linux with Hadoop on Linux on Xen [26] para-virtualised environment.…”

Section: Performance In the Cloudmentioning

confidence: 96%

“…Note approaches like Condor have significant startup time dominating performance. For basic operations [24], we find Hadoop and Dryad get similar performance on bioinformatics, particle physics and the well known kernels. Wilde [25] has emphasized the value of scripting to control these (task parallel) problems and here DryadLINQ offers some capabilities that we exploited.…”

Section: Related Workmentioning

confidence: 99%

Cloud technologies for bioinformatics applications

Qiu

Ekanayake

Beason

et al. 2009

Proceedings of the 2nd Workshop on Many-Task Computing on Grids and Supercomputers

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Abstract-Executing large number of independent jobs or jobs comprising of large number of tasks that perform minimal intertask communication is a common requirement in many domains. Various technologies ranging from classic job schedulers to latest cloud technologies such as MapReduce can be used to execute these "many-tasks" in parallel. In this paper, we present our experience in applying two cloud technologies Apache Hadoop and Microsoft DryadLINQ to two bioinformatics applications with the above characteristics. The applications are a pairwise Alu sequence alignment application and an EST (Expressed Sequence Tag) sequence assembly program. First we compare the performance of these cloud technologies using the above case and also compare them with traditional MPI implementation in one application. Next we analyze the effect of inhomogeneous data on the scheduling mechanisms of the cloud technologies. Finally we present a comparison of performance of the cloud technologies under virtual and non-virtual hardware platforms.

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“…It also distinguishes between static data that does not change in the course of the iterations, and normal data which may change during each iteration. Ekanayake et al [14] Zaharia et al [41] also found that MapReduce is not suitable for many applications that need to reuse a working set of input data across parallel operations. They propose Spark, a framework that supports iterative applications, yet retains the scalability and fault tolerance of MapReduce.…”

Section: Related Workmentioning

confidence: 99%

Scalability of Parallel Scientific Applications on the Cloud

Srirama¹,

Batrashev

Jakovits

et al. 2011

Scientific Programming

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Abstract. Cloud computing, with its promise of virtually infinite resources, seems to suit well in solving resource greedy scientific computing problems. To study the effects of moving parallel scientific applications onto the cloud, we deployed several benchmark applications like matrix-vector operations and NAS parallel benchmarks, and DOUG (Domain decomposition On Unstructured Grids) on the cloud. DOUG is an open source software package for parallel iterative solution of very large sparse systems of linear equations. The detailed analysis of DOUG on the cloud showed that parallel applications benefit a lot and scale reasonable on the cloud. We could also observe the limitations of the cloud and its comparison with cluster in terms of performance. However, for efficiently running the scientific applications on the cloud infrastructure, the applications must be reduced to frameworks that can successfully exploit the cloud resources, like the MapReduce framework. Several iterative and embarrassingly parallel algorithms are reduced to the MapReduce model and their performance is measured and analyzed. The analysis showed that Hadoop MapReduce has significant problems with iterative methods, while it suits well for embarrassingly parallel algorithms. Scientific computing often uses iterative methods to solve large problems. Thus, for scientific computing on the cloud, this paper raises the necessity for better frameworks or optimizations for MapReduce.

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High-Performance Parallel Computing with Cloud and Cloud Technologies

Cited by 21 publications

References 26 publications

Real-Time Cryptocurrency Price Prediction by Exploiting IoT Concept and Beyond: Cloud Computing, Data Parallelism and Deep Learning

Real-Time Cryptocurrency Price Prediction by Exploiting IoT Concept and Beyond: Cloud Computing, Data Parallelism and Deep Learning

Cloud technologies for bioinformatics applications

Scalability of Parallel Scientific Applications on the Cloud

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