Generating data transfers for distributed GPU parallel programs

Silber-Chaussumier, Frédérique; Muller, Alain; Habel, Rachid

doi:10.1016/j.jpdc.2013.07.022

Cited by 9 publications

(7 citation statements)

References 21 publications

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“…As can be seen from Figure 9 and 10, when the number of counters remains unchanged (2 23 , 2 26 or 2 29 ) and g increases from 1024 to 4096, the number of hosts whose cardinality cannot be approximated by VATE is also decreases. When g = 4096, for Caida 2015 02 19, the cardinality of all hosts in the time window W(600,300) can be approximately estimated.…”

Section: Methodsmentioning

confidence: 94%

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VATE: A trade-off between memory and preserving time for high accurate cardinality estimation under sliding time window

Ding

et al. 2019

Computer Communications

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Section: Methodsmentioning

confidence: 94%

“…A GPU chip contains hundreds to thousands of processing units, far more than that in the CPU. For tasks without data access conflicts and using the same instructions to process different data (single instruction multiple data streams, SIMD), GPU can achieve high speedup [28] [29].…”

Section: Deploy Vate On Gpumentioning

confidence: 99%

VATE: A trade-off between memory and preserving time for high accurate cardinality estimation under sliding time window

Ding

et al. 2019

Computer Communications

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“…The numerical optimisation approach is universal, and it admits any distortion types or types of objects. It is promising that both the training for accumulating CEP statistics and the testing shall be accelerated by GPU computations and parallelization [33], [34], [45], [46]. Very likely, using CUDA [47], [48] is preferable to higher-level languages.…”

Section: Discussionmentioning

confidence: 99%

Optimal Training Parameters and Hidden Layer Neuron Number of Two-Layer Perceptron for Generalised Scaled Object Classification Problem

Romanuke

2015

Information Technology and Management Science

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-The research is focused on optimising two-layer perceptron for generalised scaled object classification problem. The optimisation criterion is minimisation of inaccuracy. The inaccuracy depends on training parameters and hidden layer neuron number. After its statistics is accumulated, minimisation is executed by a numerical search. Perceptron is optimised additionally by extra training. As it is done, the classification error percentage does not exceed 3 % in case of the worst scale distortion.

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“…Graphic processing unit (GPU) is one of the most popular parallel computing platform in recent years. For these tasks that have no data accessing conflict and processing different data with the same instructions (SIMD), GPU can acquire a high speed up [2] [19]. Every packet will update SEAV and LDCA.…”

Section: Distributed Super Points Detection On Gpumentioning

confidence: 99%

Most Memory Efficient Distributed Super Points Detection on Core Networks

Ding

2018

Algorithms and Architectures for Parallel Processing

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The super point, a host which communicates with lots of others, is a kind of special hosts gotten great focus. Mining super point at the edge of a network is the foundation of many network research fields. In this paper, we proposed the most memory efficient super points detection scheme. This scheme contains a super points reconstruction algorithm called short estimator and a super points filter algorithm called long estimator. Short estimator gives a super points candidate list using thousands of bytes memory and long estimator improves the accuracy of detection result using millions of bytes memory. Combining short estimator and long estimator, our scheme acquires the highest accuracy using the smallest memory than other algorithms. There is no data conflict and floating operation in our scheme. This ensures that our scheme is suitable for parallel running and we deploy our scheme on a common GPU to accelerate processing speed. We also describe how to extend our algorithm to sliding time. Experiments on several real-world core network traffics show that our algorithm acquires the highest accuracy with only consuming littler than one-fifth memory of other algorithms.

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Generating data transfers for distributed GPU parallel programs

Cited by 9 publications

References 21 publications

VATE: A trade-off between memory and preserving time for high accurate cardinality estimation under sliding time window

VATE: A trade-off between memory and preserving time for high accurate cardinality estimation under sliding time window

Optimal Training Parameters and Hidden Layer Neuron Number of Two-Layer Perceptron for Generalised Scaled Object Classification Problem

Most Memory Efficient Distributed Super Points Detection on Core Networks

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