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Computers, Volume 2, Issue 4 (December 2013), Pages 152-214

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Research

Open AccessArticle Static Three-Dimensional Fuzzy Routing Based on the Receiving Probability in Wireless Sensor Networks
Computers 2013, 2(4), 152-175; doi:10.3390/computers2040152
Received: 4 July 2013 / Revised: 24 September 2013 / Accepted: 16 October 2013 / Published: 12 November 2013
Cited by 3 | PDF Full-text (724 KB) | HTML Full-text | XML Full-text
Abstract
A Wireless Sensor Network (WSN) is a collection of low-cost, low-power and large-scale wireless sensor nodes. Routing protocols are an important topic in WSN. Every sensor node should use a proper mechanism to transmit the generated packets to its destination, usually a base
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A Wireless Sensor Network (WSN) is a collection of low-cost, low-power and large-scale wireless sensor nodes. Routing protocols are an important topic in WSN. Every sensor node should use a proper mechanism to transmit the generated packets to its destination, usually a base station. In previous works, routing protocols use the global information of the network that causes the redundant packets to be increased. Moreover, it leads to an increase in the network traffic, to a decrease in the delivery ratio of data packets, and to a reduction in network life. In this paper, we propose a new inferential routing protocol called SFRRP (Static Three-Dimensional Fuzzy Routing based on the Receiving Probability). The proposed protocol solves the above mentioned problems considerably. The data packets are transmitted by hop-to-hop delivery to the base station. It uses a fuzzy procedure to transmit the sensed data or the buffered data packets to one of the neighbors called selected node. In the proposed fuzzy system, the distance and number of neighbors are input variables, while the receiving probability is the output variable. SFRRP just uses the local neighborhood information to forward the packets and is not needed by any redundant packet for route discovery. The proposed protocol has some advantages such as a high delivery ratio, less delay time, high network life, and less network traffic. The performance of the proposed protocol surpasses the performance of the Flooding routing protocol in terms of delivery ratio, delay time and network lifetime. Full article
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Open AccessArticle Exploring Graphics Processing Unit (GPU) Resource Sharing Efficiency for High Performance Computing
Computers 2013, 2(4), 176-214; doi:10.3390/computers2040176
Received: 20 September 2013 / Revised: 23 October 2013 / Accepted: 31 October 2013 / Published: 19 November 2013
Cited by 2 | PDF Full-text (906 KB) | HTML Full-text | XML Full-text
Abstract
The increasing incorporation of Graphics Processing Units (GPUs) as accelerators has been one of the forefront High Performance Computing (HPC) trends and provides unprecedented performance; however, the prevalent adoption of the Single-Program Multiple-Data (SPMD) programming model brings with it challenges of resource underutilization.
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The increasing incorporation of Graphics Processing Units (GPUs) as accelerators has been one of the forefront High Performance Computing (HPC) trends and provides unprecedented performance; however, the prevalent adoption of the Single-Program Multiple-Data (SPMD) programming model brings with it challenges of resource underutilization. In other words, under SPMD, every CPU needs GPU capability available to it. However, since CPUs generally outnumber GPUs, the asymmetric resource distribution gives rise to overall computing resource underutilization. In this paper, we propose to efficiently share the GPU under SPMD and formally define a series of GPU sharing scenarios. We provide performance-modeling analysis for each sharing scenario with accurate experimentation validation. With the modeling basis, we further conduct experimental studies to explore potential GPU sharing efficiency improvements from multiple perspectives. Both further theoretical and experimental GPU sharing performance analysis and results are presented. Our results not only demonstrate the significant performance gain for SPMD programs with the proposed efficient GPU sharing, but also the further improved sharing efficiency with the optimization techniques based on our accurate modeling. Full article
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