Compute最新文献_第5页

Evaluation of contemporary graph databases 当代图数据库评价

Compute Pub Date : 2014-10-09 DOI: 10.1145/2675744.2675752

A. Vaikuntam, V. Perumal

引用次数: 4

Predictive node expiration based energy-aware source routing (PNEB ESR) protocol for wireless sensor networks 基于预测节点过期的能量感知源路由(PNEB ESR)无线传感器网络协议

Compute Pub Date : 2014-10-09 DOI: 10.1145/2675744.2675760

Varshanth R. Rao, M. AnilKumarK.

{"title":"Predictive node expiration based energy-aware source routing (PNEB ESR) protocol for wireless sensor networks","authors":"Varshanth R. Rao, M. AnilKumarK.","doi":"10.1145/2675744.2675760","DOIUrl":"https://doi.org/10.1145/2675744.2675760","url":null,"abstract":"Wireless sensor networks essentially consist of an amalgam of autonomous sensor nodes deployed in an ad-hoc manner to collect information about the surroundings. These sensor nodes usually dispense the collected information to data sink nodes through information routing techniques. Predictive Node Expiration Based Energy -- aware Source Routing (PNEB ESR) protocol is an energy aware routing protocol which attempts to optimize the overall energy efficiency of the sensor network and ensures the sensed information reaches a data sink through a reliable path. In this protocol, the Energy Depletion Rate of a node is calculated at predetermined energy levels and is used to predict when a node will outlive its usefulness. This is used in the prediction of route expirations which will provide timely insights to the nodes on when to seek for alternative routes to data sinks. Simulations show that the PNEB ESR protocol is successful in making reliable route selections and theoretically minimizes the number of control packets flowing in the network. This leads to considerable reduction in processing and communication costs and hence minimizes overall energy consumption.","PeriodicalId":435920,"journal":{"name":"Compute","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114755955","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Motion segmentation from surveillance videos using T-test statistics 利用t检验统计量对监控视频进行运动分割

Compute Pub Date : 2014-10-09 DOI: 10.1145/2675744.2675748

M. Chandrajit, R. Girisha, T. Vasudev

引用次数: 5

Implementation of Kirchhoff-Helmholtz transform on GPU for use in digital in-line holographic microscopy 基于GPU的数字全息显微镜Kirchhoff-Helmholtz变换的实现

Compute Pub Date : 2014-10-09 DOI: 10.1145/2675744.2675755

Gagandeep Singh, M. Shukla, P. Bhimalapuram, Kishore Kothapalli

引用次数: 0

Execution profile driven speedup estimation for porting sequential code to GPU 将顺序代码移植到GPU的执行配置文件驱动的加速估计

Compute Pub Date : 2014-10-09 DOI: 10.1145/2675744.2675767

S. Sarkar, Sayantan Mitra

{"title":"Execution profile driven speedup estimation for porting sequential code to GPU","authors":"S. Sarkar, Sayantan Mitra","doi":"10.1145/2675744.2675767","DOIUrl":"https://doi.org/10.1145/2675744.2675767","url":null,"abstract":"Parallelization of an existing sequential application to achieve a good speed-up on a data-parallel infrastructure is quite difficult and time consuming effort. One of the important steps towards this is to assess whether the existing application in its current form can be parallelized to get the desired speedup. In this paper, we propose a method of analyzing an existing sequential source code that contains data-parallel loops, and give a reasonably accurate prediction of the extent of speedup possible from this algorithm. The proposed method performs static and dynamic analysis of the sequential source code to determine the time required by various portions of the code, including the data-parallel portions. Subsequently, it uses a set of novel invariants to calculate various bottlenecks that exists if the program is to be transferred to a GPGPU platform and predicts the extent of parallelization necessary by the GPU in order to achieve the desired end-to-end speedup. Our approach does not require creation of GPU code skeletons of the data parallel portions in the sequential code, thereby reducing the performance prediction effort. We observed a reasonably accurate speedup prediction when we tested our approach on multiple well-known Rodinia benchmark applications, a popular matrix multiplication program and a fast Walsh transform program.","PeriodicalId":435920,"journal":{"name":"Compute","volume":"93 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125709852","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3

Dynamic fault injection model for on-chip 2D mesh network 片上二维网格网络的动态故障注入模型

Compute Pub Date : 2014-10-09 DOI: 10.1145/2675744.2675764

Sonal Yadav, M. Gaur, V. Laxmi, M. Bhargava

引用次数: 0

A study on parallelizing grid-based fluid simulations on the CPU 基于CPU的网格流体模拟并行化研究

Compute Pub Date : 2014-10-09 DOI: 10.1145/2675744.2675756

Sandip Pawar, P. Chaudhuri

引用次数: 1

A novel approach for incident response in cloud using forensics 使用取证的云事件响应的新方法

Compute Pub Date : 2014-10-09 DOI: 10.1145/2675744.2675766

B. K. Raju, G. Geethakumari

引用次数: 3

Finding acronym expansion using semi-Markov conditional random fields 利用半马尔可夫条件随机场寻找缩略语展开

Compute Pub Date : 2014-10-09 DOI: 10.1145/2675744.2675762

Ankit Nautial, S. Bhattu, D. Somayajulu

{"title":"Finding acronym expansion using semi-Markov conditional random fields","authors":"Ankit Nautial, S. Bhattu, D. Somayajulu","doi":"10.1145/2675744.2675762","DOIUrl":"https://doi.org/10.1145/2675744.2675762","url":null,"abstract":"Acronyms are heavily used Out of Vocabulary terms in sms, search-queries, social media postings. The performance of text mining algorithms such as Part of Speech Tagging(POS), Named Entity Recognition, Chunking often suffer when they are applied over the noisy text. Text normalization systems are developed to normalize the noisy text. Acronym mapping and expansion has become an important component of the text normalization process. Since manually collecting acronyms and their corresponding expansions from the documents is difficult, automatically building such a dictionary using supervised learning is the need of the hour. In this work, we focus on the acronym search problem: Given acronyms as queries, finding their corresponding expansions in a document.\u0000 Recent works formulate the given problem as a token-level sequence labelling task and employ Hidden Markov Model, or Conditional Random Fields, to tackle the problem. However, these models do not utilize the segment level information inherent in the expansion. Hence we propose a Semi-Markov Conditional Random Field based approach for the given problem, that gives us power to write more effective features that work on a group of neighbouring tokens together than the features working on individual tokens. We design and implement Semi-Markov Conditional Random Fields to identify the correct acronym expansions for data extracted from Wikipedia and compare the performance with the Conditional Random fields. The experimental results show that Semi-CRF based approach for the given task performs better than the CRF based approach.","PeriodicalId":435920,"journal":{"name":"Compute","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126453436","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Parallelization of RNA secondary structure prediction algorithm on multicore 多核RNA二级结构预测算法的并行化

Compute Pub Date : 2014-10-09 DOI: 10.1145/2675744.2675765

N. Chaudhari, A. Mahajan

{"title":"Parallelization of RNA secondary structure prediction algorithm on multicore","authors":"N. Chaudhari, A. Mahajan","doi":"10.1145/2675744.2675765","DOIUrl":"https://doi.org/10.1145/2675744.2675765","url":null,"abstract":"RNA secondary structure prediction is invaluable in creating new drugs and understanding genetic diseases, in gene expression and regulation. RNA molecules have proven their clinically consequential deep inroads into the biological functions. Needless to mention about the role of even small RNA's with numerous nucleotides which does the function of gene splicing, editing, and regulation. There is accelerating increase of data set sizes originating from powerful high-throughput measuring devices in Bioinformatics, A major limiting factor in achieving good performance on modern architectures is memory latency and bandwidth. The cost of computation is very less than the cost of moving data from main memory. This disparity between communication and computation costs has compelled the programmers to make changes in the ways algorithms are designed.\u0000 A promising solution to speed up sequential programs, which are difficult to parallelise otherwise is through Speculative parallelisation.\u0000 This paper proposes a new software-only speculative parallelization scheme for implementing RNA Secondary Structure Prediction algorithm in parallel. The scheme is developed after a systematic evaluation of the design options available. It is also shown to be efficient, robust and to outperform previously proposed schemes used for parallel implementation of RNA Secondary Structure Prediction.","PeriodicalId":435920,"journal":{"name":"Compute","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132732105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0