For the given multiple patterns and a text string, firstly, a perfect hash function is constructed, the patterns are transformed into the unique pairs of integer values in parallel by the perfect hash function, the corresponding integer values are stored in a global hash table, and a recursion expression for computing hash function value of the signatures of each sub-string of text is also proposed. Secondly, based on divisible load principle, a linear programming model for the optimal text distribution strategy is created and a parallel approximate multi-pattern matching algorithm allowing one error is presented on the heterogeneous cluster system which processors have different computing speeds and distinct communication capabilities and different memory sizes by taking into account computation and communication startup time and using the assigned processor distribution order. The experimental results on the cluster system of heterogeneous personal computers show that the presented parallel algorithm is averagely 25% faster than that one using the even text distribution strategy, and it obtains a nearly linear speedup and good scalability.
{"title":"Parallel Approximate Multi-Pattern Matching on Heterogeneous Cluster Systems","authors":"Cheng Zhong, Zeng Fan, Defu Su","doi":"10.1109/PDCAT.2008.23","DOIUrl":"https://doi.org/10.1109/PDCAT.2008.23","url":null,"abstract":"For the given multiple patterns and a text string, firstly, a perfect hash function is constructed, the patterns are transformed into the unique pairs of integer values in parallel by the perfect hash function, the corresponding integer values are stored in a global hash table, and a recursion expression for computing hash function value of the signatures of each sub-string of text is also proposed. Secondly, based on divisible load principle, a linear programming model for the optimal text distribution strategy is created and a parallel approximate multi-pattern matching algorithm allowing one error is presented on the heterogeneous cluster system which processors have different computing speeds and distinct communication capabilities and different memory sizes by taking into account computation and communication startup time and using the assigned processor distribution order. The experimental results on the cluster system of heterogeneous personal computers show that the presented parallel algorithm is averagely 25% faster than that one using the even text distribution strategy, and it obtains a nearly linear speedup and good scalability.","PeriodicalId":282779,"journal":{"name":"2008 Ninth International Conference on Parallel and Distributed Computing, Applications and Technologies","volume":"4 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129448495","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}
Wireless Sensor Networks (WSNs) are a new technology that is expected to be used in the near future due to its cheap cost and data processing ability. However, securing WSNs with traditional cryptographic mechanism is insufficient because of the existing limited resources and the lack of tamper resistant hardware. In this paper, we propose a Reputation-based Secure Data Aggregation for WSNs (RSDA) that integrates aggregation functionality with the advantages provided by a reputation system to enhance the network lifetime and the accuracy of the aggregated data. We bind symmetric secret keys to geographic locations and assign these keys to sensor nodes based on their locations. RSDA therefore can resist an adversary that is capable to compromise up to W sensor nodes in total with no more than t -1 compromised nodes in any cell.
{"title":"RSDA: Reputation-Based Secure Data Aggregation in Wireless Sensor Networks","authors":"H. Alzaid, Ernest Foo, J. G. Nieto","doi":"10.1109/PDCAT.2008.52","DOIUrl":"https://doi.org/10.1109/PDCAT.2008.52","url":null,"abstract":"Wireless Sensor Networks (WSNs) are a new technology that is expected to be used in the near future due to its cheap cost and data processing ability. However, securing WSNs with traditional cryptographic mechanism is insufficient because of the existing limited resources and the lack of tamper resistant hardware. In this paper, we propose a Reputation-based Secure Data Aggregation for WSNs (RSDA) that integrates aggregation functionality with the advantages provided by a reputation system to enhance the network lifetime and the accuracy of the aggregated data. We bind symmetric secret keys to geographic locations and assign these keys to sensor nodes based on their locations. RSDA therefore can resist an adversary that is capable to compromise up to W sensor nodes in total with no more than t -1 compromised nodes in any cell.","PeriodicalId":282779,"journal":{"name":"2008 Ninth International Conference on Parallel and Distributed Computing, Applications and Technologies","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115249331","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}
Numerical methods based on interval arithmetic are efficient means to reliably solve nonlinear systems of equations. Algorithm bc3revise is an interval method that tightens variables' domains by enforcing a property called box consistency. It has been successfully used on difficult problems whose solving eluded traditional numerical methods.We present a new algorithm to enforce box consistency that is simpler than bc3revise, faster, and easily data parallelizable. A parallel implementation with Intel SSE2 SIMD instructions shows that an increase in performance of up to an order of magnitude and more is achievable.
{"title":"A Data-Parallel Algorithm to Reliably Solve Systems of Nonlinear Equations","authors":"F. Goualard, A. Goldsztejn","doi":"10.1109/PDCAT.2008.26","DOIUrl":"https://doi.org/10.1109/PDCAT.2008.26","url":null,"abstract":"Numerical methods based on interval arithmetic are efficient means to reliably solve nonlinear systems of equations. Algorithm bc3revise is an interval method that tightens variables' domains by enforcing a property called box consistency. It has been successfully used on difficult problems whose solving eluded traditional numerical methods.We present a new algorithm to enforce box consistency that is simpler than bc3revise, faster, and easily data parallelizable. A parallel implementation with Intel SSE2 SIMD instructions shows that an increase in performance of up to an order of magnitude and more is achievable.","PeriodicalId":282779,"journal":{"name":"2008 Ninth International Conference on Parallel and Distributed Computing, Applications and Technologies","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2008-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134317452","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}
Volunteer computing, sometimes called public resource computing, is an emerging computational model that is very suitable for work-pooled parallel processing. As more complex grid applications make use of work flows in their design and deployment it is reasonable to consider the impact of work flow deployment over a volunteer computing infrastructure. In this case, the inter work flow I/O can lead to a significant increase in I/O demands at the work pool server. A possible solution is the use of a peer-to-peer based parallel computing architecture to off-load this I/O demand to the workers; where the workers can fulfill some aspects of work flow coordination and I/O checking, etc. However, achieving robustness in such a large scale system is a challenging hurdle towards the decentralized execution of work flows and general parallel processes. To increase robustness, we propose and show the merits of using an adaptive checkpoint scheme that efficiently checkpoints the status of the parallel processes according to the estimation of relevant network and peer parameters. Based on our proposed mathematical checkpoint model, our scheme uses statistical data observed during runtime to dynamically make checkpoint decisions in a completely decentralized manner. The results of simulation show support for our proposed approach in terms of reduced required runtime.
{"title":"An Adaptive Checkpointing Scheme for Peer-to-Peer Based Volunteer Computing Work Flows","authors":"Lei Ni, A. Harwood","doi":"10.1109/PDCAT.2008.53","DOIUrl":"https://doi.org/10.1109/PDCAT.2008.53","url":null,"abstract":"Volunteer computing, sometimes called public resource computing, is an emerging computational model that is very suitable for work-pooled parallel processing. As more complex grid applications make use of work flows in their design and deployment it is reasonable to consider the impact of work flow deployment over a volunteer computing infrastructure. In this case, the inter work flow I/O can lead to a significant increase in I/O demands at the work pool server. A possible solution is the use of a peer-to-peer based parallel computing architecture to off-load this I/O demand to the workers; where the workers can fulfill some aspects of work flow coordination and I/O checking, etc. However, achieving robustness in such a large scale system is a challenging hurdle towards the decentralized execution of work flows and general parallel processes. To increase robustness, we propose and show the merits of using an adaptive checkpoint scheme that efficiently checkpoints the status of the parallel processes according to the estimation of relevant network and peer parameters. Based on our proposed mathematical checkpoint model, our scheme uses statistical data observed during runtime to dynamically make checkpoint decisions in a completely decentralized manner. The results of simulation show support for our proposed approach in terms of reduced required runtime.","PeriodicalId":282779,"journal":{"name":"2008 Ninth International Conference on Parallel and Distributed Computing, Applications and Technologies","volume":"16 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125894425","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}
Scheduling tasks onto the processors of a parallel system is a crucial part of program parallelisation. Due to the NP-hard nature of the task scheduling problem, scheduling algorithms are based on heuristics that try to produce good rather than optimal schedules. Nevertheless, in certain situations it is desirable to have optimal schedules, for example for time critical systems or to evaluate scheduling heuristics. This paper investigates the task scheduling problem using A* search algorithm. The A* scheduling algorithm implemented can produce optimal schedules in reasonable time for small to medium sized task graphs. In comparison to a previous approach, the here presented A* scheduling algorithm has a significantly reduced search space due to a much improved cost function f(s) and additional pruning techniques. Last but not least, the experimental results show that the proposed A* scheduling algorithm significantly outperforms the previous approach.
{"title":"Optimal Scheduling of Task Graphs on Parallel Systems","authors":"Ahmed Zaki Semar Shahul, O. Sinnen","doi":"10.1109/PDCAT.2008.54","DOIUrl":"https://doi.org/10.1109/PDCAT.2008.54","url":null,"abstract":"Scheduling tasks onto the processors of a parallel system is a crucial part of program parallelisation. Due to the NP-hard nature of the task scheduling problem, scheduling algorithms are based on heuristics that try to produce good rather than optimal schedules. Nevertheless, in certain situations it is desirable to have optimal schedules, for example for time critical systems or to evaluate scheduling heuristics. This paper investigates the task scheduling problem using A* search algorithm. The A* scheduling algorithm implemented can produce optimal schedules in reasonable time for small to medium sized task graphs. In comparison to a previous approach, the here presented A* scheduling algorithm has a significantly reduced search space due to a much improved cost function f(s) and additional pruning techniques. Last but not least, the experimental results show that the proposed A* scheduling algorithm significantly outperforms the previous approach.","PeriodicalId":282779,"journal":{"name":"2008 Ninth International Conference on Parallel and Distributed Computing, Applications and Technologies","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2007-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132662657","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}
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