We present the first space and time optimal parallel algorithm for the pairwise sequence alignment problem, a fundamental problem in computational biology. This problem can be solved sequentially in O(mn) time and O(m...
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We present the first space and time optimal parallel algorithm for the pairwise sequence alignment problem, a fundamental problem in computational biology. This problem can be solved sequentially in O(mn) time and O(m + n) space, where m and n are the lengths of the sequences to be aligned. The fastest known parallel space-optimal algorithm for pairwise sequence alignment takes optimal O(m+n/p) space, but suboptimal O(m+n)(2)/p) time, where p is the number of processors. On the other hand, the most space economical time-optimal parallel algorithm takes O(mn/p) time, but O(m+n/p) space. We close this gap by presenting an algorithm that achieves both time and space optimality, i.e. requires only O(m+n/p) space and O(mn/p) time. We also present an experimental evaluation of the proposed algorithm on an IBM xSeries cluster. Although presented in the context of full sequence alignments, our algorithm is applicable to other alignment problems in computational biology including local alignments and syntenic alignments. It is also a useful addition to the range of techniques available for parallel dynamic programming.
Flood control operations in river-type reservoirs are significantly affected by both the dynamic reservoir capacity and flood propagation within the reservoir area. However, the traditional reservoir flood control opt...
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Flood control operations in river-type reservoirs are significantly affected by both the dynamic reservoir capacity and flood propagation within the reservoir area. However, the traditional reservoir flood control optimal operation (RFCOO) model is usually based on the static capacity method, which may bring large errors in scheduling calculations for river-type reservoirs. To address this issue, an improved model, the reservoir dynamic capacity flood control optimal operation (RDCFCOO) model, that includes the influence of the dynamic reservoir capacity and flood propagation, was developed to improve the accuracy of flood regulation calculations in river- type reservoirs. This improved model includes a 1D unsteady flow simulation and a rederived objective function based on the maximum peak clipping criterion, and expresses the state transformation equations using the de Saint-Venant equations. Furthermore, a multi-core parallel DP (PDP) algorithm that incorporates reduction of state dimensionality (RSD) was developed to effectively derive optimal operation schemes. The improved model and algorithm developed herein were validated through an application to the Xiangjiaba Reservoir, China. The results show that: (1) By adding the item ( q w i ) to the objective function and substituting the de Saint-Venant equations for the water balance equation, the RDCFCOO model developed in this paper can account for the impact of dynamic capacity and flood propagation. As a result, it exhibits a more accurate and detailed flood control process that was closer to the actual conditions of river-type reservoirs compared to the RFCOO model;(2) By removing the invalid discrete state points from the search space and fully utilizing multi-core processors, the proposed PDP with RSD can significantly improve the computational efficiency of the DP in solving RDCFCOO problems. The improved model and algorithm can effectively improve the calculation accuracy of flood control optimal scheduli
This paper presents the dynamicparallelization of a sequential algorithm for finding common RNA secondary structures that initially does not appear to be amenable to parallelization. A critical insight into the probl...
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ISBN:
(纸本)9780769546766
This paper presents the dynamicparallelization of a sequential algorithm for finding common RNA secondary structures that initially does not appear to be amenable to parallelization. A critical insight into the problem structure, which at first appears to be inherently top-down, leads to the development of a revised sequential algorithm that uses both bottom-up tabulation and top-down memoization. This novel combined approach proves well-suited for parallelization, overcoming the inherent difficulties in parallelizing the original top-down algorithm. The improved algorithm also eliminates two factors from the space complexity to fit into quadratic space, enabling the comparison of lengthy and complex RNA structures. Experimental results demonstrate that the parallel algorithm scales well, achieving speedup of up to 32X using 64 processors for contrived worst-case data containing structures having up to 1600 nested arcs. This algorithm illustrates the significant benefits that can be achieved by designing an underlying sequential dynamicprogramming algorithm with parallelizability in mind, instead of directly parallelizing an existing sequential algorithm. Our results also show the usefulness of combining both bottom-up and top-down perspectives when designing a parallel dynamic programming algorithm.
作者：
Wang, Fei-YueZhang, Jun JasonChinese Acad Sci
Inst Automat State Key Lab Management & Control Complex Syst SKLMCCSCASIA Beijing 100190 Peoples R China Univ Denver
Ritchie Sch Engn & Comp Sci Dept Elect & Comp Engn Denver CO 80210 USA
In this position paper, we aim to provide our argumentation on why the society-centered intelligent transportation system (ITS), or Transportation 5.0, is the inevitable course for future ITS development. We also prov...
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ISBN:
(纸本)9781538615263
In this position paper, we aim to provide our argumentation on why the society-centered intelligent transportation system (ITS), or Transportation 5.0, is the inevitable course for future ITS development. We also provide our statement on the solutions to society-centered ITS: the cyber-physicalsocial system (CPSS) architecture and parallel system methodology. Cornerstone technologies, such as knowledge automation, ontology, society perception and prescription, software defined integrated communication and computing, parallel intelligent techniques, and parallel Blockchain, are also addressed in the position statement. Our best practices in Transportation 5.0 are also demonstrated to support our position. In the conclusive remarks, we provide our visions and expectations in future society- centered intelligent transportation system.
Modelling and analysis of biochemical systems such as sugar cataract development (SCD) are critical because they can provide new insights into systems, which cannot be easily tested with experiments;however, they are ...
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Modelling and analysis of biochemical systems such as sugar cataract development (SCD) are critical because they can provide new insights into systems, which cannot be easily tested with experiments;however, they are challenging problems due to the highly coupled chemical reactions that are involved. The authors present a stochastic hybrid system (SHS) framework for modelling biochemical systems and demonstrate the approach for the SCD process. A novel feature of the framework is that it allows modelling the effect of drug treatment on the system dynamics. The authors validate the three sugar cataract models by comparing trajectories computed by two simulation algorithms. Further, the authors present a probabilistic veri. cation method for computing the probability of sugar cataract formation for different chemical concentrations using safety and reachability analysis methods for SHSs. The veri. cation method employs dynamicprogramming based on a discretisation of the state space and therefore suffers from the curse of dimensionality. To analyse the SCD process, a parallel dynamic programming implementation that can handle large, realistic systems was developed. Although scalability is a limiting factor, this work demonstrates that the proposed method is feasible for realistic biochemical systems.
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