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检索条件"主题词=partitioning algorithms"

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A Novel Bayesian Network based Scheme for Finding the Optimal Solution to Stochastic Online Equi-partitioning Problems 13

A Novel Bayesian Network based Scheme for Finding the <i>Opt...

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13th International Conference on Machine Learning and Applications (ICMLA)

作者： Glimsdal, Sondre Granmo, Ole-Christoffer Univ Agder Dept ICT Grimstad Norway

ISBN: (纸本)9781479974153

A number of intriguing decision scenarios, such as order picking, revolve around partitioning a collection of objects so as to optimize some application specific objective function. In its general form, this problem is referred to as the Object partitioning Problem (OOP), known to be NP-hard. We here consider a variant of OPP, namely the Stochastic Online Equi-partitioning Problem (SO-EPP). In SO-EPP, objects arrive sequentially, in pairs. The relationship between the arriving object pairs is stochastic: They belong to the same partition with probability p. From a history of object arrivals, the goal is to predict which objects will appear together in future arrivals. As an additional complication, the partitions of related objects are required to be of equal cardinality. The decision maker, however, is not informed about the true relation between the objects, he is merely observing the stream of object pairs, and has to predict future behavior. Inferring the correct partitioning from historical behavior is thus a significant challenge, which becomes even more difficult when p is unknown. Previously, only heuristic sub-optimal solution strategies have been proposed for SO-EPP. In this paper, we propose the first optimal solution strategy. In brief, the scheme that we propose, BN-EPP, is founded on a Bayesian Network representation of SO-EPP problems. Based on probabilistic reasoning we are not only able to infer the correct object partitioning with optimal accuracy. We are also able to simultaneously infer p, allowing us to accelerate learning as object pairs arrive. Being optimal, BN-EPP provides superior performance compared to existing state-of-the-art solution schemes. BN-EPP is also highly flexible, being capable of encoding object partitioning constraints. Finally, BN-EPP is parameter free - its performance does not rely on fine tuning any parameters. As a result of these advantages, BN-EPP opens up for significantly improved performance for OOP based applicat

关键词： belief networks inference mechanisms optimisation BN-EPP Bayesian network representation NP-hard problem OOP SO-EPP novel Bayesian network based scheme object partitioning problem probabilistic reasoning stochastic online equi-partitioning problems Bayes methods Convergence Meteorology Noise partitioning algorithms Probabilistic logic Stochastic processes Bayesian networks object oriented programming Soft computing Nondeterministic polynomial-time hard partitioning algorithms Probabilistic logic Inference mechanisms Bayes methods Markov chain Meteorology Converge Noise

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Challenges and pitfalls of partitioning blockchains 48

Challenges and pitfalls of partitioning blockchains

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48th Annual IEEE/IFIP International Conference on Dependable Systems and Networks Workshops (DSN-W)

作者： Fynn, Enrique Pedone, Fernando Univ Svizzera Italiana Lugano Switzerland

ISBN: (纸本)9781538665534

Blockchain has received much attention in recent years. This immense popularity has raised a number of concerns, scalability of blockchain systems being a common one. In this paper, we seek to understand how Ethereum, a well-established blockchain system, would respond to sharding. Sharding is a prevalent technique to increase the scalability of distributed systems. To understand how sharding would affect Ethereum, we model Ethereum blockchain as a graph and evaluate five methods to partition the graph. We assess methods using three metrics: the balance among shards, the number of transactions that would involve multiple shards, and the amount of data that would be relocated across shards upon repartitioning of the graph.

关键词： Contracts Bitcoin partitioning algorithms Scalability Measurement

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Sensor Network partitioning based on Homogeneity

Sensor Network Partitioning based on Homogeneity

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Proceedings of the 2015 IEEE International Conference on Data Science and Advanced Analytics (IEEE DSAA 2015)

作者： Xu, Yi Zhang, Zhongfei Zhang, Yaqing Yu, Philip S. Binghamton Univ Dept Comp Sci Binghamton NY USA Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou Zhejiang Peoples R China Univ Illinois Dept Comp Sci Chicago IL USA

ISBN: (纸本)9781467382731

Discovering communities from sensor networks is an important problem in many real-world applications. The problem desiderata requires to partition the network according to the homogeneity of the sensor measurement and at the same time the partitioning result to be as independent as possible of the typical local change in the network topology. This poses new challenges to the current graph partitioning methodologies. We develop a graph partitioning approach from the perspective of homogeneity. In order to avoid the inherent incompatibility of the current graph partitioning methodologies, an objective functional is designed to be asymptotically close to the Mumford-Shah functional [1]. An variational algorithm ZERO-CUT and a fast approximation algorithm GREEDY-SACK are developed to solve the objective functional. We evaluate the performance of the proposed algorithms on both synthetic and real-world graphs in different applications to demonstrate its advantage and promise in solving the problem of discovering communities from sensor networks.

关键词： Approximation algorithms Approximation methods Area measurement Layout Network topology partitioning algorithms Topology Network topology area measurement partitioning algorithms Approximation algorithms Approximation method layouts topology homogeneity Sensor networks partitions

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The Random Number partitioning Problem: Overlap Gap Property and Algorithmic Barriers

The Random Number Partitioning Problem: Overlap Gap Property...

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IEEE International Symposium on Information Theory (ISIT)

作者： Gamarnik, David Kizildag, Eren C. MIT Cambridge MA 02139 USA

ISBN: (纸本)9781665421607;9781665421591

We focus on the problem of algorithmically finding a near-optimal solution for the (random) number partitioning problem (NPP), a problem that is of great practical and theoretical significance. The NPP possesses a striking gap between the existential and best algorithmic guarantee: when its input has i.i.d. standard Gaussian entries, the optimal value of NPP is Theta(root n2(-n)) (w.h.p.);whereas the best polynomial-time algorithm achieves an exponentially worse value of only 2(-Theta(log2n)) (w.h.p.). In this paper, we inquire into the origin of this gap by studying the landscape of the NPP through the lens of statistical physics and establish the presence of the Overlap Gap Property (OGP), a topological barrier for large classes of algorithms. We then leverage the OGP to establish that (a) sufficiently stable algorithms fail to find a near-optimal solution with value below 2(-omega(nlog-1/5n));and (b) a very natural Monte Carlo Markov Chain dynamics mixes slowly. A technical innovation of our paper is that we consider the overlap structure of mtuples of near- optimal solutions where m itself grows in n. Our hardness result for stable algorithms is based on a Ramsey-theoretic argument from extremal combinatorics. To the best of our knowledge, this is the first usage of Ramsey Theory to show algorithmic hardness.

关键词： Technological innovation Monte Carlo methods Heuristic algorithms Markov processes partitioning algorithms Standards Physics

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The evolution of a hierarchical partitioning algorithm for large-scale scientific data: Three steps of increasing complexity 15

The evolution of a hierarchical partitioning algorithm for l...

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15th International Conference on Scientific and Statistical Database Management

作者： Baldwin, C Eliassi-Rad, T Abdulla, G Critchlow, T Lawrence Livermore Natl Lab Livermore CA 94551 USA

ISBN: (纸本)0769519644

As scientific data sets grow exponentially in size, the need for scalable algorithms that heuristically partition the data increases. In this paper, we describe the three-step evolution of a hierarchical partitioning algorithm for large-scale spatio-temporal scientific data sets generated by massive simulations. The first version of our algorithm uses a simple top-down partitioning technique, which divides the data by using a four-way bisection of the spatio-temporal space. The shortcomings of this algorithm lead to the second version of our partitioning algorithm, which uses a bottom-up approach. In this version, a partition hierarchy is constructed by systematically agglomerating the underlying Cartesian grid that is placed on the data. Finally, the third version of our algorithm utilizes the intrinsic topology of the data given in the original scientific problem to build the partition hierarchy in a bottom-up fashion. Specifically, the topology is used to heuristically agglomerate the data at each level of the partition hierarchy. Despite the growing complexity in our algorithms, the third version of our algorithm builds partition hierarchies in less time and is able to build trees for larger size data sets as compared to the previous two versions.

关键词： Astrophysics Computational modeling Heuristic algorithms Iterative algorithms Laboratories Large-scale systems partitioning algorithms Spatial databases Spatiotemporal phenomena Topology

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Late Breaking Results: Novel Discrete Dynamic Filled Function Algorithm for Acyclic Graph partitioning 58

Late Breaking Results: Novel Discrete Dynamic Filled Functio...

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58th ACM/IEEE Design Automation Conference (DAC)

作者： Chen, Jianli Chen, Jiarui Shi, Xiao Sun, Lichong Yu, Jun Fudan Univ State Key Lab ASIC & Syst Shanghai 200433 Peoples R China Fuzhou Univ Ctr Discrete Math & Theoret Comp Sci Fuzhou 350108 Peoples R China Southeast Univ Sch Comp Sci & Engn Nanjing 210096 Peoples R China HiSilicon Technol Co Ltd Shenzhen 518129 Peoples R China

ISBN: (纸本)9781665432740

A parallel simulation that partitions a large circuit into sub-circuits is widely used to reduce simulation rtmtime. To achieve higher simulation throughput, we shall consider signal directions, and thus the final partitioning solution must he acyclic. In this paper, we model a circuit as a directed graph imd consider acyclic graph partitioning to minimize edge cuts. This problem differs from the traditional partitioning problem because of the additional acyclicity constraint. Unlike traditional heuristics that tend to be trapped in local minima, especially for large graphs, we present a novel discrete dynamic filled function algorithm for the acyclic graph partitioning problem. Our algorithm can guarantee convergence and effectively move from one discrete local minimizer to another better one. Experimental results show that our algorithm achieves 8% average cutsize reduction over the state-of-the-art works in a comparable runtime.

关键词： Runtime Design automation Heuristic algorithms Directed graphs Throughput partitioning algorithms Integrated circuit modeling

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Boosting Verification Scalability via Structural Grouping and Semantic partitioning of Properties 19

Boosting Verification Scalability via Structural Grouping an...

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19th International Conference on Formal Methods in Computer Aided Design (FMCAD)

作者： Dureja, Rohit Baumgartner, Jason Ivrii, Alexander Kanzelman, Robert Rozier, Kristin Y. Iowa State Univ Ames IA 50011 USA IBM Corp Armonk NY 10504 USA

ISBN: (纸本)9780983567899

From equivalence checking to functional verification to design-space exploration, industrial verification tasks entail checking a large number of properties on the same design. State-of-the-art tools typically solve all properties concurrently, or one-at-a-time. They do not optimally exploit subproblem sharing between properties, leaving an opportunity to save considerable verification resource via concurrent verification of properties with nearly identical cone of influence (COI). These high-affinity properties can be concurrently solved;the verification effort expended for one can be directly reused to accelerate the verification of the others, without hurting per-property verification resources through bloating COI size. We present a near-linear runtime algorithm for partitioning properties into provably high-affinity groups for concurrent solution. We also present an effective method to partition high-structural-affinity groups using semantic feedback, to yield an optimal multi-property localization abstraction solution. Experiments demonstrate substantial end-to-end verification speedups through these techniques, leveraging parallel solution of individual groups.

关键词： Logic gates Hamming distance Registers partitioning algorithms Runtime Scalability Task analysis

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GraphA: Adaptive partitioning for Natural Graphs 37

GraphA: Adaptive Partitioning for Natural Graphs

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37th IEEE International Conference on Distributed Computing Systems (ICDCS)

作者： Li, Dongsheng Zhang, Chengfei Wang, Jinyan Zhang, Zhaoning Zhang, Yiming Natl Univ Def Technol Natl Lab Parallel & Distributed Proc Changsha Hunan Peoples R China

ISBN: (纸本)9781538617915

Large-scale graph computation is central to applications ranging from language processing to social networks. However, natural graphs tend to have skewed power-law distributions where a small subset of the vertices have a large number of neighbors. Existing graph-parallel systems suffer from load imbalance, high communication cost, or suboptimal and complex processing. In this paper we present GraphA, an Adaptive approach to efficient partitioning and computation of large-scale natural graphs. GraphA provides an adaptive and uniform graph partitioning algorithm, which partitions the datasets in a load-balanced manner by using an incremental number of hash functions. We have implemented GraphA both on Spark and on GraphLab. Extensive evaluation shows that GraphA remarkably outperforms state-of-the-art graph-parallel systems (GraphX and PowerLyra) in ingress time, execution time and storage overhead, for both real-world and synthetic graphs.

关键词： partitioning algorithms Mirrors Social network services Sparks Loading Complexity theory Algorithm design and analysis

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3D-Stacked Integrated Circuits: How Fine Should System partitioning Be?

3D-Stacked Integrated Circuits: How Fine Should System Parti...

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IEEE International Symposium on Circuits and Systems (IEEE ISCAS)

作者： Delhaye, Quentin Milojevic, Dragomir Goossens, Joel ULB Ecole Polytech Bruxelles BEAMS CP165-56Av F Roosevelt B-1050 Brussels Belgium ULB Fac Sci PARTS CP21250 Av F Roosevelt B-1050 Brussels Belgium

ISBN: (纸本)9781728103976

3D stacked ICs package multiple, independently manufactured dies to reduce total system wire-length, improve timing, and reduce area and power. When designing stacked 3D-ICs, arises the question of the grain at which one should consider system partitioning to optimize the gains. This work uses known MAX-CUT graph partitioning algorithms to split designs from 42k up to 800k gates, with gates clustered from 8 and up to 32768 partitions. It has been found that with 2048 clusters, i.e. 20 to 400 gates per cluster depending on the design, a partitioning of the system allows on average to cut 35 % of the nets that account for 73 % of the total wire-length in 3D.

关键词： Three-dimensional displays Logic gates Two dimensional displays Integrated circuit interconnections partitioning algorithms Transistors Optimization

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Spectral partitioning for Node Criticality 20

Spectral Partitioning for Node Criticality

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IEEE Symposium on Computers and Communication (ISCC)

作者： Asif, Waqar Lestas, Marios Qureshi, Hassaan Khaliq Rajarajan, Muttukrishnan City Univ London Sch Engn & Math Sci London England NUST Islamabad Pakistan Frederick Univ Dept Elect Engn Nicosia Cyprus

ISBN: (纸本)9781467371940

Finding critical nodes in a network is a significant task, highly relevant to network vulnerability and security. We consider the node criticality problem as an algebraic connectivity minimization problem where the objective is to choose nodes which minimize the algebraic connectivity of the resulting network. Previous suboptimal solutions of the problem suffer from the computational complexity associated with the implementation of a maximization consensus algorithm. In this work, we use spectral partitioning concepts introduced by Fiedler, to propose a new suboptimal solution which significantly reduces the implementation complexity. Our approach, combined with recently proposed distributed Fiedler vector calculation algorithms enable each node to decide by itself whether it is a critical node. If a single node is required then the maximization algorithm is applied on a restricted set of nodes within the network. We derive a lower bound for the achievable algebraic connectivity when nodes are removed from the network and we show through simulations that our approach leads to algebraic connectivity values close to this lower bound. Similar behaviour is exhibited by other approaches at the expense, however, of a higher implementation complexity.

关键词： Complexity theory Computers Eigenvalues and eigenfunctions Electronic mail Laplace equations Measurement partitioning algorithms

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