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Schism: a WorkloadDriven Approach to Database Replication and Partitioning
"... We present Schism, a novel workloadaware approach for database partitioning and replication designed to improve scalability of sharednothing distributed databases. Because distributed transactions are expensive in OLTP settings (a fact we demonstrate through a series of experiments), our partitione ..."
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Cited by 91 (7 self)
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We present Schism, a novel workloadaware approach for database partitioning and replication designed to improve scalability of sharednothing distributed databases. Because distributed transactions are expensive in OLTP settings (a fact we demonstrate through a series of experiments), our partitioner attempts to minimize the number of distributed transactions, while producing balanced partitions. Schism consists of two phases: i) a workloaddriven, graphbased replication/partitioning phase and ii) an explanation and validation phase. The first phase creates a graph with a node per tuple (or group of tuples) and edges between nodes accessed by the same transaction, and then uses a graph partitioner to split the graph into k balanced partitions that minimize the number of crosspartition transactions. The second phase exploits machine learning techniques to find a predicatebased explanation of the partitioning strategy (i.e., a set of range predicates that represent the same replication/partitioning scheme produced by the partitioner). The strengths of Schism are: i) independence from the schema layout, ii) effectiveness on nton relations, typical in social network databases, iii) a unified and finegrained approach to replication and partitioning. We implemented and tested a prototype of Schism on a wide spectrum of test cases, ranging from classical OLTP workloads (e.g., TPCC and TPCE), to more complex scenarios derived from social network websites (e.g., Epinions.com), whose schema contains multiple nton relationships, which are known to be hard to partition. Schism consistently outperforms simple partitioning schemes, and in some cases proves superior to the best known manual partitioning, reducing the cost of distributed transactions up to 30%. 1.
Partitioning sparse matrices for parallel preconditioned iterative methods
 SIAM Journal on Scientific Computing
, 2004
"... Abstract. This paper addresses the parallelization of the preconditioned iterative methods that use explicit preconditioners such as approximate inverses. Parallelizing a full step of these methods requires the coefficient and preconditioner matrices to be well partitioned. We first show that differ ..."
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Cited by 14 (9 self)
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Abstract. This paper addresses the parallelization of the preconditioned iterative methods that use explicit preconditioners such as approximate inverses. Parallelizing a full step of these methods requires the coefficient and preconditioner matrices to be well partitioned. We first show that different methods impose different partitioning requirements for the matrices. Then we develop hypergraph models to meet those requirements. In particular, we develop models that enable us to obtain partitionings on the coefficient and preconditioner matrices simultaneously. Experiments on a set of unsymmetric sparse matrices show that the proposed models yield effective partitioning results. A parallel implementation of the right preconditioned BiCGStab method on a PC cluster verifies that the theoretical gains obtained by the models hold in practice.
PatternDirected Circuit Virtual Partitioning for Test Power Reduction
 Proc. IEEE International Test Conference (ITC) 2007, paper 25.2
"... For a large circuit under test (CUT), it is likely that some test patterns result in excessive power dissipations that exceed the CUT’s power rating. Designers may resort to lowpower automatic test pattern generation (ATPG) tools to solve this problem, which, however, usually leads to larger test da ..."
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Cited by 12 (7 self)
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For a large circuit under test (CUT), it is likely that some test patterns result in excessive power dissipations that exceed the CUT’s power rating. Designers may resort to lowpower automatic test pattern generation (ATPG) tools to solve this problem, which, however, usually leads to larger test data volume and requires extra computational effort, even if such tools are available. Another method is to partition the circuit into multiple subcircuits and test them separately. Unfortunately, this usually involves rerunning the timeconsuming ATPG for each partitioned subcircuit and solving the problem of how to achieve an acceptable fault coverage for the glue logic between subcircuits. In this paper, we propose a novel lowpower virtual test partitioning technique without the abovementioned shortcomings. The basic idea is to partition the circuit in such way that the faults in the glue logic between subcircuits can be detected by patterns with low power dissipation that are applied at the entire circuit level, while the patterns with high power dissipation can be applied within a partitioned subcircuit without loss of fault coverage. Scan chain routing cost has also been considered during the partitioning process. Experimental results show that the proposed technique is very effective in reducing test power. 1
Fast Iterative Graph Computation: A Path Centric Approach
 In SC
, 2014
"... Abstract—Large scale graph processing represents an interesting systems challenge due to the lack of locality. This paper presents PathGraph, a system for improving iterative graph computation on graphs with billions of edges. Our system design has three unique features: First, we model a large gra ..."
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Cited by 4 (1 self)
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Abstract—Large scale graph processing represents an interesting systems challenge due to the lack of locality. This paper presents PathGraph, a system for improving iterative graph computation on graphs with billions of edges. Our system design has three unique features: First, we model a large graph using a collection of treebased partitions and use pathcentric computation rather than vertexcentric or edgecentric computation. Our pathcentric graph parallel computation model significantly improves the memory and disk locality for iterative computation algorithms on large graphs. Second, we design a compact storage that is optimized for iterative graph parallel computation. Concretely, we use deltacompression, partition a large graph into treebased partitions and store trees in a DFS order. By clustering highly correlated paths together, we further maximize sequential access and minimize random access on storage media. Third but not the least, we implement the pathcentric computation model by using a scatter/gather programming model, which parallels the iterative computation at partition tree level and performs sequential local updates for vertices in each tree partition to improve the convergence speed. We compare PathGraph to most recent alternative graph processing systems such as GraphChi and XStream, and show that the pathcentric approach outperforms vertexcentric and edgecentric systems on a number of graph algorithms for both inmemory and outofcore graphs.
Guiding Global Placement with Wire Density
"... Abstract—This paper presents an efficient technique for the estimation of the routed wirelength during global placement using the wire density of the net. The proposed method identifies congested regions of the chip and incorporates the model of the routed wirelength into the objective function in o ..."
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Abstract—This paper presents an efficient technique for the estimation of the routed wirelength during global placement using the wire density of the net. The proposed method identifies congested regions of the chip and incorporates the model of the routed wirelength into the objective function in order to effectively alleviate these regions from congestion. The method is integrated in the analytical placement framework and the twolevel structure improves the scalability of the placer and speeds up the algorithm. The proposed analytical placer provides the bestsofar average routed wirelength in the IBM version2 benchmark suite. I.
Towards Community Detection in kPartite kUniform Hypergraphs
"... Recently, numerous applications have emerged that create data most naturally interpreted as kpartite kuniform hypergraphs. We identify benefits and challenges of generalizing community detection algorithms to these structures. We propose an algorithm which handles some of these challenges and a hy ..."
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Recently, numerous applications have emerged that create data most naturally interpreted as kpartite kuniform hypergraphs. We identify benefits and challenges of generalizing community detection algorithms to these structures. We propose an algorithm which handles some of these challenges and a hypergraph generalization of the “caveman ” model for the generation of synthetic evaluation datasets. The proposed algorithm outperforms a standard community detection algorithm working on a reduced, graphlike version of the original data, in particular when different domains possess differing community structures. 1
Consistency of Spectral Partitioning of Uniform Hypergraphs under Planted Partition Model
"... Spectral graph partitioning methods have received significant attention from both practitioners and theorists in computer science. Some notable studies have been carried out regarding the behavior of these methods for infinitely large sample size (von Luxburg et al., 2008; Rohe et al., 2011), which ..."
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Cited by 2 (2 self)
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Spectral graph partitioning methods have received significant attention from both practitioners and theorists in computer science. Some notable studies have been carried out regarding the behavior of these methods for infinitely large sample size (von Luxburg et al., 2008; Rohe et al., 2011), which provide sufficient confidence to practitioners about the effectiveness of these methods. On the other hand, recent developments in computer vision have led to a plethora of applications, where the model deals with multiway affinity relations and can be posed as uniform hypergraphs. In this paper, we view these models as random muniform hypergraphs and establish the consistency of spectral algorithm in this general setting. We develop a planted partition model or stochastic blockmodel for such problems using higher order tensors, present a spectral technique suited for the purpose and study its large sample behavior. The analysis reveals that the algorithm is consistent for muniform hypergraphs for larger values of m, and also the rate of convergence improves for increasing m. Our result provides the first theoretical evidence that establishes the importance of mway affinities. 1
Ö˘güdücü. A new graphbased evolutionary approach to sequence clustering
 In Proc. of 4th International Conference of Machine Learning and Applications
, 2005
"... Clustering methods provide users with methods to summarize and organize the huge amount of data in order to help them find what they are looking for. However, one of the drawbacks of clustering algorithms is that the result may vary greatly when using different clustering criteria. In this paper, we ..."
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Cited by 2 (1 self)
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Clustering methods provide users with methods to summarize and organize the huge amount of data in order to help them find what they are looking for. However, one of the drawbacks of clustering algorithms is that the result may vary greatly when using different clustering criteria. In this paper, we present a new clustering algorithm based on graph partitioning approach that only considers the pairwise similarities. The algorithm makes no assumptions about the size or the number of clusters. Besides this, the algorithm can make use of multiple clustering criteria functions. We will present experimental results on a synthetic data set and a real world web log data. Our experiments indicate that our clustering algorithm can efficiently cluster data items without any constraints on the number of clusters. 1.
Adapting biochemical Kripke structures for Distributed Model Checking
 In Corrado Priami, Anna Ingolfsdottir, Bud Mishra, and Hanne Riis Nielson, editors, Transactions on Computational Systems Biology
, 2006
"... Abstract. In this paper, we use some observations on the nature of biochemical reactions to derive interesting properties of qualitative biochemical Kripke structures. We show that these characteristics make Kripke structures of biochemical pathways suitable for assumption based distributed model ch ..."
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Abstract. In this paper, we use some observations on the nature of biochemical reactions to derive interesting properties of qualitative biochemical Kripke structures. We show that these characteristics make Kripke structures of biochemical pathways suitable for assumption based distributed model checking. The number of chemical species participating in a biochemical reaction is usually bounded by a small constant. This observation is used to show that the Hamming distance between adjacent states of a qualitative biochemical Kripke structures is bounded. We call such structures as Bounded Hamming Distance Kripke structures (BHDKS). We, then, argue the suitability of assumption based distributed model checking for BHDKS by constructively deriving worst case upper bounds on the size of the fragments of the state space that need to be stored at each distributed node. We also show that the distributed state space can be mapped naturally to a hypercube based distributed architecture. We support our results by experimental evaluation over benchmarks and biochemical pathways from public databases. 1