34505121
Adaptive Path-Based Multicast on Wormhole-Routed Hypercubes
In this paper, we consider path-based multicast on wormhole-routed hypercubes. A minimum set of routing restrictions is used as the base routing algorithm. To correctly perform multicast, we present the natural list to sort the destination nodes. It can be proved to be deadlock-free for both one-port and multi-port systems. Furthermore, it creates only one worm for each multicast. Between each pair of nodes in a multicast path with distance k, on the average, there are at least (k+1)!/2k adaptive shortest paths, which is superior to previous works. We also propose a heuristic algorithm to reduce the path length. The simulation result shows that its sustainable throughput is much better than related works. In addition, unicast and broadcast can be treated as degenerated cases and use the same routing algorithm. Therefore, it offers a comprehensive routing solution for communication on hypercubes.
1928330
A near-optimal broadcasting algorithm in all-port wormhole-routed hypercubes
For massively parallel computing mechanism, broadcasting is widely used in a variety of applications. When the computation is distributed among the powerful processors, communication overhead always limits the speedup. To reduce the influence of communication latency, a variety of routing methods have been discussed. In this paper, we propose an efficient routing method of broadcasting on all-port worrnhole-routed hybercubes. By exploiting the distance-insensitivity of wormhole switching, this efficient broadcasting algorithm reduce communication latency to l_rzl_logz(n+l )11steps in an n-dimensional hypercube, which is much better than previous results and very close to the theoretical optimum, [rz/log2(n+l )1.Further, by adopting the Hamming code, a well-known algebraic approach in coding theory, this proposed algorithm is contention-free in each broadcasting step.
10710066
An Algorithm for the Chromatic Number of a Graph
Christofides' algorithm for finding the chromatic number of a graph is improved both in speed and memory space by using a depth-first search rule to search for a shortest path in a reduced subgraph tree.
7097063
Summarizing itemset patterns using probabilistic models
In this paper, we propose a novel probabilistic approach to summarize frequent itemset patterns. Such techniques are useful for summarization, post-processing, and end-user interpretation, particularly for problems where the resulting set of patterns are huge. In our approach items in the dataset are modeled as random variables. We then construct a Markov Random Fields (MRF) on these variables based on frequent itemsets and their occurrence statistics. The summarization proceeds in a level-wise iterative fashion. Occurrence statistics of itemsets at the lowest level are used to construct an initial MRF. Statistics of itemsets at the next level can then be inferred from the model. We use those patterns whose occurrence can not be accurately inferred from the model to augment the model in an iterative manner, repeating the procedure until all frequent itemsets can be modeled. The resulting MRF model affords a concise and useful representation of the original collection of itemsets. Extensive empirical study on real datasets show that the new approach can effectively summarize a large number of itemsets and typically significantly outperforms extant approaches.
9741529
Mining frequent itemsets in distributed and dynamic databases
Traditional methods for frequent itemset mining typically assume that data is centralized and static. Such methods impose excessive communication overhead when data is distributed, and they waste computational resources when data is dynamic. We present what we believe to be the first unified approach that overcomes these assumptions. Our approach makes use of parallel and incremental techniques to generate frequent itemsets in the presence of data updates without examining the entire database, and imposes minimal communication overhead when mining distributed databases. Further, our approach is able to generate both local and global frequent itemsets. This ability permits our approach to identify high-contrast frequent itemsets, which allows one to examine how the data is skewed over different sites.
17451344
Compositional SCC Analysis for Language Emptiness
We propose a refinement approach to language emptiness, which is based on the enumeration and the successive refinements of SCCs on over-approximations of the exact system. Our algorithm is compositional: It performs as much computation as possible on the abstract systems, and prunes uninteresting part of the search space as early as possible. It decomposes the state space disjunctively so that each state subset can be checked in isolation to decide language emptiness for the given system. We prove that the strength of an SCC or a set of SCCs decreases monotonically with composition. This allows us to deploy the proper model checking algorithms according to the strength of the SCC at hand. We also propose to use the approximate distance of a fair cycle from the initial states to guide the search. Experimental studies on a set of LTL model checking problems prove the effectiveness of our method.
14760800
Deciding Separation Logic Formulae by SAT and Incremental Negative Cycle Elimination
Separation logic is a subset of the quantifier-free first order logic. It has been successfully used in the automated verification of systems that have large (or unbounded) integer-valued state variables, such as pipelined processor designs and timed systems. In this paper, we present a fast decision procedure for separation logic, which combines Boolean satisfiability (SAT) with a graph based incremental negative cycle elimination algorithm. Our solver abstracts a separation logic formula into a Boolean formula by replacing each predicate with a Boolean variable. Transitivity constraints over predicates are detected from the constraint graph and added on a need-to basis. Our solver handles Boolean and theory conflicts uniformly at the Boolean level. The graph based algorithm supports not only incremental theory propagation, but also constant time theory backtracking without using a cumbersome history stack. Experimental results on a large set of benchmarks show that our new decision procedure is scalable, and outperforms existing techniques for this logic.