For a source node, v, and target node, w, the traceroute command iteratively issues "kth-hop" queries, for k = 1, 2, . . ., δ(v, w), which return the name of the kth vertex on a shortest path from v to w, w...
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With the increasing signal bandwidth of oppor-tunistic radiators, real-time clutter suppression has become a serious challenge for passive radars. To solve the problem, this paper designs an inter-segment parallel alg...
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Binary search trees (BSTs) are one of the most important data structures in computer science. A parallel construction algorithm of a BST can be easily derived from the sequential algorithm. Since the structure of the ...
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We present the first near-linear work and poly-logarithmic depth algorithm for computing a minimum cut in an undirected graph. Previous parallel algorithms with poly-logarithmic depth required at least quadratic work ...
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We present the first near-linear work and poly-logarithmic depth algorithm for computing a minimum cut in an undirected graph. Previous parallel algorithms with poly-logarithmic depth required at least quadratic work in the number of vertices. In a graph with n vertices and m edges, our randomized algorithm computes the minimum cut with high probability in O(m log(4) n) work and O(log(3) n) depth. This result is obtained by parallelizing a data structure that aggregates weights along paths in a tree, in addition exploiting the connection between minimum cuts and approximate maximum packings of spanning trees. In addition, our algorithm improves upon bounds on the number of cache misses incurred to compute a minimum cut.
We propose a parallel exact diagonalization method for solving the large-scale Hubbard model. The core of this algorithm is the parallelization of the Lanczos algorithm, for which we propose a hierarchical communicati...
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The Multi-Way Number Partitioning (MWNP) problem is to divide a multiset of n numbers into k subsets in such a way that the largest subset sum is minimized. MWNP is an NP-hard combinatorial optimization problem which ...
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Motivated from parallel network mapping, we provide efficient query complexity and round complexity bounds for graph reconstruction using distance queries, including a bound that improves a previous sequential complex...
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ISBN:
(纸本)9781450380706
Motivated from parallel network mapping, we provide efficient query complexity and round complexity bounds for graph reconstruction using distance queries, including a bound that improves a previous sequential complexity bound. Our methods use a high-probability parametric parallelization of a graph clustering technique of Thorup and Zwick, which may be of independent interest.
SPPARKS is an open-source parallel simulation code for developing and running various kinds of on-lattice Monte Carlo models at the atomic or meso scales. It can be used to study the properties of solid-state material...
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SPPARKS is an open-source parallel simulation code for developing and running various kinds of on-lattice Monte Carlo models at the atomic or meso scales. It can be used to study the properties of solid-state materials as well as model their dynamic evolution during processing. The modular nature of the code allows new models and diagnostic computations to be added without modification to its core functionality, including its parallel algorithms. A variety of models for microstructural evolution (grain growth), solid-state diffusion, thin film deposition, and additive manufacturing (AM) processes are included in the code. SPPARKS can also be used to implement grid-based algorithms such as phase field or cellular automata models, to run either in tandem with a Monte Carlo method or independently. For very large systems such as AM applications, the Stitch I/O library is included, which enables only a small portion of a huge system to be resident in memory. In this paper we describe SPPARKS and its parallel algorithms and performance, explain how new Monte Carlo models can be added, and highlight a variety of applications which have been developed within the code.
Successive relaxation iterative algorithm (SOR) is a common iterative algorithm for solving linear symmetric transformation equations. When the coefficient matrix is positive, it has faster convergence speed. However,...
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Computing routing schemes that support both high throughput and low latency is one of the core challenges of network optimization. Such routes can be formalized as h-length flows which are defined as flows whose flow ...
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ISBN:
(纸本)9781450399135
Computing routing schemes that support both high throughput and low latency is one of the core challenges of network optimization. Such routes can be formalized as h-length flows which are defined as flows whose flow paths have length at most h. Many well-studied algorithmic primitives-such as maximal and maximum length-constrained disjoint paths-are special cases of h-length flows. Likewise the optimal h-length flow is a fundamental quantity in network optimization, characterizing, up to poly-log factors, how quickly a network can accomplish numerous distributed primitives. In this work, we give the first efficient algorithms for computing (1 - epsilon)-approximate h-length flows that are nearly "as integral as possible." We give deterministic algorithms that take (O) over tilde (poly(h, 1/epsilon)) parallel time and (O) over tilde (poly(h, 1/epsilon) center dot 2(O) (root log n)) distributed CONGEST time. We also give a CONGEST algorithm that succeeds with high probability and only takes (O) over tilde (poly(h, 1/epsilon)) time. Using our h-length flow algorithms, we give the first efficient deterministic CONGEST algorithms for the maximal disjoint paths problem with length constraints-settling an open question of Chang and Saranurak (FOCS 2020)-as well as essentially-optimal parallel and distributed approximation algorithms for maximum length-constrained disjoint paths. The former greatly simplifies deterministic CONGEST algorithms for computing expander decompositions. We also use our techniques to give the first efficient and deterministic (1-epsilon)-approximation algorithms for bipartite b-matching in CONGEST. Lastly, using our flow algorithms, we give the first algorithms to efficiently compute h-length cutmatches, an object at the heart of recent advances in length-constrained expander decompositions.
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