Artificial intelligence
Heuristic search in restricted memory (research note)
Artificial Intelligence
Computer architecture: a quantitative approach
Computer architecture: a quantitative approach
Performance Analysis of k-ary n-cube Interconnection Networks
IEEE Transactions on Computers
Randomized parallel algorithms for backtrack search and branch-and-bound computation
Journal of the ACM (JACM)
Scalable load balancing strategies for parallel A* algorithms
Journal of Parallel and Distributed Computing - Special issue on scalability of parallel algorithms and architectures
The art of computer programming, volume 3: (2nd ed.) sorting and searching
The art of computer programming, volume 3: (2nd ed.) sorting and searching
Random Trees and the Analysis of Branch and Bound Procedures
Journal of the ACM (JACM)
Algorithms for VLSI Physical Design Automation
Algorithms for VLSI Physical Design Automation
Introduction to Algorithms
Load Balancing for Distributed Branch and Bound Algorithms
IPPS '92 Proceedings of the 6th International Parallel Processing Symposium
MANIP-a parallel computer system for implementing branch and bound algorithms
ISCA '81 Proceedings of the 8th annual symposium on Computer Architecture
ICS '96 Proceedings of the 10th international conference on Supercomputing
A Parallel Algorithm for Multiple Objective Linear Programs
Computational Optimization and Applications
State of the Art in Parallel Search Techniques for Discrete Optimization Problems
IEEE Transactions on Knowledge and Data Engineering
On multiprocessor task scheduling using efficient state space search approaches
Journal of Parallel and Distributed Computing
Short communication: A modified ant optimization algorithm for path planning of UCAV
Applied Soft Computing
Evaluations of hash distributed A* in optimal sequence alignment
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
Path planning of UCAV based on a modified GeesePSO algorithm
ICIC'12 Proceedings of the 8th international conference on Intelligent Computing Theories and Applications
Evaluation of a simple, scalable, parallel best-first search strategy
Artificial Intelligence
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For many applications of the A* algorithm, the state space is a graph rather than a tree. The implication of this for parallel A* algorithms is that different processors may perform significant duplicated work if interprocessor duplicates are not pruned. In this paper, we consider the problem of duplicate pruning in parallel A* graph-search algorithms implemented on distributed-memory machines. A commonly used method for duplicate pruning uses a hash function to associate with each distinct node of the search space a particular processor to which duplicate nodes arising in different processors are transmitted and thereby pruned. This approach has two major drawbacks. First, load balance is determined solely by the hash function. Second, node transmissions for duplicate pruning are global; this can lead to hot spots and slower message delivery. To overcome these problems, we propose two different duplicate pruning strategies: 1) To achieve good load balance, we decouple the task of duplicate pruning from load balancing, by using a hash function for the former and a load balancing scheme for the latter. 2) A novel search-space partitioning scheme that allocates disjoint parts of the search space to disjoint subcubes in a hypercube (or disjoint processor groups in the target architecture), so that duplicate pruning is achieved with only intrasubcube or adjacent intersubcube communication. Thus message latency and hot-spot probability are greatly reduced. The above duplicate pruning schemes were implemented on an nCUBE2 hypercube multicomputer to solve the Traveling Salesman Problem (TSP). For uniformly distributed intercity costs, our strategies yield a speedup improvement of 13 to 35 percent on 1,024-processors over previous methods that do not prune any duplicates, and 13 to 25 percent over the previous hashing-only scheme. For normally distributed data the corresponding figures are 135 percent and 10 to 155 percent. Finally, we analyze the scalability of our parallel A* algorithms on k-ary n-cube networks in terms of the isoefficiency metric, and show that they have isoefficiency lower and upper bounds of 驴(P log P) and 驴(Pkn2), respectively.