Solving tree problems on a Mesh-connected processor Array
Information and Control
A simple parallel tree contraction algorithm
Journal of Algorithms
Information Processing Letters
Parallel tree contraction part 2: further applications
SIAM Journal on Computing
Efficient parallel algorithms for tree accumulations
Science of Computer Programming
Efficient massively parallel implementation of some combinatorial algorithms
Theoretical Computer Science
More general parallel tree contraction: register allocation and broadcasting in a tree
Theoretical Computer Science - Special issue: graph theoretic concepts in computer science
Practical parallel list ranking
Journal of Parallel and Distributed Computing
Efficient Algorithms for List Ranking and for Solving Graph Problems on the Hypercube
IEEE Transactions on Parallel and Distributed Systems
HiPC '02 Proceedings of the 9th International Conference on High Performance Computing
Many-to-many personalized communication with bounded traffic
FRONTIERS '95 Proceedings of the Fifth Symposium on the Frontiers of Massively Parallel Computation (Frontiers'95)
A Provably Optimal, Distribution-Independent Parallel Fast Multipole Method
IPDPS '00 Proceedings of the 14th International Symposium on Parallel and Distributed Processing
IEEE Transactions on Software Engineering
Parallel tree contraction and its application
SFCS '85 Proceedings of the 26th Annual Symposium on Foundations of Computer Science
Efficient parallel algorithms for constructing a k-tree center and a k-tree core of a tree network
ISAAC'05 Proceedings of the 16th international conference on Algorithms and Computation
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Accumulations are abstract operations on trees useful in many applications involving trees. The upward accumulation problem is to aggregate data in the subtree under each node of the tree. The downward accumulation problem is to aggregate data at all the ancestors of each node. In this paper, we present parallel algorithms for these problems on coarse-grained distributed memory parallel computers. We first show that when the accumulation function and the set of possible values at nodes of the tree form an Abelian (commutative) group, this problem can be solved by a remarkably simple algorithm-Upward accumulation takes Onp+@tp+@mnp time, where n is the number of nodes in the tree, p is the number of processors, @t is the communication latency and @m is the transfer time per unit message size. Downward accumulation takes Onp+(@t+@m)logp time, making it very communication efficient. For the general case, we present upward and downward accumulation algorithms that run in Onplogn+@tplogn+@mnplogn time.