The complexity of file transfer scheduling with forwarding
SIAM Journal on Computing
General purpose parallel architectures
Handbook of theoretical computer science (vol. A)
Highly parallel computing (2nd ed.)
Highly parallel computing (2nd ed.)
Efficient multiple multicasting in hypercubes
Journal of Systems Architecture: the EUROMICRO Journal
Doubly Logarithmic Communication Algorithms for Optical-Communication Parallel Computers
SIAM Journal on Computing
Complexity and approximations for multimessage multicasting
Journal of Parallel and Distributed Computing
Open Shop Scheduling to Minimize Finish Time
Journal of the ACM (JACM)
A general packet replication scheme for multicasting in interconnection networks
INFOCOM '95 Proceedings of the Fourteenth Annual Joint Conference of the IEEE Computer and Communication Societies (Vol. 1)-Volume - Volume 1
Distributed Algorithms for Multimessage Multicasting
Distributed Algorithms for Multimessage Multicasting
Proofs for Improved Approximation Algorithms forMultimessage Multicasting
Proofs for Improved Approximation Algorithms forMultimessage Multicasting
Simple Algorithms for Multimessage Multicasting With Forwarding
Simple Algorithms for Multimessage Multicasting With Forwarding
Gossiping in the Multicasting Communication Environment
IPDPS '01 Proceedings of the 15th International Parallel & Distributed Processing Symposium
An Efficient Algorithm for Gossiping in the Multicasting Communication Environment
IEEE Transactions on Parallel and Distributed Systems
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We consider Multimessage Multicasting over the n processor complete (or fully connected) static network (MMc). We present a fast approximation algorithm with an improved approximation bound for problem instances with small fan-out (maximum number of processors receiving any given message), but arbitrary degree d, where d is the maximum number of messages that each processor may send or receive. These problem instances are the ones that arise in practice, since the fan-out restriction is imposed by the applications and the number of processors available in commercial systems.Our algorithm is centralized and requires all the communication information ahead of time. Applications where this information is available include iterative algorithms for solving linear equations and most dynamic programming procedures. The Meiko CS-2 machine as well as other computer systems whose processors communicate via dynamic networks will also benefit from our results at the expense of doubling the number of communication phases.