
This Article  
 
Share  
Bibliographic References  
Add to:  
Digg Furl Spurl Blink Simpy Del.icio.us Y!MyWeb  
Search  
 
ASCII Text  x  
P. Agrawal, A. Ng, "Computing Network Flow on a Multiple Processor Pipeline," IEEE Transactions on Parallel and Distributed Systems, vol. 5, no. 6, pp. 653658, June, 1994.  
BibTex  x  
@article{ 10.1109/71.285611, author = {P. Agrawal and A. Ng}, title = {Computing Network Flow on a Multiple Processor Pipeline}, journal ={IEEE Transactions on Parallel and Distributed Systems}, volume = {5}, number = {6}, issn = {10459219}, year = {1994}, pages = {653658}, doi = {http://doi.ieeecomputersociety.org/10.1109/71.285611}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, }  
RefWorks Procite/RefMan/Endnote  x  
TY  JOUR JO  IEEE Transactions on Parallel and Distributed Systems TI  Computing Network Flow on a Multiple Processor Pipeline IS  6 SN  10459219 SP653 EP658 EPD  653658 A1  P. Agrawal, A1  A. Ng, PY  1994 KW  Index Termspipeline processing; distributed algorithms; graph theory; network flow; multiple processor pipeline; maximum flow; GoldbergTarjan algorithm; parallel implementations; network graph; partitioned algorithm; six processors; messagepassing multicomputer; performance estimates VL  5 JA  IEEE Transactions on Parallel and Distributed Systems ER   
We demonstrate the feasibility of a distributed implementation of the GoldbergTarjan algorithm for finding the maximum flow in a network. Unlike other parallel implementations of this algorithm, where the network graph is partitioned among many processors, we partition the algorithm among processors arranged in a pipeline. The network graph data are distributed among the processors according to local requirements. The partitioned algorithm is implemented on six processors within a 15processor pipelined messagepassing multicomputer operating at 5 MHz. We used randomly generated networks with integer capacities as examples. Performance estimates based upon a sixprocessor pipelined implementation indicated a speedup between 3.8 and 5.9 over a single processor.
[1] P. Agrawal and W. J. Dally, "A hardware logic simulation system,"IEEE Trans. Comput. Aided Design of Circuits Syst., vol. 9, pp. 1929, Jan. 1990.
[2] F. Alizadeh and A. V. Goldberg, "Experiments with the pushrelabel method for the maximum flow problem on a connection machine,"DIMACS Implementation Challenge Workshop: Network Flows and Matching, Tech. Rep. 924, pp. 5671, Sept. 1991.
[3] R. J. Anderson and J. C. Setubal, "Parallel and sequential implementations of maximumflow algorithms,"DIMACS Implementation Challenge Workshop: Network Flows and Matching, Tech. Rep. 924, pp. 1741, Sept. 1991.
[4] J. Cheriyan and S. N. Maheshwari, "Analysis of preflow push algorithms for maximum network flow,"SIAM J. Comput., vol. 18, pp. 10571086, 1989.
[5] A. Goldberg and R. Tarjan, "A new approach to the maximum flow problem," inProc. 18th ACM Symp. Theory Comput., 1986, pp. 136146.
[6] A. V. Goldberg, "Efficient graph algorithms for sequential and parallel computers," Tech. Rep. TR374, Lab. for Comput. Sci., Massachusetts Inst. of Technol., Cambridge, MA, 1987.
[7] A. V. Goldberg, "Processorefficient implementation of a network flow algorithm," Tech. Rep. STANCS901301, Dept. of Comput. Sci., Stanford Univ., Palo Alto, CA, 1990.
[8] A. V. Karzanov, "Determining the maximum flow in a network by the method of preflows,"Soviet Math. Dokl., vol. 15, pp. 4337, 1974.
[9] E. L. Lawler, Combinatorial Optimization, Networks and Matroids. New York: Holt, Rinehart and Winston, 1976.
[10] K. J. Singh, A. R. Wang, R. K. Brayton, and A. L. SangiovanniVincentelli, "Timing optimization in combinational circuits,"IEEE Int. Conf. Comput.Aided Design, 1988, pp. 282285.