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A.N. Choudhary, B. Narahari, D.M. Nicol, R. Simha, "Optimal Processor Assignment for a Class of Pipelined Computations," IEEE Transactions on Parallel and Distributed Systems, vol. 5, no. 4, pp. 439445, April, 1994.  
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@article{ 10.1109/71.273050, author = {A.N. Choudhary and B. Narahari and D.M. Nicol and R. Simha}, title = {Optimal Processor Assignment for a Class of Pipelined Computations}, journal ={IEEE Transactions on Parallel and Distributed Systems}, volume = {5}, number = {4}, issn = {10459219}, year = {1994}, pages = {439445}, doi = {http://doi.ieeecomputersociety.org/10.1109/71.273050}, 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  Optimal Processor Assignment for a Class of Pipelined Computations IS  4 SN  10459219 SP439 EP445 EPD  439445 A1  A.N. Choudhary, A1  B. Narahari, A1  D.M. Nicol, A1  R. Simha, PY  1994 KW  Index Termspipeline processing; resource allocation; parallel architectures; pipelined computations;multitasked parallel architectures; processor assignment problem; data dependencies;seriesparallel partial order; computer vision; parallel analysis; data sets; task structure;seriesparallel task system; series analysis VL  5 JA  IEEE Transactions on Parallel and Distributed Systems ER   
The availability of largescale multitasked parallel architectures introduces the followingprocessor assignment problem. We are given a long sequence of data sets, each of whichis to undergo processing by a collection of tasks whose intertask data dependencies forma seriesparallel partial order. Each individual task is potentially parallelizable, with aknown experimentally determined execution signature. Recognizing that data sets can bepipelined through the task structure, the problem is to find a "good" assignment ofprocessors to tasks. Two objectives interest us: minimal response time per data set,given a throughput requirement, and maximal throughput, given a response timerequirement. Our approach is to decompose a seriesparallel task system into its essential"serial" and "parallel" components; our problem admits the independent solution andrecomposition of each such component. We provide algorithms for the series analysis, and use an algorithm due to Krishnamurti and Ma for the parallel analysis. For a p processor system and a seriesparallel precedence graph with n constituent tasks, we give a O(np/sup 2/) algorithm that finds the optimal assignment (over a broad class ofassignments) for the response time optimization problem; we find the assignmentoptimizing the constrained throughput in O(np/sup 2/ log p) time. These techniques areapplied to a task system in computer vision.
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