Publication 2000 Issue No. 10 - October Abstract - Better Adaptive Diagnosis of Hypercubes
October 2000 (vol. 49 no. 10)
pp. 1013-1020
 ASCII Text x Evangelos Kranakis, Andrzej Pelc, "Better Adaptive Diagnosis of Hypercubes," IEEE Transactions on Computers, vol. 49, no. 10, pp. 1013-1020, October, 2000.
 BibTex x @article{ 10.1109/12.888036,author = {Evangelos Kranakis and Andrzej Pelc},title = {Better Adaptive Diagnosis of Hypercubes},journal ={IEEE Transactions on Computers},volume = {49},number = {10},issn = {0018-9340},year = {2000},pages = {1013-1020},doi = {http://doi.ieeecomputersociety.org/10.1109/12.888036},publisher = {IEEE Computer Society},address = {Los Alamitos, CA, USA},}
 RefWorks Procite/RefMan/Endnote x TY - JOURJO - IEEE Transactions on ComputersTI - Better Adaptive Diagnosis of HypercubesIS - 10SN - 0018-9340SP1013EP1020EPD - 1013-1020A1 - Evangelos Kranakis, A1 - Andrzej Pelc, PY - 2000KW - Adaptive diagnosisKW - faultKW - hypercubeKW - test.VL - 49JA - IEEE Transactions on ComputersER -

Abstract—We consider the problem of adaptive fault diagnosis in hypercube multiprocessor systems. Processors perform tests on one another and later tests can be scheduled on the basis of previous test results. Fault-free testers correctly identify the fault status of tested processors, while faulty testers can give arbitrary test results. The goal is to identify correctly the status of all processors, assuming that the number of faults does not exceed the hypercube dimension. We propose an adaptive diagnosis algorithm whose efficiency is drastically better than that of any previously known strategies. While the worst-case number of tests for any of them exceeds $2^n \log n$ for an $n{\hbox{-}}{\rm dimensional}$ hypercube, our method uses at most $2^n + 3n/2$ tests in the worst case. We can also modify our algorithm to improve the number of testing rounds. By slightly increasing the number of tests to $2^n + (n+1)^2$ (still a much better performance than $2^n \log n$), we can carry out diagnosis in at most 11 rounds in the worst case (as opposed to over $n$ rounds in the best previously known strategy).

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