Dhrystone: a synthetic systems programming benchmark
Communications of the ACM
Computers and Intractability; A Guide to the Theory of NP-Completeness
Computers and Intractability; A Guide to the Theory of NP-Completeness
Performance of Various Computers Using Standard Linear Equations Software
Performance of Various Computers Using Standard Linear Equations Software
Where the really hard problems are
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 1
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In optimization, search, and learning, it is very common to compare our new results with previous works but, sometimes, we can find some troubles: it is not easy to reproduce the results or to obtain an exact implementation of the original work, or we do not have access to the same processor where the original algorithm was tested for running our own algorithm. With the present work we try to provide the basis for a methodology to characterize the execution time of an algorithm in a processor, given its execution time in another one, so that we could fairly compare algorithms running in different processors. In this paper, we present a proposal for such a methodology, as well as an example of its use applied to two well-known algorithms (Genetic Algorithms and Simulated Annealing) and solving the MAXSAT problem.