Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  two classifiers have been derived in order to determine if identical computer tasks have been executed at different processors. The classifiers have been developed analytically following a classical hypothesis testing approach. The main assumption of this work is that the probability distribution function (pdf) of the random times taken by the processors to serve tasks are known. This assumption has been fulfilled by empirically characterizing the pdf of such random times. The performance of the classifiers developed here has been assessed using traces from real processors. Further, the performance of the classifiers is compared to heuristic classifiers, linear discriminants, and non-linear discriminants among other classifiers.
['Hugo Pisco Pacheco', 'Jonathan Pino', 'Julio Sergio Santana', 'Pablo Ulloa', 'Jorge E. Pezoa']
I am writing a research paper on a idea Classifying execution times in parallel computing systems: a classical hypothesis testing approach
[{"from": "human", "value": "I am writing a research paper on a idea Classifying execution times in parallel computing systems: a classical hypothesis testing approach"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  two classifiers have been derived in order to determine if identical computer tasks have been executed at different processors. The classifiers have been developed analytically following a classical hypothesis testing approach. The main assumption of this work is that the probability distribution function (pdf) of the random times taken by the processors to serve tasks are known. This assumption has been fulfilled by empirically characterizing the pdf of such random times. The performance of the classifiers developed here has been assessed using traces from real processors. Further, the performance of the classifiers is compared to heuristic classifiers, linear discriminants, and non-linear discriminants among other classifiers."}]