Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  present a statistical optimization approach for scheduling a task dependence graph with variable task execution times onto a heterogeneous multiprocessor system. Scheduling methods in the presence of variations typically rely on worst-case timing estimates for hard real-time applications, or average-case analysis for other applications. Hover, a large class of soft real-time applications require only statistical guarantees on latency and throughput.  present a general statistical model that captures the probability distributions of task execution times as ll as the correlations of execution times of different tasks.  use a Monte Carlo based technique to perform makespan analysis of different schedules based on this model. This approach can be used to analyze the variability present in a variety of soft real-time applications, including a H.264 video processing application.    present two scheduling algorithms based on statistical makespan analysis. The first is a heuristic based on a critical path analysis of the task dependence graph. The other is a simulated annealing algorithm using incremental timing analysis. Both algorithms take as input the required statistical guarantee, and can thus be easily re-used for different required guarantees.  show that optimization methods based on statistical analysis show a 25-30% improvement in makespan over methods based on static worst-case analysis.
['Nadathur Satish', 'Kaushik Ravindran', 'Kurt Keutzer']
I am writing a research paper on a idea Scheduling task dependence graphs with variable task execution times onto heterogeneous multiprocessors
[{"from": "human", "value": "I am writing a research paper on a idea Scheduling task dependence graphs with variable task execution times onto heterogeneous multiprocessors"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  present a statistical optimization approach for scheduling a task dependence graph with variable task execution times onto a heterogeneous multiprocessor system. Scheduling methods in the presence of variations typically rely on worst-case timing estimates for hard real-time applications, or average-case analysis for other applications. Hover, a large class of soft real-time applications require only statistical guarantees on latency and throughput.  present a general statistical model that captures the probability distributions of task execution times as ll as the correlations of execution times of different tasks.  use a Monte Carlo based technique to perform makespan analysis of different schedules based on this model. This approach can be used to analyze the variability present in a variety of soft real-time applications, including a H.264 video processing application.    present two scheduling algorithms based on statistical makespan analysis. The first is a heuristic based on a critical path analysis of the task dependence graph. The other is a simulated annealing algorithm using incremental timing analysis. Both algorithms take as input the required statistical guarantee, and can thus be easily re-used for different required guarantees.  show that optimization methods based on statistical analysis show a 25-30% improvement in makespan over methods based on static worst-case analysis."}]