Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Edge profiling is a very common means for providing feedback on program behavior that can be used statically by an optimizer to produce highly optimized binaries. Hover collecting full edge profile carries a significant runtime overhead. This overhead creates addition problems for real-time applications, as it may prevent the system from meeting runtime deadlines and thus alter its behavior.   show how a low overhead sampling technique can be used to collect inaccurate profile which is later used to approximate the full edge profile using a novel technique based on the Minimum Cost Circulation Problem. The outcome is a machine independent profile gathering scheme that creates a slowdown of only 2%-3% during the training set, and produces an optimized binary which is only 0.6% less than a fully optimized one.
['Roy Levin', 'Ilan Newman', 'Gadi Haber']
I am writing a research paper on a idea Complementing missing and inaccurate profiling using a minimum cost circulation algorithm
[{"from": "human", "value": "I am writing a research paper on a idea Complementing missing and inaccurate profiling using a minimum cost circulation algorithm"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Edge profiling is a very common means for providing feedback on program behavior that can be used statically by an optimizer to produce highly optimized binaries. Hover collecting full edge profile carries a significant runtime overhead. This overhead creates addition problems for real-time applications, as it may prevent the system from meeting runtime deadlines and thus alter its behavior.   show how a low overhead sampling technique can be used to collect inaccurate profile which is later used to approximate the full edge profile using a novel technique based on the Minimum Cost Circulation Problem. The outcome is a machine independent profile gathering scheme that creates a slowdown of only 2%-3% during the training set, and produces an optimized binary which is only 0.6% less than a fully optimized one."}]