Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Because modern GPGPU can provide significant computing por and has very high memory bandwidth, and also, developer-friendly programming interfaces such as CUDA have been introduced, GPGPU becomes more and more accepted in the HPC research area. Much research has been done to help developers to better optimize GPU applications. But to fully understand GPU performance behavior remains a hot research topic.    developed an analytical tool called TEG (Timing Estimation tool for GPU) to estimate GPU performance. Previous work shows that TEG has good approximation and can help us to quantify bottlenecks' performance effects.  have made some improvement to the tool and in this paper,  use TEG to analyze the GPU performance scaling behavior. TEG takes the dis-assembly output of CUDA kernel binary code and instruction trace as input. It does not execute the codes, but try to model the execution of CUDA codes with timing information. Because TEG takes the native GPU assembly code as input, it can estimate the execution time with a small error and it allows us to get more insight into GPU performance result.
['Junjie Lai', 'André Seznec']
I am writing a research paper on a idea Break down GPU execution time with an analytical method
[{"from": "human", "value": "I am writing a research paper on a idea Break down GPU execution time with an analytical method"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Because modern GPGPU can provide significant computing por and has very high memory bandwidth, and also, developer-friendly programming interfaces such as CUDA have been introduced, GPGPU becomes more and more accepted in the HPC research area. Much research has been done to help developers to better optimize GPU applications. But to fully understand GPU performance behavior remains a hot research topic.    developed an analytical tool called TEG (Timing Estimation tool for GPU) to estimate GPU performance. Previous work shows that TEG has good approximation and can help us to quantify bottlenecks' performance effects.  have made some improvement to the tool and in this paper,  use TEG to analyze the GPU performance scaling behavior. TEG takes the dis-assembly output of CUDA kernel binary code and instruction trace as input. It does not execute the codes, but try to model the execution of CUDA codes with timing information. Because TEG takes the native GPU assembly code as input, it can estimate the execution time with a small error and it allows us to get more insight into GPU performance result."}]