Title: GPU Performance Prediction using Parametrized Models
The recent developments in GP-GPU programming, have lead to the need to port
sequential code to these parallel architectures.
When parallelizing code for heterogeneous platforms it is important to
estimate the benefits of the transformation.
In order to address this problem we developed a GPU cost model which
predicts the run-time and identifies parallelization bottlenecks of
Using our model the user can quickly identify which parts of the program
achieve speedup, thus increasing his productivity when parallelizing code.