Performance Prediction Modeling of GPU Applications
SessionDoctoral Showcase Session 3
Presenter
Event Type
Doctoral Showcase
Accelerators
Applications
Architectures
Heterogeneous Systems
Performance
TimeTuesday, November 14th3:48pm -
4:06pm
Location210-212
DescriptionGiven the large number of GPU architectures and the
many different possibilities to execute an algorithm,
the prediction of application execution times over GPUs
is a great challenge and is essential for efficient
JMSs. The available GPU performance modeling solutions
are very sensitive to applications and platform
changes,
Here a summary of two main works is shown. In the first work, we present the comparison of a developed BSP-based model to three different ML techniques, this comparison was done with 9 well-known matrix/vector applications. In this work, we wanted to perform a fair comparison, for this reason, we decided that ML process would had the same features that the BSP-based model.
In the second work, we have compared among ML techniques. Here, a two step of extraction features are done. First a correlation analysis and after hierarchical clustering analysis. In this second work, 10 irregular CUDA kernels were used.
Here a summary of two main works is shown. In the first work, we present the comparison of a developed BSP-based model to three different ML techniques, this comparison was done with 9 well-known matrix/vector applications. In this work, we wanted to perform a fair comparison, for this reason, we decided that ML process would had the same features that the BSP-based model.
In the second work, we have compared among ML techniques. Here, a two step of extraction features are done. First a correlation analysis and after hierarchical clustering analysis. In this second work, 10 irregular CUDA kernels were used.
Presenter
