Performance prediction across platforms is increasingly important in today's diverse
computing environments. As both programs and their developers face unprecedented wide …

Performance prediction across platforms is increasingly important as developers can choose
from a wide range of execution platforms. The main challenge remains to perform accurate …

Automated performance modeling and performance prediction of parallel programs are
highly valuable in many use cases, such as in guiding task management and job …

Predicting the execution time of parallel applications in High Performance Computing (HPC)
clusters has served different objectives, including helping developers to find relevant areas …

In order to measure the performance of a parallel machine, a set of application kernels as
benchmarks have often been used. However, it is not always possible to characterize the …

Different techniques for estimating the execution time of parallel applications have been
studied for the last 25 years. These approaches have proposed different methods for …

Predicting the performance of parallel scientific applications is becoming increasingly
complex. Our goal was to characterize the behavior of message-passing applications on …

Traditional means of gathering performance data are tracing, which is limited by the
available storage, and profiling, which has limited accuracy. Performance modeling is often …

Task-based programming models are becoming increasingly important, as they can reduce
the synchronization costs of parallel programs on multi-cores. Instances of the same task …

Accurate prediction of parallel applications' performance is becoming increasingly complex.
We seek to characterize the behavior of message-passing applications by extracting a …