Machine learning is widely used in our daily lives. Large amounts of data have been
continuously produced and transmitted to the cloud for model training and data processing …

We present filtering methods for finding all sub-graphs of a large multiplex network that are
isomorphic to a smaller template network. These methods are shown to be effective on a set …

Predicting the performance of an application running on parallel computing platforms is
increasingly becoming important because of its influence on development time and resource …

As the US Department of Energy (DOE) invests in exascale computing, performance
modeling of physics codes on CPUs remain a challenge in computational co-design due to …

The use of machine learning techniques to model execution time and power consumption,
and, more generally, to characterize performance data is gaining traction in the HPC …

Understanding the performance characteristics of applications in modern HPC environments
is becoming more challenging due to the increase in the architectural and programming …

Proxy applications are a simplified means for stake-holders to evaluate how both hardware
and software stacks might perform on the class of real applications that they are meant to …

Machine learning has been widely used in our daily lives. Large amounts of data have been
continuously produced and transmitted to the cloud for model training and data processing …

CMT-bone is a proxy app of CMT-nek, which is a solver of the compressible Navier-Stokes
equations for multiphase flows being developed at University of Florida. While the objective …

In this paper, we present a methodology for modeling the expected runtime of a job based
on historical application data and data from the job itself. This estimation model is useful for …