Performance is arguably the most crucial attribute that reflects the quality of a configurable
software system. However, given the increasing scale and complexity of modern software …

This review highlights the efficacy of combining federated learning (FL) and transfer learning
(TL) for cancer detection via image analysis. By integrating these techniques, research has …

As parallel applications become more complex, auto-tuning becomes more desirable,
challenging, and time-consuming. We propose, Bliss, a novel solution for auto-tuning …

Performance modeling is an important and active area of research in high-performance
computing (HPC). It helps in better job scheduling and also improves overall performance of …

Machine/deep learning models have been widely adopted for predicting the configuration
performance of software systems. However, a crucial yet unaddressed challenge is how to …

Deep learning is revolutionizing various domains and significantly impacting medical image
analysis. Despite notable progress, numerous challenges remain, necessitating the …

As we enter the exascale computing era, efficiently utilizing power and optimizing the
performance of scientific applications under power and energy constraints has become …

High performance computing applications, runtimes, and platforms are becoming more
configurable to enable applications to obtain better performance. As a result, users are …

We develop a ytopt autotuning framework that leverages Bayesian optimization to explore
the parameter space search and compare four different supervised learning methods within …

Deep learning models assist us in fruit classification, which allow us to use digital images
from cameras to classify a fruit and find its class of ripeness automatically. Apple ripeness …