Adapting applications to optimally utilize available hardware is no mean feat: the plethora of
choices for optimization techniques are infeasible to maximize manually. To this end, auto …

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

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

Recent years have witnessed phenomenal growth in the application, and capabilities of
graphical processing units (GPUs) due to their high parallel computation power at relatively …

Graphics Processing Units (GPUs) have revolutionized the computing landscape over the
past decade. However, the growing energy demands of data centres and computing …

We present an autotuning approach for compile-time optimization of numerical discretization
methods in simulations of partial differential equations. Our approach is based on data …

We introduce the Bayesian Compiler Optimization framework (BaCO), a general purpose
autotuner for modern compilers targeting CPUs, GPUs, and FPGAs. BaCO provides the …

We formally introduce a systematic (de/re)-composition approach, based on the algebraic
formalism of" Multi-Dimensional Homomorphisms (MDHs)". Our approach is designed as …

Programs with high levels of complexity often face challenges in adjusting execution
parameters, particularly when the ideal value for these parameters may change based on …

We introduce a new scheduling language, based on the formalism of Multi-Dimensional
Homomorphisms (MDH). In contrast to existing scheduling languages, our MDH-based …