As computer system technology approaches the end of Moore's law, new computing
paradigms that improve performance become a necessity. One such paradigm is …

High-performance computing communities are increasingly adopting Neural Networks (NN)
as surrogate models in their applications to generate scientific insights. Replacing an …

As we reach the limits of Moore's law and the end of Dennard scaling, increased emphasis
is being given to alternative system architectures and computing paradigms to achieve …

Approximate computing is a well-known method [7] to achieve higher performance or lower
energy consumption while accepting a loss of output accuracy. Many applications such as …

Recent advancements in Machine Learning (ML) have substantially improved its predictive
and computational abilities, offering promising opportunities for surrogate modeling in …

The end of Dennard scaling and the slowdown of Moore's law led to a shift in technology
trends towards parallel architectures, particularly in HPC systems. To continue providing …

Computational workflows are important methods for automating complex data-generation
and analysis pipelines. Workflows are composed of sub-graphs that perform specific tasks …

Memory deduplication effectively relieves the memory space bottleneck by removing
duplicate pages, especially in virtualized systems in which virtual machines run the same …

Energy and performance optimization of embedded hardware and software is of critical
importance to achieve the overall system goals. In this work, we study the optimization of …

The use of approximation is fundamental in computational science. Almost all computational
methods adopt approximations in some form in order to obtain a favourable cost/accuracy …