Energy consumption is one of the top challenges for achieving the next generation of
supercomputing. Codesign of hardware and software is critical for improving energy …

Conjugate gradient (CG) and biconjugate gradient stabilized (BiCGSTAB) are effective
methods used for solving sparse linear systems. We in this paper propose Mille-feuille, a …

Empirical auto‐tuning and machine learning techniques have been showing high potential
to improve execution time, power consumption, code size, reliability and other important …

Graphics processing units (GPUs) brought huge performance improvements in the scientific
and numerical fields. We present an efficient hybrid CPU/GPU approach that is portable …

The convergence of several unprecedented changes, including formidable new system
design constraints and revolutionary levels of heterogeneity, has made it clear that much of …

In this PhD thesis, we present our research in the domain of high performance software for
computational fluid dynamics (CFD). With the increasing demand of high-resolution …

Solving linear systems of equations is ubiquitous in scientific computing. Therefore,
numerical algorithms for solving them are of paramount importance. There are two main …

In this paper, we tackle a significant bottleneck-the panel factorization step-in the Gauss-
Huard algorithm through a novel parallel computing approach. We address the open …

The expedient design of precision components in aerospace and other high-tech industries
requires simulations of physical phenomena often described by partial differential equations …

Communication overhead in parallel systems can be a significant bottleneck in scaling up
parallel computation. In this paper, we propose event-triggered communication methods to …