Optimization of Computationally and I/O Intense Patterns in Electronic Structure and Machine Learning Algorithms

Development of scalable High-Performance Computing (HPC) applications is already a challenging task even in the pre-Exascale era. Utilization of the full potential of (near-)future supercomputers will most likely require the mastery of massively parallel heterogeneous architectures with multi-tier persistence systems, ideally in fault tolerant mode. With the change in hardware architectures HPC applications are also widening their scope to `Big data' processing and analytics using machine learning algorithms and neural networks. In this work, in cooperation with the INTERTWinE FET-HPC project, we demonstrate how the GASPI (Global Address Space Programming Interface) programming model helps to address these Exascale challenges on examples of tensor contraction, K-means and Terasort algorithms