Computational science is crucial for delivering reliable weather and climate predictions.
However, despite decades of high-performance computing experience, there is serious …

Data redundancy is ubiquitous in the inputs and intermediate results of Deep Neural
Networks (DNN). It offers many significant opportunities for improving DNN performance and …

Transformers are one of the most important machine learning workloads today. Training one
is a very compute-intensive task, often taking days or weeks, and significant attention has …

Spatial computing architectures promise a major stride in performance and energy efficiency
over the traditional load/store devices currently employed in large scale computing systems …

Python has become the de facto language for scientific computing. Programming in Python
is highly productive, mainly due to its rich science-oriented software ecosystem built around …

Most compilers have a single core intermediate representation (IR)(eg, LLVM) sometimes
complemented with vaguely defined IR-like data structures. This IR is commonly low-level …

In this thought-provoking article, we discuss certain myths and legends that are folklore
among members of the high-performance computing community. We gathered these myths …

Computational fluid dynamics (CFD), in particular applied to turbulent flows, is a research
area with great engineering and fundamental physical interest. However, already at …

The computational efficiency of a state of the art ab initio quantum transport (QT) solver,
capable of revealing the coupled electrothermal properties of atomically-resolved nano …

Python, already one of the most popular languages for scientific computing, has made
significant inroads in High Performance Computing (HPC). At the center of Python's …