Deep learning (DL) training algorithms utilize nondeterminism to improve models' accuracy
and training efficiency. Hence, multiple identical training runs (eg, identical training data …

The rapid adoption and the diversification of cloud computing technology exacerbate the
importance of a sound experimental methodology for this domain. This work investigates …

Fuzz testing has enjoyed great success at discovering security critical bugs in real software.
Recently, researchers have devoted significant effort to devising new fuzzing techniques …

Constraining dynamic control transfers is a common technique for mitigating software
vulnerabilities. This defense has been widely and successfully used to protect return …

In the last decade, machine-learning-based compilation has moved from an obscure
research niche to a mainstream activity. In this paper, we describe the relationship between …

Measuring and reporting performance of parallel computers constitutes the basis for
scientific advancement of high-performance computing (HPC). Most scientific reports show …

HPCToolkit is an integrated suite of tools that supports measurement, analysis, attribution,
and presentation of application performance for both sequential and parallel programs …

Benchmarking is a widely used method in experimental computer science, in particular, for
the comparative evaluation of tools and algorithms. As a consequence, a number of …

Control flow defenses against ROP either use strict, expensive, but strong protection against
redirected RET instructions with shadow stacks, or much faster but weaker protections …

After more than twenty-five years of research, memory safety violations remain one of the
major causes of security vulnerabilities in real-world software. Memory-safe languages, like …