Random program generation—fuzzing—is an effective technique for discovering bugs in
compilers but successful fuzzers require extensive development effort for every language …

Inductive invariants can be robustly synthesized using a learning model where the teacher is
a program verifier who instructs the learner through concrete program configurations …

The Single Instruction Multiple Data (SIMD) architecture, supported by various high-
performance computing platforms, efficiently utilizes data-level parallelism. The SIMD model …

This paper reports on the VerCors tool set for verifying parallel and concurrent software. Its
main characteristics are (i) that it can verify programs under different concurrency models …

Predictive modeling using machine learning is an effective method for building compiler
heuristics, but there is a shortage of benchmarks. Typical machine learning experiments …

This book is the first ever to focus on the emerging field of self-aware computing from an
engineering perspective. It first comprehensively introduces fundamentals for self …

A GPU's computing power lies in its abundant memory bandwidth and massive parallelism.
However, its hardware thread schedulers, despite being able to quickly distribute …

Many interesting program properties like determinism or information flow security are
hyperproperties, that is, they relate multiple executions of the same program …

OpenMP plays a growing role as a portable programming model to harness on-node
parallelism, yet, existing data race checkers for OpenMP have high overheads and generate …

We design learning algorithms for synthesizing invariants using Horn implication
counterexamples (Horn-ICE), extending the ICE-learning model. In particular, we describe a …