Hardware transactional memory promised easy lock‑free programming, but a mix of hardware complexity, unpredictable performance, high silicon cost, and insufficient language and library support kept it from widespread adoption.
This article walks through modeling lock‑free queues and stacks in TLA⁺, proving safety and liveness, and offers practical tips for scaling verification to production code.
A deep dive into how formal verification methods—like model checking and type‑level proofs—eliminate deadlocks in Rust’s concurrent code, with practical examples and tool recommendations.
Explore why static type checkers fall short of ensuring runtime safety, the mechanisms that break static guarantees, and practical strategies to bridge the gap.
Introduction Random numbers are the invisible glue that holds together many of the security guarantees we rely on every day. From establishing encrypted TLS sessions to generating cryptocurrency private keys, the quality of a random number generator (RNG) can be the difference between a system that is provably secure and one that is trivially broken. While “random” may sound informal, in cryptography it carries a very precise meaning: the output must be unpredictable, uniformly distributed, and indistinguishable from true randomness to any feasible adversary. Achieving these properties is far from trivial. A strong random generator must combine high‑entropy sources, robust mixing algorithms, and careful operational practices. ...