Guy L. Steele

Victor Cacciari Miraldo, Harold Carr, Mark Moir et al.

Authenticated Append-Only Skiplists (AAOSLs) enable maintenance and querying of an authenticated log (such as a blockchain) without requiring any single party to store or verify the entire log, or to trust another party regarding its contents. AAOSLs can help to enable efficient dynamic participation (e.g., in consensus) and reduce storage overhead. In this paper, we formalize an AAOSL originally described by Maniatis and Baker, and prove its key correctness properties. Our model and proofs are machine checked in Agda. Our proofs apply to a generalization of the original construction and provide confidence that instances of this generalization can be used in practice. Our formalization effort has also yielded some simplifications and optimizations.

Jean-Baptiste Tristan, Daniel Huang, Joseph Tassarotti et al.

It is time-consuming and error-prone to implement inference procedures for each new probabilistic model. Probabilistic programming addresses this problem by allowing a user to specify the model and having a compiler automatically generate an inference procedure for it. For this approach to be practical, it is important to generate inference code that has reasonable performance. In this paper, we present a probabilistic programming language and compiler for Bayesian networks designed to make effective use of data-parallel architectures such as GPUs. Our language is fully integrated within the Scala programming language and benefits from tools such as IDE support, type-checking, and code completion. We show that the compiler can generate data-parallel inference code scalable to thousands of GPU cores by making use of the conditional independence relationships in the Bayesian network.