Gabriel Rodríguez | Searcharxiv

arXiv2026

Stochastic Volatility in Mean Models with Heavy Tails: A Fast Approximate Bayesian Inference Using Hidden Markov Models

This paper extends the approximate Bayesian estimation framework for Stochastic Volatility in Mean (SVM) models to accommodate heavy-tailed distributions from the Scale Mixture of Normals (SMN) family. To overcome the computational challenges arising from these models, we propose a numerically stable estimation procedure that exploits special functions to eliminate the need for direct numerical integration. Furthermore, the implementation incorporates parallel computing strategies that substantially reduce computational costs. Simulation studies and empirical applications demonstrate that the proposed approach delivers accurate inference while achieving computational times that are approximately an order of magnitude smaller than those required by conventional Markov chain Monte Carlo (MCMC) methods.

stat.CO↗

arXiv2020

Coherence Traffic in Manycore Processors with Opaque Distributed Directories

Manycore processors feature a high number of general-purpose cores designed to work in a multithreaded fashion. Recent manycore processors are kept coherent using scalable distributed directories. A paramount example is the Intel Mesh interconnect, which consists of a network-on-chip interconnecting "tiles", each of which contains computation cores, local caches, and coherence masters. The distributed coherence subsystem must be queried for every out-of-tile access, imposing an overhead on memory latency. This paper studies the physical layout of an Intel Knights Landing processor, with a particular focus on the coherence subsystem, and uncovers the pseudo-random mapping function of physical memory blocks across the pieces of the distributed directory. Leveraging this knowledge, candidate optimizations to improve memory latency through the minimization of coherence traffic are studied. Although these optimizations do improve memory throughput, ultimately this does not translate into performance gains due to inherent overheads stemming from the computational complexity of the mapping functions.

cs.DC↗