arXiv subjects

Zerui Guo

Publications and source records attributed to Zerui Guo.

3 recordsLinked to original sources

arXiv2026

Building A CSFQ-Inspired Transport for Switched CXL Memory Pooling

Emerging switched CXL memory pooling systems, albeit promising, suffer from significant performance interference due to the shared but performance-uncontrolled data path among concurrent memory streams between a host core and a remote DIMM. We systematically characterize a memory pooling appliance based on XConn's Apollo CXL switch and identify three issues: intra-host contention, in-fabric congestion, and unmanaged host-remote DIMM interaction. This paper presents a new transport layer, MemChannel, which provides the mchannel abstraction to manage end-to-end fabric bandwidth among competing memory flows and enable application-specific traffic for switched CXL memory pooling. Our key idea is to build a sender-driven, fabric-informed transport protocol, inspired by Core-Stateless Fair Queueing (CSFQ), that admits just the right amount of CXL requests to each mchannel based on the estimated core-to-CXL-DIMM bandwidth availability. To address CXL-induced idiosyncrasies, MemChannel introduces time-based rate control, host-side admission control, cross-host bookkeeping, new congestion signals, rate estimation based on the fluid model, and delay-based link-capacity adjustment. We build MemChannel from scratch and support unmodified applications. Evaluations over switched memory pooling demonstrate its effectiveness from performance-isolation, scalability, and multi-tenancy perspectives.

cs.NI↗

arXiv2026

A Frequency-Domain Approach for Integrating Multiple Functional Time Series

Integrative analysis of multivariate functional time series (MFTS) is both critical and challenging across many scientific domains. Such data often exhibit complex multi-way dependencies arising from within-curve structures, temporal correlations across curves, and cross-subject interactions, underscoring the need for efficient methods that can jointly capture these dependencies and support accurate downstream analyses. In this work, we propose a novel frequency-domain framework based on a marginal dynamic Karhunen--Lo\`eve expansion. The key idea is to integrate individual spectral densities of the MFTS to construct a marginal spectral operator, whose eigenfunctions yield optimal functional filters. These filters transform complex functional observations into a structured multivariate time series representation, providing a powerful foundation for joint modeling and estimation. Through extensive simulation studies, we demonstrate the superior performance of the proposed approach. We further validate its practical utility through an application to the imputation and forecasting of air pollutant concentration trajectories in China.

stat.ME↗

arXiv2024

A Unified Principal Components Analysis for Stationary Functional Time Series

Functional time series (FTS) data have become increasingly available in real-world applications. Research on such data typically focuses on two objectives: curve reconstruction and forecasting, both of which require efficient dimension reduction. While functional principal component analysis (FPCA) serves as a standard tool, existing methods often fail to achieve simultaneous parsimony and optimality in dimension reduction, thereby restricting their practical implementation. To address this limitation, we propose a novel notion termed optimal functional filters, which unifies and enhances conventional FPCA methodologies. Specifically, we establish connections among diverse FPCA approaches through a dependence-adaptive representer for stationary FTS. Building on this theoretical foundation, we develop an estimation procedure for optimal functional filters that enables both dimension reduction and prediction within a Bayesian modeling framework. Theoretical properties are established for the proposed methodology, and comprehensive simulation studies validate its superiority over competing approaches. We further illustrate our method through an application to reconstructing and forecasting daily air pollutant concentration trajectories.

stat.ME↗