arXiv subjects

Tianyi Cui

Publications and source records attributed to Tianyi Cui.

3 recordsLinked to original sources

arXiv2026

A Programming Paradigm for Spatiotemporal Composability

Modern software -- from plugin systems to self-evolving agent harnesses -- increasingly requires dynamic composition, yet its formal foundations remain underdeveloped. We identify two orthogonal dimensions of the problem: temporal composability, the ability to completely revert a component's side effects upon removal, and spatial composability, the ability to declare and reactively manage inter-component dependencies. We address the two dimensions by lifting classical effect and coeffect concepts to runtime mechanisms. In particular, we formalize revertible effects, in which every context transformation carries an inverse that the runtime holds, establishing temporal composability local to one component. We formalize reactive coeffects, in which every context change is classified against a component's coeffect specification to drive its activation and deactivation, establishing spatial composability local to one component. We then unify the effect context and the coeffect context into a single context type and mediate every effect and coeffect through it, yielding a discipline we call the context paradigm; the mediation induces an observational equivalence up to which the effects of distinct components interleave without disturbing one another. Combining these mechanisms into the notion of a component, we give a calculus of dynamic composition whose metatheory carries spatiotemporal composability from a single component to a whole system of interleaved components. We implement these ideas in Cordis, a meta-framework of spatiotemporal composability that provides a core library with effect tracking and coeffect resolution, as well as a declarative component loader with configuration reconciliation and hot module replacement.

cs.PL↗

arXiv2024

Laconic: Streamlined Load Balancers for SmartNICs

Load balancers are pervasively used inside today's clouds to scalably distribute network requests across data center servers. Given the extensive use of load balancers and their associated operating costs, several efforts have focused on improving their efficiency by implementing Layer-4 load-balancing logic within the kernel or using hardware acceleration. This work explores whether the more complex and connection-oriented Layer-7 load-balancing capability can also benefit from hardware acceleration. In particular, we target the offloading of load-balancing capability onto programmable SmartNICs. We fully leverage the cost and energy efficiency of SmartNICs using three key ideas. First, we argue that a full and complex TCP/IP stack is not required for Layer-7 load balancers and instead propose a lightweight forwarding agent on the SmartNIC. Second, we develop connection management data structures with a high degree of concurrency with minimal synchronization when executed on multi-core SmartNICs. Finally, we describe how the load-balancing logic could be accelerated using custom packet-processing accelerators on SmartNICs. We prototype Laconic on two types of SmartNIC hardware, achieving over 150 Gbps throughput using all cores on BlueField-2, while a single SmartNIC core achieves 8.7x higher throughput and comparable latency to Nginx on a single x86 core.

cs.NI↗

arXiv2015

Ordering-sensitive and Semantic-aware Topic Modeling

Topic modeling of textual corpora is an important and challenging problem. In most previous work, the "bag-of-words" assumption is usually made which ignores the ordering of words. This assumption simplifies the computation, but it unrealistically loses the ordering information and the semantic of words in the context. In this paper, we present a Gaussian Mixture Neural Topic Model (GMNTM) which incorporates both the ordering of words and the semantic meaning of sentences into topic modeling. Specifically, we represent each topic as a cluster of multi-dimensional vectors and embed the corpus into a collection of vectors generated by the Gaussian mixture model. Each word is affected not only by its topic, but also by the embedding vector of its surrounding words and the context. The Gaussian mixture components and the topic of documents, sentences and words can be learnt jointly. Extensive experiments show that our model can learn better topics and more accurate word distributions for each topic. Quantitatively, comparing to state-of-the-art topic modeling approaches, GMNTM obtains significantly better performance in terms of perplexity, retrieval accuracy and classification accuracy.

cs.LG↗