arXiv subjects

Yuhang Gan

Publications and source records attributed to Yuhang Gan.

9 recordsLinked to original sources

arXiv2026

Concordia: JIT-Compiled Persistent-Kernel Checkpointing for Fault-Tolerant LLM Inference

Long-running LLM agents keep valuable state resident on GPUs: KV caches, request schedulers, communication state, and sometimes online adapters. Losing this state after a GPU or communicator failure can discard minutes to hours of work, yet existing recovery mechanisms either restart the whole serving stack or require application-specific checkpoint logic inside every attention and runtime component. This paper argues that fault tolerance for such workloads needs a GPU-resident execution context: checkpoint hooks must run at device synchronization points, observe binary kernels that frameworks and libraries actually execute, and recover without putting the host CPU on the critical path. We present Concordia, a runtime that uses a device-resident persistent kernel as the substrate for fault-tolerant LLM inference. Concordia interposes on GPU module loading and supports PTX- and SASS-level instrumentation, allowing checkpoint and pause hooks to be inserted below framework code and library boundaries. For each registered LLM state region, Concordia JIT-compiles a specialized delta-checkpoint handler -- for example, a KV-block scanner, adapter-page scanner, or recovery applier -- and hot-swaps it into the persistent kernel's operator table. The persistent kernel consumes a lock-free ring buffer of compute, checkpoint, append-log, and recovery tasks, so the same always-on executor triggers dirty-page detection, stages deltas, and appends committed records to a CPU-visible log in CXL memory or host DRAM.

cs.DC↗

arXiv2026

One-Prompt Censorship Evasion via Generative Diffusion Models

The escalating arms race between Internet censorship and evasion has driven censors to evolve from static rule-based filtering to sophisticated deep learning-based traffic analysis. While recent automated evasion tools have attempted to counter this by leveraging stochastic search and programmable heuristics, they continue to suffer from insufficient evasion robustness across diverse censorship modalities and poor usability due to complex, mechanism-specific configurations that require manual fitness tuning or domain-specific languages. In this paper, we propose a paradigm shift that reframes censorship evasion as a semantic image-to-image editing task, allowing users to execute it with a single prompt. We introduce FlowPaint, a novel generative framework that leverages the "world knowledge" of large diffusion models to automatically reshape censored traffic into benign patterns. FlowPaint utilizes an instruction-tuned diffusion architecture to perform semantic editing on network flows. Evaluations against both industrial-grade rule-based middleboxes and learning-based classifiers demonstrate that FlowPaint outperforms existing censorship evasion baselines, enabling users to counter diverse censorship paradigms solely by varying natural language instructions

cs.CR↗

arXiv2026

GPUOS: A GPU Operating System Primitive for Transparent Operation Fusion

Modern deep learning workloads often consist of many small tensor operations, especially in inference, attention, and micro-batched training. In these settings, kernel launch overhead can become a major bottleneck, sometimes exceeding the actual computation time. We present GPUOS, a GPU runtime JIT system that reduces launch overhead using a persistent kernel architecture with runtime operator injection. GPUOS runs a single long-lived GPU kernel that continuously processes tasks from a host-managed work queue, eliminating repeated kernel launches. To support diverse operations, GPUOS uses NVIDIA NVRTC to just-in-time compile operators at runtime and inject them into the running kernel through device function pointer tables. This design enables operator updates without restarting the kernel or recompiling the system. GPUOS introduces four key ideas: (1) a persistent worker kernel with atomic task queues, (2) a runtime operator injection mechanism based on NVRTC and relocatable device code, (3) a dual-slot aliasing scheme for safe concurrent operator updates, and (4) transparent PyTorch integration through TorchDispatch that batches micro-operations into unified submissions. The system supports arbitrary tensor shapes, strides, data types, and broadcasting through a generic tensor abstraction. Experiments show that GPUOS achieves up to 15.3x speedup over standard PyTorch on workloads dominated by small operations, including micro-batched inference and attention patterns. GPUOS improves utilization while remaining compatible with the PyTorch ecosystem.

cs.DC↗

arXiv2025

Scalable Community Detection Using Quantum Hamiltonian Descent and QUBO Formulation

We present a quantum-inspired algorithm that utilizes Quantum Hamiltonian Descent (QHD) for efficient community detection. Our approach reformulates the community detection task as a Quadratic Unconstrained Binary Optimization (QUBO) problem, and QHD is deployed to identify optimal community structures. We implement a multi-level algorithm that iteratively refines community assignments by alternating between QUBO problem setup and QHD-based optimization. Benchmarking shows our method achieves up to 5.49\% better modularity scores while requiring less computational time compared to classical optimization approaches. This work demonstrates the potential of hybrid quantum-inspired solutions for advancing community detection in large-scale graph data.

quant-ph↗

arXiv2025

Detect Changes like Humans: Incorporating Semantic Priors for Improved Change Detection

When given two similar images, humans identify their differences by comparing the appearance (e.g., color, texture) with the help of semantics (e.g., objects, relations). However, mainstream binary change detection models adopt a supervised training paradigm, where the annotated binary change map is the main constraint. Thus, such methods primarily emphasize difference-aware features between bi-temporal images, and the semantic understanding of changed landscapes is undermined, resulting in limited accuracy in the face of noise and illumination variations. To this end, this paper explores incorporating semantic priors from visual foundation models to improve the ability to detect changes. Firstly, we propose a Semantic-Aware Change Detection network (SA-CDNet), which transfers the knowledge of visual foundation models (i.e., FastSAM) to change detection. Inspired by the human visual paradigm, a novel dual-stream feature decoder is derived to distinguish changes by combining semantic-aware features and difference-aware features. Secondly, we explore a single-temporal pre-training strategy for better adaptation of visual foundation models. With pseudo-change data constructed from single-temporal segmentation datasets, we employ an extra branch of proxy semantic segmentation task for pre-training. We explore various settings like dataset combinations and landscape types, thus providing valuable insights. Experimental results on five challenging benchmarks demonstrate the superiority of our method over the existing state-of-the-art methods. The code is available at $\href{https://github.com/DREAMXFAR/SA-CDNet}{github}$.

cs.CV↗

arXiv2025

Optimizing Compilation for Distributed Quantum Computing via Clustering and Annealing

Efficiently mapping quantum programs onto Distributed quantum computing (DQC) are challenging, particularly when considering the heterogeneous quantum processing units (QPUs) with different structures. In this paper, we present a comprehensive compilation framework that addresses these challenges with three key insights: exploiting structural patterns within quantum circuits, using clustering for initial qubit placement, and adjusting qubit mapping with annealing algorithms. Experimental results demonstrate the effectiveness of our methods and the capability to handle complex heterogeneous distributed quantum systems. Our evaluation shows that our method reduces the objective value at most 88.40\% compared to the baseline.

quant-ph↗

arXiv2025

CloudQC: A Network-aware Framework for Multi-tenant Distributed Quantum Computing

Distributed quantum computing (DQC) that allows a large quantum circuit to be executed simultaneously on multiple quantum processing units (QPUs) becomes a promising approach to increase the scalability of quantum computing. It is natural to envision the near-future DQC platform as a multi-tenant cluster of QPUs, called a Quantum Cloud. However, no existing DQC work has addressed the two key problems of running DQC in a multi-tenant quantum cloud: placing multiple quantum circuits to QPUs and scheduling network resources to complete these jobs. This work is the first attempt to design a circuit placement and resource scheduling framework for a multi-tenant environment. The proposed framework is called CloudQC, which includes two main functional components, circuit placement and network scheduler, with the objectives of optimizing both quantum network cost and quantum computing time. Experimental results with real quantum circuit workloads show that CloudQC significantly reduces the average job completion time compared to existing DQC placement algorithms for both single-circuit and multi-circuit DQC. We envision this work will motivate more future work on network-aware quantum cloud.

cs.DC↗

arXiv2024

RFL-CDNet: Towards Accurate Change Detection via Richer Feature Learning

Change Detection is a crucial but extremely challenging task of remote sensing image analysis, and much progress has been made with the rapid development of deep learning. However, most existing deep learning-based change detection methods mainly focus on intricate feature extraction and multi-scale feature fusion, while ignoring the insufficient utilization of features in the intermediate stages, thus resulting in sub-optimal results. To this end, we propose a novel framework, named RFL-CDNet, that utilizes richer feature learning to boost change detection performance. Specifically, we first introduce deep multiple supervision to enhance intermediate representations, thus unleashing the potential of backbone feature extractor at each stage. Furthermore, we design the Coarse-To-Fine Guiding (C2FG) module and the Learnable Fusion (LF) module to further improve feature learning and obtain more discriminative feature representations. The C2FG module aims to seamlessly integrate the side prediction from the previous coarse-scale into the current fine-scale prediction in a coarse-to-fine manner, while LF module assumes that the contribution of each stage and each spatial location is independent, thus designing a learnable module to fuse multiple predictions. Experiments on several benchmark datasets show that our proposed RFL-CDNet achieves state-of-the-art performance on WHU cultivated land dataset and CDD dataset, and the second-best performance on WHU building dataset. The source code and models are publicly available at https://github.com/Hhaizee/RFL-CDNet.

cs.CV↗

arXiv2022

An End-to-end Supervised Domain Adaptation Framework for Cross-Domain Change Detection

Existing deep learning-based change detection methods try to elaborately design complicated neural networks with powerful feature representations, but ignore the universal domain shift induced by time-varying land cover changes, including luminance fluctuations and season changes between pre-event and post-event images, thereby producing sub-optimal results. In this paper, we propose an end-to-end Supervised Domain Adaptation framework for cross-domain Change Detection, namely SDACD, to effectively alleviate the domain shift between bi-temporal images for better change predictions. Specifically, our SDACD presents collaborative adaptations from both image and feature perspectives with supervised learning. Image adaptation exploits generative adversarial learning with cycle-consistency constraints to perform cross-domain style transformation, effectively narrowing the domain gap in a two-side generation fashion. As to feature adaptation, we extract domain-invariant features to align different feature distributions in the feature space, which could further reduce the domain gap of cross-domain images. To further improve the performance, we combine three types of bi-temporal images for the final change prediction, including the initial input bi-temporal images and two generated bi-temporal images from the pre-event and post-event domains. Extensive experiments and analyses on two benchmarks demonstrate the effectiveness and universality of our proposed framework. Notably, our framework pushes several representative baseline models up to new State-Of-The-Art records, achieving 97.34% and 92.36% on the CDD and WHU building datasets, respectively. The source code and models are publicly available at https://github.com/Perfect-You/SDACD.

cs.CV↗