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Yuke Wang

Publications and source records attributed to Yuke Wang.

At least 37 records · Page 2Linked to original sources
arXiv2025

SuperGen: An Efficient Ultra-high-resolution Video Generation System with Sketching and Tiling

Diffusion models have recently achieved remarkable success in generative tasks (e.g., image and video generation), and the demand for high-quality content (e.g., 2K/4K videos) is rapidly increasing across various domains. However, generating ultra-high-resolution videos on existing standard-resolution (e.g., 720p) platforms remains challenging due to the excessive re-training requirements and prohibitively high computational and memory costs. To this end, we introduce SUPERGEN, an efficient tile-based framework for ultra-high-resolution video generation. SUPERGEN features a novel training-free algorithmic innovation with tiling to successfully support a wide range of resolutions without additional training efforts while significantly reducing both memory footprint and computational complexity. Moreover, SUPERGEN incorporates a tile-tailored, adaptive, region-aware caching strategy that accelerates video generation by exploiting redundancy across denoising steps and spatial regions. SUPERGEN also integrates cache-guided, communication-minimized tile parallelism for enhanced throughput and minimized latency. Evaluations show that SUPERGEN maximizes performance gains while achieving high output quality across various benchmarks.

cs.LG↗
arXiv2025

An Efficient and Adaptive Watermark Detection System with Tile-based Error Correction

Efficient and reliable detection of generated images is critical for the responsible deployment of generative models. Existing approaches primarily focus on improving detection accuracy and robustness under various image transformations and adversarial manipulations, yet they largely overlook the efficiency challenges of watermark detection across large-scale image collections. To address this gap, we propose QRMark, an efficient and adaptive end-to-end method for detecting embedded image watermarks. The core idea of QRMark is to combine QR Code-inspired error correction with tailored tiling techniques to improve detection efficiency while preserving accuracy and robustness. At the algorithmic level, QRMark employs a Reed-Solomon error correction mechanism to mitigate the accuracy degradation introduced by tiling. At the system level, QRMark implements a resource-aware multi-channel horizontal fusion policy that adaptively assigns more streams to GPU-intensive stages of the detection pipeline. It further employs a tile-based workload interleaving strategy to overlap data-loading overhead with computation and schedules kernels across stages to maximize efficiency. End-to-end evaluations show that QRMark achieves an average 2.43x inference speedup over the sequential baseline.

cs.DC↗
arXiv2025

PATCHEDSERVE: A Patch Management Framework for SLO-Optimized Hybrid Resolution Diffusion Serving

The Text-to-Image (T2I) diffusion model has emerged as one of the most widely adopted generative models. However, serving diffusion models at the granularity of entire images introduces significant challenges, particularly under multi-resolution workloads. First, image-level serving obstructs batching across requests. Second, heterogeneous resolutions exhibit distinct locality characteristics, making it difficult to apply a uniform cache policy effectively. To address these challenges, we present PatchedServe, a Patch Management Framework for SLO-Optimized Hybrid-Resolution Diffusion Serving. PatchedServe is the first SLO-optimized T2I diffusion serving framework designed to handle heterogeneous resolutions. Specifically, it incorporates a novel patch-based processing workflow that substantially improves throughput for hybrid-resolution inputs. Moreover, PatchedServe devises a patch-level cache reuse policy to fully exploit diffusion redundancies and integrates an SLO-aware scheduling algorithm with lightweight online latency prediction to improve responsiveness. Our evaluation demonstrates that PatchedServe achieves 30.1 % higher SLO satisfaction than the state-of-the-art diffusion serving system, while preserving image quality.

cs.DC↗
arXiv2025

HedraRAG: Coordinating LLM Generation and Database Retrieval in Heterogeneous RAG Serving

This paper addresses emerging system-level challenges in heterogeneous retrieval-augmented generation (RAG) serving, where complex multi-stage workflows and diverse request patterns complicate efficient execution. We present HedraRAG, a runtime system built on a graph-based abstraction that exposes optimization opportunities across stage-level parallelism, intra-request similarity, and inter-request skewness. These opportunities are realized through dynamic graph transformations, such as node splitting, reordering, edge addition, and dependency rewiring, applied to wavefronts of subgraphs spanning concurrent requests. The resulting execution plans are mapped onto hybrid CPU-GPU pipelines to improve resource utilization and reduce latency. Evaluations across a wide range of RAG workflows demonstrate speedups exceeding 1.5x and reaching up to 5x over existing frameworks, showcasing the effectiveness of coordinated generation and retrieval in serving environments.

cs.DB↗
arXiv2025

Utilizing Sequential Information of General Lab-test Results and Diagnoses History for Differential Diagnosis of Dementia

Early diagnosis of Alzheimer's Disease (AD) faces multiple data-related challenges, including high variability in patient data, limited access to specialized diagnostic tests, and overreliance on single-type indicators. These challenges are exacerbated by the progressive nature of AD, where subtle pathophysiological changes often precede clinical symptoms by decades. To address these limitations, this study proposes a novel approach that takes advantage of routinely collected general laboratory test histories for the early detection and differential diagnosis of AD. By modeling lab test sequences as "sentences", we apply word embedding techniques to capture latent relationships between tests and employ deep time series models, including long-short-term memory (LSTM) and Transformer networks, to model temporal patterns in patient records. Experimental results demonstrate that our approach improves diagnostic accuracy and enables scalable and costeffective AD screening in diverse clinical settings.

q-bio.QM↗
arXiv2024

Empowering Distributed Training with Sparsity-driven Data Synchronization

Distributed training is the de facto standard to scale up the training of deep learning models with multiple GPUs. Its performance bottleneck lies in communications for gradient synchronization. Although high tensor sparsity is widely observed, the optimal communication scheme to fully leverage sparsity is still missing. This paper aims to bridge this gap. We first analyze the characteristics of sparse tensors in popular models to understand the fundamentals of sparsity. We then systematically explore the design space of communication schemes for sparse tensors and find the optimal ones. These findings give a new understanding and inspire us to develop a holistic gradient synchronization system called Zen for sparse tensors. We demonstrate that Zen can achieve up to 5.09x speedup in communication time and up to $2.48\times$ speedup in training throughput compared to the state-of-the-art methods.

cs.LG↗
arXiv2023

MGG: Accelerating Graph Neural Networks with Fine-grained intra-kernel Communication-Computation Pipelining on Multi-GPU Platforms

The increasing size of input graphs for graph neural networks (GNNs) highlights the demand for using multi-GPU platforms. However, existing multi-GPU GNN systems optimize the computation and communication individually based on the conventional practice of scaling dense DNNs. For irregularly sparse and fine-grained GNN workloads, such solutions miss the opportunity to jointly schedule/optimize the computation and communication operations for high-performance delivery. To this end, we propose MGG, a novel system design to accelerate full-graph GNNs on multi-GPU platforms. The core of MGG is its novel dynamic software pipeline to facilitate fine-grained computation-communication overlapping within a GPU kernel. Specifically, MGG introduces GNN-tailored pipeline construction and GPU-aware pipeline mapping to facilitate workload balancing and operation overlapping. MGG also incorporates an intelligent runtime design with analytical modeling and optimization heuristics to dynamically improve the execution performance. Extensive evaluation reveals that MGG outperforms state-of-the-art full-graph GNN systems across various settings: on average 4.41X, 4.81X, and 10.83X faster than DGL, MGG-UVM, and ROC, respectively.

cs.DC↗
arXiv2023

OneQ: A Compilation Framework for Photonic One-Way Quantum Computation

In this paper, we propose OneQ, the first optimizing compilation framework for one-way quantum computation towards realistic photonic quantum architectures. Unlike previous compilation efforts for solid-state qubit technologies, our innovative framework addresses a unique set of challenges in photonic quantum computing. Specifically, this includes the dynamic generation of qubits over time, the need to perform all computation through measurements instead of relying on 1-qubit and 2-qubit gates, and the fact that photons are instantaneously destroyed after measurements. As pioneers in this field, we demonstrate the vast optimization potential of photonic one-way quantum computing, showcasing the remarkable ability of OneQ to reduce computing resource requirements by orders of magnitude.

quant-ph↗
arXiv2023

TC-GNN: Bridging Sparse GNN Computation and Dense Tensor Cores on GPUs

Recently, graph neural networks (GNNs), as the backbone of graph-based machine learning, demonstrate great success in various domains (e.g., e-commerce). However, the performance of GNNs is usually unsatisfactory due to the highly sparse and irregular graph-based operations. To this end, we propose TC-GNN, the first GNN acceleration framework based on GPU Tensor Core Units (TCUs). The core idea is to reconcile the "Sparse" GNN computation with the high-performance "Dense" TCUs. Specifically, we conduct an in-depth analysis of the sparse operations in mainstream GNN computing frameworks. We introduce a novel sparse graph translation technique to facilitate TCU processing of the sparse GNN workload. We implement an effective CUDA core and TCU collaboration design to fully utilize GPU resources. We integrate TC-GNN with the PyTorch framework for high programmability. Rigorous experiments show an average of 1.70X speedup over the state-of-the-art DGL framework across various models and datasets.

cs.LG↗
arXiv2023

ALCOP: Automatic Load-Compute Pipelining in Deep Learning Compiler for AI-GPUs

Pipelining between data loading and computation is a critical tensor program optimization for GPUs. In order to unleash the high performance of latest GPUs, we must perform a synergetic optimization of multi-stage pipelining across the multi-level buffer hierarchy of GPU. Existing frameworks rely on hand-written libraries such as cuBLAS to perform pipelining optimization, which is inextensible to new operators and un-composable with prior tensor compiler optimizations. This paper presents ALCOP, the first framework that is compiler-native and fully supports multi-stage multi-level pipelining. ALCOP overcomes three critical obstacles in generating code for pipelining: detection of pipelining-applicable buffers, program transformation for multi-level multi-stage pipelining, and efficient schedule parameter search by incorporating static analysis. Experiments show that ALCOP can generate programs with 1.23x speedup on average (up to 1.73x) over vanilla TVM. On end-to-end models, ALCOP can improve upon TVM by up to 1.18x, and XLA by up to 1.64x. Besides, our performance model significantly improves the efficiency of the schedule tuning process and can find schedules with 99% of the performance given by exhaustive search while costing 40x fewer trials.

cs.DC↗
arXiv2022

Faith: An Efficient Framework for Transformer Verification on GPUs

Transformer verification draws increasing attention in machine learning research and industry. It formally verifies the robustness of transformers against adversarial attacks such as exchanging words in a sentence with synonyms. However, the performance of transformer verification is still not satisfactory due to bound-centric computation which is significantly different from standard neural networks. In this paper, we propose Faith, an efficient framework for transformer verification on GPUs. We first propose a semantic-aware computation graph transformation to identify semantic information such as bound computation in transformer verification. We exploit such semantic information to enable efficient kernel fusion at the computation graph level. Second, we propose a verification-specialized kernel crafter to efficiently map transformer verification to modern GPUs. This crafter exploits a set of GPU hardware supports to accelerate verification specialized operations which are usually memory-intensive. Third, we propose an expert-guided autotuning to incorporate expert knowledge on GPU backends to facilitate large search space exploration. Extensive evaluations show that Faith achieves $2.1\times$ to $3.4\times$ ($2.6\times$ on average) speedup over state-of-the-art frameworks.

cs.LG↗
arXiv2022

GMI-DRL: Empowering Multi-GPU Deep Reinforcement Learning with GPU Spatial Multiplexing

With the increasing popularity of robotics in industrial control and autonomous driving, deep reinforcement learning (DRL) raises the attention of various fields. However, DRL computation on the modern powerful GPU platform is still inefficient due to its heterogeneous workloads and interleaved execution paradigm. To this end, we propose GMI-DRL, a systematic design to accelerate multi-GPU DRL via GPU spatial multiplexing. We introduce a novel design of resource-adjustable GPU multiplexing instances (GMIs) to match the actual needs of DRL tasks, an adaptive GMI management strategy to simultaneously achieve high GPU utilization and computation throughput, and a highly efficient inter-GMI communication support to meet the demands of various DRL communication patterns. Comprehensive experiments reveal that GMI-DRL outperforms state-of-the-art NVIDIA Isaac Gym with NCCL (up to 2.81X) and Horovod (up to 2.34X) support in training throughput on the latest DGX-A100 platform. Our work provides an initial user experience with GPU spatial multiplexing in processing heterogeneous workloads with a mixture of computation and communication.

cs.DC↗
arXiv2021

QGTC: Accelerating Quantized Graph Neural Networks via GPU Tensor Core

Over the most recent years, quantized graph neural network (QGNN) attracts lots of research and industry attention due to its high robustness and low computation and memory overhead. Unfortunately, the performance gains of QGNN have never been realized on modern GPU platforms. To this end, we propose the first Tensor Core (TC) based computing framework, QGTC, to support any-bitwidth computation for QGNNs on GPUs. We introduce a novel quantized low-bit arithmetic design based on the low-bit data representation and bit-decomposed computation. We craft a novel TC-tailored CUDA kernel design by incorporating 3D-stacked bit compression, zero-tile jumping, and non-zero tile reuse technique to improve the performance systematically. We incorporate an effective bandwidth-optimized subgraph packing strategy to maximize the transferring efficiency between CPU host and GPU device. We integrate QGTC with Pytorch for better programmability and extensibility. Extensive experiments demonstrate that QGTC achieves on average 2.7x speedup compared with the state-of-the-art Deep Graph Library framework across diverse settings.

cs.DC↗
arXiv2021

Towards Efficient Ansatz Architecture for Variational Quantum Algorithms

Variational quantum algorithms are expected to demonstrate the advantage of quantum computing on near-term noisy quantum computers. However, training such variational quantum algorithms suffers from gradient vanishing as the size of the algorithm increases. Previous work cannot handle the gradient vanishing induced by the inevitable noise effects on realistic quantum hardware. In this paper, we propose a novel training scheme to mitigate such noise-induced gradient vanishing. We first introduce a new cost function of which the gradients are significantly augmented by employing traceless observables in truncated subspace. We then prove that the same minimum can be reached by optimizing the original cost function with the gradients from the new cost function. Experiments show that our new training scheme is highly effective for major variational quantum algorithms of various tasks.

quant-ph↗
arXiv2021

APNN-TC: Accelerating Arbitrary Precision Neural Networks on Ampere GPU Tensor Cores

Over the years, accelerating neural networks with quantization has been widely studied. Unfortunately, prior efforts with diverse precisions (e.g., 1-bit weights and 2-bit activations) are usually restricted by limited precision support on GPUs (e.g., int1 and int4). To break such restrictions, we introduce the first Arbitrary Precision Neural Network framework (APNN-TC) to fully exploit quantization benefits on Ampere GPU Tensor Cores. Specifically, APNN-TC first incorporates a novel emulation algorithm to support arbitrary short bit-width computation with int1 compute primitives and XOR/AND Boolean operations. Second, APNN-TC integrates arbitrary precision layer designs to efficiently map our emulation algorithm to Tensor Cores with novel batching strategies and specialized memory organization. Third, APNN-TC embodies a novel arbitrary precision NN design to minimize memory access across layers and further improve performance. Extensive evaluations show that APNN-TC can achieve significant speedup over CUTLASS kernels and various NN models, such as ResNet and VGG.

cs.DC↗
arXiv2021

An Efficient Quantitative Approach for Optimizing Convolutional Neural Networks

With the increasing popularity of deep learning, Convolutional Neural Networks (CNNs) have been widely applied in various domains, such as image classification and object detection, and achieve stunning success in terms of their high accuracy over the traditional statistical methods. To exploit the potential of CNN models, a huge amount of research and industry efforts have been devoted to optimizing CNNs. Among these endeavors, CNN architecture design has attracted tremendous attention because of its great potential of improving model accuracy or reducing model complexity. However, existing work either introduces repeated training overhead in the search process or lacks an interpretable metric to guide the design. To clear these hurdles, we propose 3D-Receptive Field (3DRF), an explainable and easy-to-compute metric, to estimate the quality of a CNN architecture and guide the search process of designs. To validate the effectiveness of 3DRF, we build a static optimizer to improve the CNN architectures at both the stage level and the kernel level. Our optimizer not only provides a clear and reproducible procedure but also mitigates unnecessary training efforts in the architecture search process. Extensive experiments and studies show that the models generated by our optimizer can achieve up to 5.47% accuracy improvement and up to 65.38% parameters deduction, compared with state-of-the-art CNN structures like MobileNet and ResNet.

cs.CV↗
arXiv2021

GNNAdvisor: An Adaptive and Efficient Runtime System for GNN Acceleration on GPUs

As the emerging trend of graph-based deep learning, Graph Neural Networks (GNNs) excel for their capability to generate high-quality node feature vectors (embeddings). However, the existing one-size-fits-all GNN implementations are insufficient to catch up with the evolving GNN architectures, the ever-increasing graph sizes, and the diverse node embedding dimensionalities. To this end, we propose \textbf{GNNAdvisor}, an adaptive and efficient runtime system to accelerate various GNN workloads on GPU platforms. First, GNNAdvisor explores and identifies several performance-relevant features from both the GNN model and the input graph, and uses them as a new driving force for GNN acceleration. Second, GNNAdvisor implements a novel and highly-efficient 2D workload management, tailored for GNN computation to improve GPU utilization and performance under different application settings. Third, GNNAdvisor capitalizes on the GPU memory hierarchy for acceleration by gracefully coordinating the execution of GNNs according to the characteristics of the GPU memory structure and GNN workloads. Furthermore, to enable automatic runtime optimization, GNNAdvisor incorporates a lightweight analytical model for an effective design parameter search. Extensive experiments show that GNNAdvisor outperforms the state-of-the-art GNN computing frameworks, such as Deep Graph Library ($3.02\times$ faster on average) and NeuGraph (up to $4.10\times$ faster), on mainstream GNN architectures across various datasets.

cs.DC↗