arXiv subjects

Mingyu Yan

Publications and source records attributed to Mingyu Yan.

At least 19 recordsLinked to original sources

arXiv2026

ESR-HGNN: Eliminating Semantic Redundancy for Efficient Mini-batch HGNN Inference

Heterogeneous graph neural networks (HGNNs) are highly effective in processing heterogeneous graph data and have been widely adopted in critical domains. As real-world graph data continues to scale, performing direct inference on entire graphs becomes increasingly infeasible, making mini-batch methods the standard approach. However, in end-to-end HGNN inference, metapath-based mini-batch sampling constitutes a significant performance bottleneck due to the extensive random memory accesses induced by the irregular traversal of graph structures. Existing sampling paradigms suffer from excessive redundant traversals caused by inherent semantic redundancy, severely degrading sampling efficiency and, consequently, leading to suboptimal mini-batch inference performance. In this work, we propose a redundancy-aware HGNN sampling paradigm that leverages a metapath trie to reuse traversal paths, effectively eliminating redundant memory accesses. We then map it onto a multi-channel hardware sampling unit denominated ESR-HGNN. Furthermore, we introduce a reusability-driven metapath grouping technique that optimally clusters metapaths to maximize reusable traversal paths within hardware channels, enhancing efficiency in scenarios with semantic parallelism. Extensive experimental results demonstrate that ESR-HGNN achieves an average sampling performance improvement of one order of magnitude over CPU and GPU, accompanied by significant energy savings. Additionally, it delivers substantial speedup in end-to-end mini-batch inference when integrated with GPU and state-of-the-art HGNN inference accelerator.

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arXiv2026

Dynamic Analysis of Centralized Energy Storage Systems -- A Comparison between Grid-following and Grid-forming Controls

This study investigates the small-signal stability of centralized energy storage systems (CESSs) using grid-following (GFL) and grid-forming (GFM) controls, particularly focusing on bidirectional power flow and multiple energy storage systems (ESSs). To address the issue of complex dynamics in CESSs when comprehensive GFL and GFM control loops are considered, high-order dynamics are simplified using the virtual damping method by focusing on the dominant oscillation mode. Damping analysis verifies that CESSs using a single-type control (either GFL or GFM) have dynamic superimposition characteristics. Specifically, as ESS number increases, the damping of GFM-CESSs improves but that of GFL-CESSs decreases. The damping sensitivity shows that the damping of GFM-CESSs is more sensitive to bidirectional power flow and all control loops, whereas that of GFL-CESSs is more sensitive to d-axis control loop. Consequently, GFM-CESSs are preferred for large-scale integration but are limited in scenarios with significant power reversal. If GFL and GFM controls are hybridized in CESSs, the ratio of GFM-CESSs should be constrained to avoid instability from modal resonance between GFL-CESSs and GFM-CESSs. This highlights that implementing GFM-CESSs necessitates considering scenario limitations rather than pursuing maximal integration under hybrid integration conditions. The conclusions are validated through modal analysis and time-domain simulations.

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arXiv2025

A Systematic Characterization of LLM Inference on GPUs

This work presents a systematic characterization of Large Language Model (LLM) inference to address fragmented understanding. Through comprehensive experiments, we establish a four-dimensional analytical framework: (1) Two-Phase Heterogeneity Observation; (2) Microarchitectural Root Cause Analysis; (3) System Scaling Principles; and (4) Emerging Paradigm Boundaries. Our investigation progresses systematically from observation to foresight: identifying performance phenomena, revealing hardware causes, validating system behavior, and exploring new paradigms. This study not only consolidates a reliable empirical foundation for existing research but also provides new discoveries and practical optimization guidance for LLM inference.

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arXiv2025

TLV-HGNN: Thinking Like a Vertex for Memory-efficient HGNN Inference

Heterogeneous graph neural networks (HGNNs) excel at processing heterogeneous graph data and are widely applied in critical domains. In HGNN inference, the neighbor aggregation stage is the primary performance determinant, yet it suffers from two major sources of memory inefficiency. First, the commonly adopted per-semantic execution paradigm stores intermediate aggregation results for each semantic prior to semantic fusion, causing substantial memory expansion. Second, the aggregation process incurs extensive redundant memory accesses, including repeated loading of target vertex features across semantics and repeated accesses to shared neighbors due to cross-semantic neighborhood overlap. These inefficiencies severely limit scalability and reduce HGNN inference performance. In this work, we first propose a semantics-complete execution paradigm from a vertex perspective that eliminates per-semantic intermediate storage and redundant target vertex accesses. Building on this paradigm, we design TVL-HGNN, a reconfigurable hardware accelerator optimized for efficient aggregation. In addition, we introduce a vertex grouping technique based on cross-semantic neighborhood overlap, with hardware implementation, to reduce redundant accesses to shared neighbors. Experimental results demonstrate that TVL-HGNN achieves average speedups of 7.85x and 1.41x over the NVIDIA A100 GPU and the state-of-the-art HGNN accelerator HiHGNN, respectively, while reducing energy consumption by 98.79% and 32.61%.

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arXiv2025

Accelerating GNN Training through Locality-aware Dropout and Merge

Graph Neural Networks (GNNs) have demonstrated significant success in graph learning and are widely adopted across various critical domains. However, the irregular connectivity between vertices leads to inefficient neighbor aggregation, resulting in substantial irregular and coarse-grained DRAM accesses. This lack of data locality presents significant challenges for execution platforms, ultimately degrading performance. While previous accelerator designs have leveraged on-chip memory and data access scheduling strategies to address this issue, they still inevitably access features at irregular addresses from DRAM. In this work, we propose LiGNN, a hardware-based solution that improves data locality by applying dropout and merge techniques during neighbor aggregation to accelerate GNN training. Unlike conventional algorithm-level dropout methods that primarily aim to improve accuracy while overlooking hardware costs, LiGNN introduces a locality-aware feature dropout mechanism. This approach selectively drops node features with data locality awareness, effectively reducing irregular DRAM accesses without compromising model accuracy. Moreover, by leveraging detailed knowledge of memory layout and organization-including critical alignment constraints-LiGNN strategically merges memory accesses during neighbor aggregation at the DRAM row level, guided by GNN-level semantics. This optimization significantly improves data locality with minimal additional cost. Under the commonly adopted 0.5 dropout rate, LiGNN outperforms state-of-the-art methods, delivering a 1.48~3.02x speedup, reducing DRAM accesses by 34%~55%, and lowering DRAM row activations by 59%~82%, all while maintaining model accuracy.

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arXiv2025

Multi-dimensional evaluation on a rural integrated energy system including solar, wind, biomass and geothermal energy

This study focuses on the novel municipal-scale rural integrated energy system (RIES), which encompasses energy supply and application. By constructing a seven-dimensional evaluation system including energy efficiency, energy supply, low-carbon sustainability, environmental impact, energy economy, social benefits, and integrated energy system development, this research combines the improved analytic hierarchy process (IAHP) and entropy weight method (EWM) by sum of squares of deviations to balance expert experience and data objectivity. Furthermore, the cloud model is introduced to handle the fuzziness and randomness in the evaluation. This method can quantify the differences in system performance before and after the planning implementation. The results indicate that after planning, the comprehensive score has increased from 83.12 to 87.55, the entropy value has decreased from 6.931 to 5.336, indicating enhanced system stability. The hyper-entropy has dropped from 3.08 to 2.278, reflecting a reduction in uncertainty. The research findings provide a scientific basis for the planning optimization, policy-making, and sustainable development of rural integrated energy systems, possessing both theoretical innovation and practical guiding value.

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arXiv2025

Considering the multi-time scale rolling optimization scheduling method of micro-energy network connected to electric vehicles

The large-scale access of electric vehicles to the power grid not only provides flexible adjustment resources for the power system, but the temporal uncertainty and distribution complexity of their energy interaction pose significant challenges to the economy and robustness of the micro-energy network. In this paper, we propose a multi-time scale rolling optimization scheduling method for micro-energy networks considering the access of electric vehicles. In order to solve the problem of evaluating the dispatchable potential of electric vehicle clusters, a charging station aggregation model was constructed based on Minkowski summation theory, and the scattered electric vehicle resources were aggregated into virtual energy storage units to participate in system scheduling. Integrate price-based and incentive-based demand response mechanisms to synergistically tap the potential of source-load two-side regulation; On this basis, a two-stage optimal scheduling model of day-ahead and intra-day is constructed. The simulation results show that the proposed method reduces the scale of "preventive curtailment" due to more accurate scheduling, avoids the threat of power shortage to the safety of the power grid, and has more advantages in the efficiency of new energy consumption. At the same time, intra-day scheduling significantly reduces economic penalties and operating costs by avoiding output shortages, and improves the economy of the system in an uncertain forecasting environment.

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arXiv2025

Large Processor Chip Model

Computer System Architecture serves as a crucial bridge between software applications and the underlying hardware, encompassing components like compilers, CPUs, coprocessors, and RTL designs. Its development, from early mainframes to modern domain-specific architectures, has been driven by rising computational demands and advancements in semiconductor technology. However, traditional paradigms in computer system architecture design are confronting significant challenges, including a reliance on manual expertise, fragmented optimization across software and hardware layers, and high costs associated with exploring expansive design spaces. While automated methods leveraging optimization algorithms and machine learning have improved efficiency, they remain constrained by a single-stage focus, limited data availability, and a lack of comprehensive human domain knowledge. The emergence of large language models offers transformative opportunities for the design of computer system architecture. By leveraging the capabilities of LLMs in areas such as code generation, data analysis, and performance modeling, the traditional manual design process can be transitioned to a machine-based automated design approach. To harness this potential, we present the Large Processor Chip Model (LPCM), an LLM-driven framework aimed at achieving end-to-end automated computer architecture design. The LPCM is structured into three levels: Human-Centric; Agent-Orchestrated; and Model-Governed. This paper utilizes 3D Gaussian Splatting as a representative workload and employs the concept of software-hardware collaborative design to examine the implementation of the LPCM at Level 1, demonstrating the effectiveness of the proposed approach. Furthermore, this paper provides an in-depth discussion on the pathway to implementing Level 2 and Level 3 of the LPCM, along with an analysis of the existing challenges.

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arXiv2025

MetaDSE: A Few-shot Meta-learning Framework for Cross-workload CPU Design Space Exploration

Cross-workload design space exploration (DSE) is crucial in CPU architecture design. Existing DSE methods typically employ the transfer learning technique to leverage knowledge from source workloads, aiming to minimize the requirement of target workload simulation. However, these methods struggle with overfitting, data ambiguity, and workload dissimilarity. To address these challenges, we reframe the cross-workload CPU DSE task as a few-shot meta-learning problem and further introduce MetaDSE. By leveraging model agnostic meta-learning, MetaDSE swiftly adapts to new target workloads, greatly enhancing the efficiency of cross-workload CPU DSE. Additionally, MetaDSE introduces a novel knowledge transfer method called the workload-adaptive architectural mask algorithm, which uncovers the inherent properties of the architecture. Experiments on SPEC CPU 2017 demonstrate that MetaDSE significantly reduces prediction error by 44.3\% compared to the state-of-the-art. MetaDSE is open-sourced and available at this \href{https://anonymous.4open.science/r/Meta_DSE-02F8}{anonymous GitHub.}

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arXiv2025

Exergy Battery Modeling and P2P Trading Based Optimal Operation of Virtual Energy Station

Virtual energy stations (VESs) work as retailers to provide electricity and natural gas sale services for integrated energy systems (IESs), and guide IESs energy consumption behaviors to tackle the varying market prices via integrated demand response (IDR). However, IES customers are risk averse and show low enthusiasm in responding to the IDR incentive signals. To address this problem, exergy is utilized to unify different energies and allowed to be virtually stored and withdrawn for arbitrage by IESs. The whole incentive mechanism operating process is innovatively characterized by a virtual exergy battery. Peer to peer (P2P) exergy trading based on shared exergy storage is also developed to reduce the energy cost of IESs without any extra transmission fee. In this way, IES can reduce the economic loss risk caused by the market price fluctuation via the different time (time dimension), multiple energy conversion (energy dimension), and P2P exergy trading (space dimension) arbitrage. Moreover, the optimal scheduling of VES and IESs is modeled by a bilevel optimization model. The consensus based alternating direction method of multipliers (CADMM) algorithm is utilized to solve this problem in a distributed way. Simulation results validate the effectiveness of the proposed incentive mechanism and show that the shared exergy storage can enhance the benefits of different type IESs by 18.96%, 3.49%, and 3.15 %, respectively.

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arXiv2025

A Profit Sharing Mechanism for Coordinated Power Traffic System

The transportation network operator (TNO) and the power distribution network operator (DNO) act non cooperatively during the scheduling process. Under the TNOs management, the distribution of charging load may exacerbate the local supply-demand imbalance in the power distribution network (PDN), which negatively impacts the secure and economic operation of the PDN. This paper proposes a profit sharing mechanism based on the principle of incentive compatibility for coordinating the transportation network (TN) and the PDN to minimize the total operation cost of the PDN. In this mechanism, the scheduling process of the power transportation system is divided into two stages. At the prescheduling stage, the TNO allocates traffic flow and charging load without considering the operation of the PDN, after which the DNO schedules and obtains the original cost. At the rescheduling stage, the DNO shares part of the saved dispatch cost to motivate the TNO to reallocate the EVs charging, which is more beneficial to the operation of the PDN. This two-stage process is then simulated by two single level models and a bilevel model. Finally, the optimal sharing ratio is identified, at which the total scheduling cost of the DNO can decrease to the lowest point when gaming with the TNO. The efficiency of the proposed mechanism is simulated via a coupled network with 12 traffic nodes and 18 electric buses. Numerical results demonstrate that the DNO can achieve the minimum total cost. Simultaneously, the TNO can also benefit from the proposed profit-sharing mechanism.

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arXiv2024

Leveraging Large Language Models for Effective Label-free Node Classification in Text-Attributed Graphs

Graph neural networks (GNNs) have become the preferred models for node classification in graph data due to their robust capabilities in integrating graph structures and attributes. However, these models heavily depend on a substantial amount of high-quality labeled data for training, which is often costly to obtain. With the rise of large language models (LLMs), a promising approach is to utilize their exceptional zero-shot capabilities and extensive knowledge for node labeling. Despite encouraging results, this approach either requires numerous queries to LLMs or suffers from reduced performance due to noisy labels generated by LLMs. To address these challenges, we introduce Locle, an active self-training framework that does Label-free node Classification with LLMs cost-Effectively. Locle iteratively identifies small sets of "critical" samples using GNNs and extracts informative pseudo-labels for them with both LLMs and GNNs, serving as additional supervision signals to enhance model training. Specifically, Locle comprises three key components: (i) an effective active node selection strategy for initial annotations; (ii) a careful sample selection scheme to identify "critical" nodes based on label disharmonicity and entropy; and (iii) a label refinement module that combines LLMs and GNNs with a rewired topology. Extensive experiments on five benchmark text-attributed graph datasets demonstrate that Locle significantly outperforms state-of-the-art methods under the same query budget to LLMs in terms of label-free node classification. Notably, on the DBLP dataset with 14.3k nodes, Locle achieves an 8.08% improvement in accuracy over the state-of-the-art at a cost of less than one cent. Our code is available at https://github.com/HKBU-LAGAS/Locle.

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arXiv2024

Multi-objective Optimization in CPU Design Space Exploration: Attention is All You Need

Design Space Exploration (DSE) is essential to modern CPU design, yet current frameworks struggle to scale and generalize in high-dimensional architectural spaces. As the dimensionality of design spaces continues to grow, existing DSE frameworks face three fundamental challenges: (1) reduced accuracy and poor scalability of surrogate models in large design spaces; (2) inefficient acquisition guided by hand-crafted heuristics or exhaustive search; (3) limited interpretability, making it hard to pinpoint architectural bottlenecks. In this work, we present \textbf{AttentionDSE}, the first end-to-end DSE framework that \emph{natively integrates} performance prediction and design guidance through an attention-based neural architecture. Unlike traditional DSE workflows that separate surrogate modeling from acquisition and rely heavily on hand-crafted heuristics, AttentionDSE establishes a unified, learning-driven optimization loop, in which attention weights serve a dual role: enabling accurate performance estimation and simultaneously exposing the performance bottleneck. This paradigm shift elevates attention from a passive representation mechanism to an active, interpretable driver of design decision-making. Key innovations include: (1) a \textbf{Perception-Driven Attention} mechanism that exploits architectural hierarchy and locality, scaling attention complexity from $\mathcal{O}(n^2)$ to $\mathcal{O}(n)$ via sliding windows; (2) an \textbf{Attention-aware Bottleneck Analysis} that automatically surfaces critical parameters for targeted optimization, eliminating the need for domain-specific heuristics. Evaluated on high-dimensional CPU design space using the SPEC CPU2017 benchmark suite, AttentionDSE achieves up to \textbf{3.9\% higher Pareto Hypervolume} and over \textbf{80\% reduction in exploration time} compared to state-of-the-art baselines.

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arXiv2024

SiHGNN: Leveraging Properties of Semantic Graphs for Efficient HGNN Acceleration

Heterogeneous Graph Neural Networks (HGNNs) have expanded graph representation learning to heterogeneous graph fields. Recent studies have demonstrated their superior performance across various applications, including medical analysis and recommendation systems, often surpassing existing methods. However, GPUs often experience inefficiencies when executing HGNNs due to their unique and complex execution patterns. Compared to traditional Graph Neural Networks, these patterns further exacerbate irregularities in memory access. To tackle these challenges, recent studies have focused on developing domain-specific accelerators for HGNNs. Nonetheless, most of these efforts have concentrated on optimizing the datapath or scheduling data accesses, while largely overlooking the potential benefits that could be gained from leveraging the inherent properties of the semantic graph, such as its topology, layout, and generation. In this work, we focus on leveraging the properties of semantic graphs to enhance HGNN performance. First, we analyze the Semantic Graph Build (SGB) stage and identify significant opportunities for data reuse during semantic graph generation. Next, we uncover the phenomenon of buffer thrashing during the Graph Feature Processing (GFP) stage, revealing potential optimization opportunities in semantic graph layout. Furthermore, we propose a lightweight hardware accelerator frontend for HGNNs, called SiHGNN. This accelerator frontend incorporates a tree-based Semantic Graph Builder for efficient semantic graph generation and features a novel Graph Restructurer for optimizing semantic graph layouts. Experimental results show that SiHGNN enables the state-of-the-art HGNN accelerator to achieve an average performance improvement of 2.95$\times$.

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arXiv2024

Accelerating Mini-batch HGNN Training by Reducing CUDA Kernels

Heterogeneous graph neural networks (HGNNs) are essential for capturing the structure and semantic information in heterogeneous graphs. However, existing GPU-based solutions, such as PyTorch Geometric, suffer from low GPU utilization due to numerous short-execution-time and memory-bound CUDA kernels during HGNN training. To address this issue, we introduce HiFuse, an enhancement for PyTorch Geometric designed to accelerate mini-batch HGNN training on CPU-GPU systems. From the data perspective, we reorganize and merge multiple smaller vertex feature matrices into larger ones, enabling a single kernel to process larger data chunks. This efficiently exploits data locality, reduces the kernel launch overhead, and improves overall GPU utilization. From the workflow perspective, we sophisticatedly offload the construction of semantic graphs from GPU to CPU to reduce the number of CUDA kernels. To meet the parallelism requirements on CPU and ensure seamless execution between CPU and GPU, we employ parallelization techniques including multi-threading and asynchronous pipeline. This allows different stages of the process to overlap, enhancing GPU utilization and reducing end-to-end execution latency, leading to a more efficient and balanced use of computational resources. Through extensive experiments, HiFuse demonstrates an average 2.38 times speedup compared to a state-of-the-art solution.

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arXiv2024

Survey on Characterizing and Understanding GNNs from a Computer Architecture Perspective

Characterizing and understanding graph neural networks (GNNs) is essential for identifying performance bottlenecks and facilitating their deployment in parallel and distributed systems. Despite substantial work in this area, a comprehensive survey on characterizing and understanding GNNs from a computer architecture perspective is lacking. This work presents a comprehensive survey, proposing a triple-level classification method to categorize, summarize, and compare existing efforts, particularly focusing on their implications for parallel architectures and distributed systems. We identify promising future directions for GNN characterization that align with the challenges of optimizing hardware and software in parallel and distributed systems. Our survey aims to help scholars systematically understand GNN performance bottlenecks and execution patterns from a computer architecture perspective, thereby contributing to the development of more efficient GNN implementations across diverse parallel architectures and distributed systems.

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arXiv2024

Characterizing and Understanding HGNN Training on GPUs

Owing to their remarkable representation capabilities for heterogeneous graph data, Heterogeneous Graph Neural Networks (HGNNs) have been widely adopted in many critical real-world domains such as recommendation systems and medical analysis. Prior to their practical application, identifying the optimal HGNN model parameters tailored to specific tasks through extensive training is a time-consuming and costly process. To enhance the efficiency of HGNN training, it is essential to characterize and analyze the execution semantics and patterns within the training process to identify performance bottlenecks. In this study, we conduct an in-depth quantification and analysis of two mainstream HGNN training scenarios, including single-GPU and multi-GPU distributed training. Based on the characterization results, we disclose the performance bottlenecks and their underlying causes in different HGNN training scenarios and provide optimization guidelines from both software and hardware perspectives.

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arXiv2024

ADE-HGNN: Accelerating HGNNs through Attention Disparity Exploitation

Heterogeneous Graph Neural Networks (HGNNs) have recently demonstrated great power in handling heterogeneous graph data, rendering them widely applied in many critical real-world domains. Most HGNN models leverage attention mechanisms to significantly improvemodel accuracy, albeit at the cost of increased computational complexity and memory bandwidth requirements. Fortunately, the attention disparity from source vertices towards a common target vertex unveils an opportunity to boost the model execution performance by pruning unimportant source vertices during neighbor aggregation. In this study, we commence with a quantitative analysis of the attention disparity in HGNN models, where the importance of different source vertices varies for the same target vertex. To fully exploit this finding for inference acceleration, we propose a runtime pruning method based on min-heap and map it to a dedicated hardware pruner to discard unimportant vertices. Given that the pruning overhead itself is non-negligible and cannot be amortized by conventional staged execution paradigm, an operation-fusion execution fow of HGNNs is introduced to overlap the pruning overhead while harnessing inter-stage parallelism. Finally, we present the design of a novel HGNN accelerator, ADE-HGNN, tailored to support the proposed execution framework. Our experimental results demonstrate that ADE-HGNN achieves an average performance improvement of 28.21x over the NVIDIA GPU T4 platform and 7.98x over the advanced GPU A100, with the inference accuracy loss kept within a negligible range of 0.11%~1.47%. Furthermore, ADE-HGNN significantly reduces energy consumption to 1.97% and 5.37% of the two platforms, respectively.

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