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GreenB plus Tree: an energy-efficient B plus tree for MIMD architectures

CCF TRANSACTIONS ON HIGH PERFORMANCE COMPUTING 2025年第1期7卷 72-84页

作者： Peng, Muchun Wang, Qinglin Liang, Yuechao Guo, Weihao Yang, Shun Liang, Yaling Shi, Yongzhen Cao, Ligang Liu, Jie Natl Universe Def Technol Lab Digitizing Software Frontier Equipment Yanwachi St Changsha 410073 Hunan Peoples R China Natl Universe Def Technol Natl Key Lab Parallel & Distributed Comp Yanwachi St Changsha 410073 Hunan Peoples R China

In the current data-intensive landscape, B+trees are crucial data structures utilized across various fields like databases and web indexing. With the rise of data explosion, the demand for high-performance real-time query processing in database systems has surged. For instance, Alibaba's PolarDB and AnalyticDB systems handle massive query volumes and real-time data processing, highlighting the need for efficient solutions. Traditional approaches leveraging GPUs for B+tree performance enhancement have yielded positive results but suffer from high energy consumption, making their widespread deployment in large data centers impractical. This paper introduces GreenB+Tree, an energy-efficient B+tree optimized for the PEZY-SC3s chip, known for its high energy efficiency and MIMD architecture, which mitigates common GPU memory and warp divergence issues. GreenB+Tree innovatively transforms the B+tree structure into two one-dimensional arrays, significantly reducing memory costs and computational overhead. It further incorporates query-agglomerated optimization (QAO) and the persistent data residency strategy (PDRS) to minimize global memory access and enhance cache efficiency. Experimental evaluations demonstrate that GreenB+Tree achieves a throughput of 62.6 Million Queries Per Second per Watt (MQPS/W), outperforming contemporary GPU-based solutions by approximately 4.5 times.

关键词： B plus tree MIMD Energy-efficient

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Implicit Neural Alignment Network for Arbitrary-scale Space-Time Video Super-Resolution

Implicit Neural Alignment Network for Arbitrary-scale Space-...

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International Joint Conference on Neural Networks (IJCNN)

作者： Jiang, Qin Wang, Qinglin Chi, Lihua Liu, Jie Sci & Technol Parallel & Distributed Proc Lab Paris France Lab Digitizing Software Frontier Equipment Changsha Peoples R China Hunan GuoKe Computil Technol Co Ltd Changsha Hunan Peoples R China

ISBN: (纸本)9798350359329;9798350359312

Space-time video super-resolution (STVSR) is a comprehensive task comprising two subtasks: video super resolution in space dimension and video frame interpolation in time dimension. Conventional decoupled two-stage approaches tend to overlook the intrinsic correlation between the two tasks. Overcoming this challenge requires the development of a unified model capable of simultaneously implementing space-time superresolution across arbitrary scales. Most existing models are confined to training on fixed space upsampling scales or specific frame-rate videos, resulting in limited generalization capabilities for flexible space-time super-resolution scenarios. In response to this limitation, our approach draws inspiration from continuous implicit neural representation. We propose an enhanced Implicit Neural Alignment Network (INAN) based on the VideoINR framework, encompassing feature refinement, precise motion flow estimation, and multi-scale feature fusion to optimize the final implicit neural decoding. Our extensive experimental evaluations on multiple benchmarks underscore the efficacy of the INAN model, indicate its superior performance compared to prior STVSR methods.

关键词： Implicit Neural Representation Alignment Video Frame Interpolation Video Super-Resolution

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A physics-informed generative adversarial network for advancing solutions in ocean acoustics

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PHYSICS OF FLUIDS 2025年第3期37卷

作者： Xia, Rui Guo, Xiao-Wei Zhang, Huajian Li, Genglin Xiao, Jing Xiao, Qisong Song, Min Li, Chao Liu, Jie Natl Univ Def Technol Lab Digitizing Software Frontier Equipment Changsha 410073 Peoples R China Natl Univ Def Technol Coll Comp Sci & Technol Changsha 410073 Peoples R China Natl Univ Def Technol Natl Key Lab Parallel & Distributed Comp Changsha 410073 Peoples R China

Advancements in artificial intelligence, notably the groundbreaking efforts in deep learning exemplified by physics-informed neural networks, have opened up innovative pathways for addressing intricate ocean acoustic problems. However, conventional physics-informed neural networks are limited in solving high-frequency forward and inverse problems. This paper introduces a novel physics-informed generative adversarial network integrating a forward-solving network (generator) and an inverse parameter-estimating network (discriminator). The generator network incorporates convolutional neural networks with hard-constrained boundary conditions and optimized loss functions to effectively predict the solution governed by the time-domain wave equation. For inverse problems, a discriminator is introduced for parameter estimation to complete the generative adversarial network. Furthermore, customized optimization strategies and an adaptive weighting loss function are devised to boost the training performance further. The test results of both forward and reverse cases show the advantage of our model over existing methods in terms of accuracy. The result indicates its vast potential for applications in ocean acoustics engineering.

关键词： Discriminators

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Enhancing Code Representation Learning for Code Search with Abstract Code Semantics

Enhancing Code Representation Learning for Code Search with ...

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International Joint Conference on Neural Networks (IJCNN)

作者： Zhang, Shaojie Ding, Yiwei Hu, Enrui Yu, Yue Zhang, Yu Southern Univ Sci & Technol Dept Comp Sci & Engn Shenzhen Peoples R China Peng Cheng Lab Shenzhen Peoples R China Huawei Distributed & Parallel Software Lab Shenzhen Peoples R China

ISBN: (纸本)9798350359329;9798350359312

Code representation learning is an important way to encode the semantics of source code through pre-training. The learned representation supports a variety of downstream tasks, such as natural language code search and code defect detection. Inspired by pre-trained models for natural language representation learning, existing approaches often treat the source code or its structural information (e.g., Abstract Syntax Tree or AST) as a plain token sequence. Unlike natural language, programming language has its unique code unit information (e.g., identifiers and expressions) and logic information (e.g., the functionality of a code snippet). To further explore those properties, we propose Abstract Code Embedding (AbCE), a selfsupervised learning method that considers the abstract semantics of code logic. Instead of scattered tokens, AbCE treats an entire node or a subtree in an AST as a basic code unit during pre-training, which preserves the entirety of a coding unit. Moreover, AbCE learns the abstract semantics of AST nodes via a self-distillation way. Experimental results show that it achieves significant improvements over state-of-the-art baselines on code search tasks and comparable performance on code clone detection and defect detection tasks even without using contrastive learning or curriculum learning.

关键词： Natural Language Processing Code Comprehension Self-distillation Code Search

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FE-PIRBN: Feature-enhanced physics-informed radial basis neural networks for solving high-frequency electromagnetic scattering problems

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JOURNAL OF COMPUTATIONAL PHYSICS 2025年 527卷

作者： Zhang, Huajian Li, Chao Xia, Rui Chen, Xinhai Xiao, Tiaojie Guo, Xiao-Wei Liu, Jie Natl Univ Def Technol Coll Comp Sci & Technol Changsha 410000 Hunan Peoples R China Natl Univ Def Technol Lab Digitizing Software Frontier Equipment Changsha 410000 Hunan Peoples R China Natl Univ Def Technol Sci & Technol Parallel & Distributed Proc Lab Changsha 410000 Hunan Peoples R China

The solution of electromagnetic scattering problems holds significant application prospects in fields such as target detection and electromagnetic metamaterial design. However, efficient prediction of electromagnetic fields under high-frequency and intricate geometric conditions remains one of the most challenging problems. In this study, we proposed a feature-enhanced physics-informed radial basis neural network (FE-PIRBN) for solving high-frequency electromagnetic scattering problems. As a physics-driven neural network model, FE-PIRBN incorporates a PIRBN core network to preserve the local approximating property during the electromagnetic wave propagation process. It combines multi-resolution hash encoding techniques to optimize the capture of high-frequency electromagnetic field features. Under data-free training, the FE-PIRBN enables the accurate prediction of high-frequency electromagnetic scattering at sub-wavelength scales. Based on the testing results of typical cases including the electromagnetic scattering from single and double cylinder, it is evident that FE-PIRBN achieves computational accuracy within a 1.40%-5.82% relative error compared to numerical simulation results and the reference solutions. In increasing frequency scenarios, FE-PIRBN shows excellent generalization capabilities, achieving significant accuracy improvements and more robust convergence in high- frequency electromagnetic scattering tasks, ensuring future reliability and accuracy in wider band applications. Of particular importance is that as the electromagnetic frequency increases, the computational time for numerical methods grows exponentially. In contrast, FE-PIRBN, as a neural network model, maintains a relatively stable parameter size, resulting in significantly faster computation. This presents a promising and potential solution for addressing large-scale electromagnetic problems in the field.

关键词： High-frequency electromagnetic scattering Physics-informed neural network Hash encoding Radial basis neural network

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Optimizing CSR-Based SpMV on a New MIMD Architecture Pezy-SC3s 1

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23rd International Conference on Algorithms and Architectures for parallel Processing (ICA3PP)

作者： Guo, Jihu Liu, Jie Wang, Qinglin Zhu, Xiaoxiong Natl Univ Def Technol Lab Digitizing Software Frontier Equipment Changsha 410073 Peoples R China Natl Univ Def Technol Natl Key Lab Parallel & Distributed Comp Changsha 410073 Peoples R China

ISBN: (数字)9789819708017

ISBN: (纸本)9789819708000;9789819708017

Sparse matrix-vector multiplication (SpMV) is extensively used in scientific computing and often accounts for a significant portion of the overall computational overhead. Therefore, improving the performance of SpMV is crucial. However, sparse matrices exhibit a sporadic and irregular distribution of non-zero elements, resulting in workload imbalance among threads and challenges in vectorization. To address these issues, numerous efforts have focused on optimizing SpMV based on the hardware characteristics of computing platforms. In this paper, we present an optimization on CSR-Based SpMV, since the CSR format is the most widely used and supported by various high-performance sparse computing libraries, on a novel MIMD computing platform Pezy-SC3s. Based on the hardware characteristics of Pezy-SC3s, we tackle poor data locality, workload imbalance, and vectorization challenges in CSRBased SpMV by employing matrix chunking, applying Atomic Cache for workload scheduling, and utilizing SIMD instructions during performing SpMV. As the first study to investigate SpMV optimization on Pezy-SC3s, we evaluate the performance of our work by comparing it with the CSR-Based SpMV and SpMV provided by Nvidia's CuSparse. Through experiments conducted on 2092 matrices obtained from SuiteSparse, we demonstrate that our optimization achieves a maximum speedup ratio of x17.63 and an average of x1.56 over CSR-Based SpMV and an average bandwidth utilization of 35.22% for large-scale matrices (nnz >= 10(6)) compared with 36.17% obtained using CuSparse. These results demonstrate that our optimization effectively harnesses the hardware resources of Pezy-SC3s, leading to improved performance of CSR-Based SpMV.

关键词： SpMV Optimization CSR-Based SpMV Pezy-SC3s

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A heterogeneous parallel algorithm for the Cartesian discrete ordinates for multizone heterogeneous system

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JOURNAL OF SUPERCOMPUTING 2025年第4期81卷 1-33页

作者： Li, Runhua Wang, Qinglin Liu, Jie Natl Univ Def Technol Coll Comp Sci & Technol Changsha 410073 Hunan Peoples R China Natl Univ Def Technol Lab Digitizing Software Frontier Equipment Changsha 410073 Hunan Peoples R China Natl Univ Def Technol Natl Key Lab Parallel & Distributed Comp Changsha 410073 Hunan Peoples R China

Advancements in computing technology have revolutionized the efficiency and cost-effectiveness of realistic reactor core simulations. The discrete ordinates (SN\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$S_N$$\end{document}) method is a widely adopted approach for numerically solving the Boltzmann transport equation (BTE), which describes neutron distribution in nuclear reactors. Recently, the MT-3000, a novel multizone heterogeneous architecture designed for high-performance computing, has been developed, offering a peak double-precision performance of 11.6 TFLOPS at 1.2 GHz. In this study, we propose an efficient four-level heterogeneous SN\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$S_N$$\end{document} parallel algorithm for structured hexahedral grids on the MT-3000 system. The algorithm incorporates a two-level KBA strategy with an optimized communication scheme among the MT-3000's acceleration cores and employs a software caching technique to reduce memory access latency. Numerical experiments reveal that our algorithm achieves 1.68 TFLOPS on a single MT-3000 chip, representing 14.5% of its peak performance. The heterogeneous parallel algorithm demonstrates strong scaling efficiency, consistently exceeding 50% across 4 to 256 MT-3000 systems. Furthermore, we develop a performance model that closely aligns with the experimental results.

关键词： Discrete ordinates KBA Neutron transport High-performance computing MT-3000

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Modifying the softening process for knowledge distillation

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JOURNAL OF INTELLIGENT & FUZZY SYSTEMS 2022年第3期42卷 2247-2259页

作者： Tan, Chao Liu, Jie Natl Univ Def Technol Sci & Parallel & Distributed Proc Lab Changsha Peoples R China Natl Univ Def Technol Lab Software Engn Complex Syst Changsha Peoples R China

The prime focus of knowledge distillation (KD) seeks a light proxy termed student to mimic the outputs of its heavy neural networks termed teacher, and makes the student run real-time on the resource-limited devices. This paradigm requires aligning the soft logits of both teacher and student. However, few doubts whether the process of softening the logits truly give full play to the teacher-student paradigm. In this paper, we launch several analyses to delve into this issue from scratch. Subsequently, several simple yet effective functions are devised to replace the vanilla KD. The ultimate function can be an effective alternative to its original counterparts and work well with other skills like FitNets. To claim this point, we conduct several visual tasks on individual benchmarks, and experimental results verify the potential of our proposed function in terms of performance gains. For example, when the teacher and student networks are ShuffleNetV2-1.0 and ShuffleNetV2-0.5, our proposed method achieves 40.88% top-1 error rate on Tiny ImageNet.

关键词： Neural network compression knowledge distillation knowledge transfer

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Towards a new paradigm in intelligence-driven computational fluid dynamics simulations

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ENGINEERING APPLICATIONS OF COMPUTATIONAL FLUID MECHANICS 2024年第1期18卷

作者： Chen, Xinhai Wang, Zhichao Deng, Liang Yan, Junjun Gong, Chunye Yang, Bo Wang, Qinglin Zhang, Qingyang Yang, Lihua Pang, Yufei Liu, Jie Natl Univ Def Technol Sci & Technol Parallel & Distributed Proc Lab Changsha 410073 Peoples R China Natl Univ Def Technol Lab Digitizing Software Frontier Equipment ChangshaW 410073 Peoples R China China Aerodynam Res & Dev Ctr Mianyang Peoples R China

Computational Fluid Dynamics (CFD) plays a crucial role in investigating new physical phenomena and exploring the principles of fluid mechanics. However, CFD numerical methods often face the challenges of long research cycles, high costs, and extensive human-computer interactions due to the growing complexity of computational tasks. To meet the burgeoning requirements of contemporary physical sciences, in recent years, the coupling of traditional scientific computing techniques with promising deep learning techniques well-known from computer science have emerged as a new research paradigm. This paradigm aims to create automated, intelligent tools for obtaining valuable insights as well as being able to categorize, predict, and make evidence-based decisions in novel ways. These tools can be used to reduce the reliance on expert experience and laborious computations inherent in existing numerical theories and methods. In this paper, we delve into the essence of science paradigms, the evolution of computing intelligence, and provide a comprehensive overview of the key applications driving the development of a new intelligence paradigm in CFD simulations. In addition, we outline a prototype platform for CFD simulations within this new paradigm. Based on this platform, three intelligent workflows are proposed, anticipating to serve as a reference source for future research and foster the emergence of innovative applications in the field of *** learning techniques emerged as a new method to create automated, intelligent tools for CFD simulations.A review of deep learning methods for mesh pre-processing.A review of deep learning methods for numerical solving.A review of deep learning methods for post-processing visualization.A prototype platform for CFD simulations within the new *** on challenges and future directions.

关键词： Computational fluid dynamics deep learning science paradigm prototype platform intelligent workflow

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Locality-Preserving Graph Traversal With Split Live Migration

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IEEE TRANSACTIONS ON parallel AND distributed SYSTEMS 2024年第10期35卷 1810-1825页

作者： Chen, Rong Wei, Xingda Xiating, Xie Chen, Haibo Shanghai Jiao Tong Univ Inst Parallel & Distributed Syst Shanghai 200240 Peoples R China Engn Res Ctr Domain Specif Operating Syst MOE Shanghai 200240 Peoples R China Chinese Acad Sci Key Lab Syst Software Beijing 100045 Peoples R China

Graph models many real-world data like social, transportation, biology, and communication data. Hence, graph traversal including multi-hop or graph-walking queries has been the key operation atop graph stores. However, since different graph traversals may touch different sets of vertices, it is hard or even impossible to have a one-size-fits-all graph partitioning algorithm that preserves access locality for various graph traversal workloads. Meanwhile, prior shard-based migration faces a dilemma such that coarse-grained migration may incur more migration overhead over increased locality benefits, while fine-grained migration usually requires excessive metadata and incurs non-trivial maintenance costs. We present Pragh, an efficient locality-preserving live graph migration scheme for graph stores in the form of key-value pairs. The key idea of Pragh is a split migration model that only migrates values physically while retaining keys in the initial location. This allows fine-grained migration while avoiding the need to maintain excessive metadata. Pragh integrates an RDMA-friendly location cache from DrTM-KV to provide fully-localized access to migrated data and further makes a novel reuse of the cache replacement policy for lightweight monitoring. Pragh further supports evolving graphs through a check-and-forward mechanism to resolve the conflict between updates and migration of graph data. Evaluations on an 8-node RDMA-capable cluster (100 Gbps) using a representative graph traversal benchmark show that Pragh can increase the throughput by up to 19x and decrease the median latency by up to 94%, thanks to split live migration that eliminates 97% remote accesses. A port of split live migration to Wukong shows up to 2.53x throughput improvement on representative workloads like LUBM-10240, thanks to a reduction of 88% remote accesses. This further confirms the effectiveness and generality of Pragh. Finally, though Pragh focuses on RDMA-based graph traversal, we show it

关键词： Throughput distributed databases Metadata Monitoring Social networking (online) Protocols Production Graph query graph traversal live migration locality preserving RDMA split migration

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