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Ethereum Phishing Detection Using Hyperbolic Neural Networks and Temporal Information

Ethereum Phishing Detection Using Hyperbolic Neural Networks...

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international symposium on parallel and distributed Processing with Applications, ISPA

作者： Rong Xu Jun Zhang Xiaoyue Zhu Zhaoxiong Song Zhi Yu College of Computer Science Inner Mongolia University Hohhot China The State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China Institute of Computing Technology Chinese Academy of Sciences Beijing China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

In recent years, the frequent occurrence of phishing scams on Ethereum has posed serious threats to transaction security and the financial safety of users. This paper proposes an Ethereum phishing scam detection method based on Hyperbolic Neural Networks (HGNNs) and temporal information. The method maps the Ethereum transaction network to hyperbolic space for structural feature extraction, effectively capturing hierarchical structures and complex relationships within the graph, thereby improving the accuracy of phishing scam detection. The model includes a structural feature extraction module and a temporal feature extraction module. It uses HGNN and self-attention mechanism to extract the structural features of the transaction graph, and uses a multi-head attention mechanism to capture the dynamic evolution pattern of the graph. Experimental validation on real Ethereum datasets demonstrates that the proposed model outperforms benchmark models, showcasing its effectiveness.

关键词： Attention mechanisms Phishing Neural networks Benchmark testing Feature extraction Real-time systems Blockchains Safety Security Tuning

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A General parallel Framework for Material Point Method Based on the p4est Library

A General Parallel Framework for Material Point Method Based...

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international symposium on parallel and distributed Processing with Applications, ISPA

作者： Shaobo Tian Jiaheng Wang Jialin Li Huang Ye Jian Zhang Computer Network Information Center Chinese Academy of Sciences University of Chinese Academy of Sciences Renmin University of China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Material Point Method(MPM) is widely used to simulate large deformation processes such as material fracture, collision, and fluid structure interaction. In general, an MPM simulation evolves a dynamic process in which a large number of Lagrangian particles move on top of Eulerian grids. During the process, physical quantities such as momentum and force are interpolated frequently between the particles and their corresponding grids. High Performance computing(HPC) has become an indispensable tool for large-scale MPM simulations, while dynamic task decomposition and load balancing are critical for *** this paper, we present a general parallel framework integrating MPM and a well-established oct-tree mesh management library, p4est, aiming to improve the efficiency of large-scale MPM simulations. Through careful design of data structure and interfaces to connect the original Grid class of MPM with the p4est mesh, a highly modular structure is realized for the framework. This design allows the users to easily add new features or optimize existing ones, thereby enhancing its flexibility and reusability. On top of this, dynamic load-balancing strategies for MPM can be realized without much effort. A recommended strategy that considers both particle and grid workload is presented. In addition, the advantage of dynamic load balancing is demonstrated and analyzed through practical simulations. The versatility and efficiency of the proposed framework are also demonstrated through concrete real-world applications including penetration and building implosion simulations with up to 1.5 billion degrees of freedom. The code achieves 87% overall parallel efficiency scaling up to 2048 processors.

关键词： distributed processing Program processors Fluids Deformation Dynamics Buildings Force Load management Libraries Extensibility

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AdCoalescer: An Adaptive Coalescer to Reduce the Inter-Module Traffic in MCM-GPUs

AdCoalescer: An Adaptive Coalescer to Reduce the Inter-Modul...

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Xu Zhang Guangda Zhang Lu Wang Xia Zhao Defense Innovation Institute Academy of Military Sciences Beijing China

ISBN: (数字)9798350364606

ISBN: (纸本)9798350364613

The push for greater computing capabilities has led to the development of Multi-chip-module GPUs (MCM-GPUs), advancing parallel processing potential. Unfortunately, MCM-GPUs encounter a notable challenge, i.e., the performance bottleneck due to the inter-module network. Within MCM-G PUs, a large proportion of memory accesses by Streaming Multiprocessors (SMs) must traverse this inter-module network to access remote memory, encountering bandwidth constraints and increased latency. This is in contrast to the efficient network-on-chip designs in single-module GPU architectures. In MCM-G PUs, we identify significant data access redundancy among SMs within a GPU module which can be coalesced to reduce the network pressure. However, directly coalescing by recording every memory address is inefficient, as a significant number of memory requests are directed to private data addresses, thus underutilizing the hardware resources. To address this challenge, we introduce the Adaptive Coalescer (AdCoalescer). AdCoalescer is a novel framework designed to adaptively coalesce memory requests from different SMs sent to the same cache lines, especially those likely to be concurrently accessed by multiple SMs. Our evaluations validate AdCoalescer design in alleviating the challenges posed by the inter-module network. On average, AdCoalescer achieves a performance improvement of 22.5% (with up to 71.9% improvement) compared to traditional designs with minimal hardware cost.

关键词： distributed processing Costs Conferences Redundancy Memory management Graphics processing units Network-on-chip

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The First international Workshop on Coarse-Grained Reconfigurable Architectures for High-Performance computing (CGRA4HPC)

The First International Workshop on Coarse-Grained Reconfigu...

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Artur Podobas Kentaro Sano Jason Anderson KTH Royal Institute of Technology Sweden RIKEN Center for Computational Science Japan University of Toronto Canada

Welcome to the First international Workshop on Coarse-Grained Reconfigurable Architectures for High-Performance computing (CGRA4HPC), held in conjunction with IPDPS 2022.

ISBN: (数字)9781665497473

ISBN: (纸本)9781665497480

Welcome to the First international Workshop on Coarse-Grained Reconfigurable Architectures for High-Performance computing (CGRA4HPC), held in conjunction with IPDPS 2022.

关键词： Conferences Field programmable gate arrays Reconfigurable architectures Industries High performance computing distributed processing Scientific computing

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Hardware Specialization for distributed computing 21

Hardware Specialization for Distributed Computing

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30th international symposium on High-Performance parallel and distributed computing (HPDC)

作者： Alonso, Gustavo Swiss Fed Inst Technol Dept Comp Sci Zurich Switzerland

ISBN: (纸本)9781450382175

Several trends in the IT industry are driving an increasing specialization of the hardware layers. On the one hand, demanding workloads, large data volumes, diversity in data types, etc. are all factors contributing to make general purpose computing too inefficient. On the other hand, cloud computing and its economies of scale allow vendors to invest on specialized hardware for particular tasks that otherwise would be too expensive or consume resources needed elsewhere. In this talk I will discuss the shift towards hardware acceleration and show with several examples why specialized systems are here to stay and are likely to dominate the computer landscape for years to come. I will also discuss Enzian, an open research platform developed at ETH to enable the exploration of hardware acceleration and present some preliminary results achieved with it.

关键词： distributed computing Accelerators HPC

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Using a Deployable Analysis Environment to Study Continental-Scale Phenology at High-Spatial Resolution

Using a Deployable Analysis Environment to Study Continental...

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IEEE international symposium on Geoscience and Remote Sensing (IGARSS)

作者： Francesco Nattino Emma Izquierdo-Verdiguier Pranav Chandramouli Meiert W. Grootes Ou Ku Fakhereh Alidoost Raúl Zurita-Milla Netherlands eScience Center Science Park 402 (Matrix III) Amsterdam The Netherlands Institute of Geomatics University of Natural Resources and Life Science (BOKU) Vienna Austria Faculty of Geo-Information Science and Earth Observation (ITC) University of Twente Enschede The Netherlands

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

The increasing availability of large open datasets and computational resources offers unprecedented opportunities for new insight and discoveries. However, researchers are often faced with the challenge of running analyses at scale. We consider here an example from the domain of plant phenology and show how a Python-based deployable analysis environment leveraging Dask for parallel and distributed computing as well as Jupyter for interactive data exploration could be employed to run regression-based phenological models at high spatial resolution and continental scale.

关键词： Analytical models Computational modeling Phenology Geoscience and remote sensing distributed databases Data models Spatial resolution

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General Chair

General Chair

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

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Leveraging PaRSEC Runtime Support to Tackle Challenging 3D Data-Sparse Matrix Problems 35

Leveraging PaRSEC Runtime Support to Tackle Challenging 3D D...

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35th IEEE international parallel and distributed Processing symposium (IPDPS)

作者： Cao, Qinglei Pei, Yu Akbudak, Kadir Bosilca, George Ltaief, Hatem Keyes, David Dongarra, Jack Univ Tennessee Innovat Comp Lab Knoxville TN 37996 USA King Abdullah Univ Sci & Technol Extreme Comp Res Ctr Div Comp Elect & Math Sci & Engn Thuwal Saudi Arabia ASELSAN Res Ctr Ankara Turkey Oak Ridge Natl Lab Oak Ridge TN USA Univ Manchester Manchester Lancs England

ISBN: (纸本)9781665440660

The task-based programming model associated with dynamic runtime systems has gained popularity for challenging problems because of workload imbalance, heterogeneous resources, or extreme concurrency. During the last decade, low-rank matrix approximations-where the main idea consists of exploiting data sparsity, typically by compressing off-diagonal tiles up to an application-specific accuracy threshold-have been adopted to address the curse of dimensionality at extreme scale. In this paper, we create a bridge between the runtime and the linear algebra by communicating knowledge of the data sparsity to the runtime. We design and implement this synergistic approach with high user productivity in mind, in the context of the PaRSEC runtime system and the HiCMA numerical library. This requires extending PaRSEC with new features to integrate rank information into the dataflow so that proper decisions can be made at runtime. We focus on the tile low-rank (TLR) Cholesky factorization for solving 3D data-sparse covariance matrix problems arising in environmental applications. In particular, we employ the 3D exponential model of the Match] matrix kernel, which exhibits challenging nonuniform high ranks in off-diagonal tiles. We first provide dynamic data structure management driven by a performance model to reduce extra floating-point operations. Next, we optimize the memory footprint of the application by relying on a dynamic memory allocator, and supported by a rank-aware data distribution to cope with the workload imbalance. Finally, we expose further parallelism using kernel recursive formulations to shorten the critical path. Our resulting high-performance implementation outperforms existing data-sparse TLR Cholesky factorization by up to 7-fold on a large-scale distributed-memory system, while minimizing the memory footprint up to a 44-fold factor. This multidisciplinary work highlights the need to empower runtime systems beyond their original duty of task scheduling fo

关键词： Low-rank matrix computations Task-based programming model Dynamic runtime system Asynchronous executions and load balancing High-performance computing User productivity Environmental applications

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Research on Incentive Mechanisms and Resource Allocation of Edge Intelligence Networks for Multiple Federated Learning Services

Research on Incentive Mechanisms and Resource Allocation of ...

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international symposium on parallel and distributed Processing with Applications, ISPA

作者： Xin Jin Fang Fu Zhicai Zhang School of Physics and Electronic Engineering Shanxi University Taiyuan China School of Computer Science and Technology Hainan University Haikou China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

The integration of artificial intelligence and edge computing has given rise to edge intelligence. Federated Learning (FL), as a key technology of edge intelligence, utilizes data and computation power on edge devices to achieve decentralized edge intelligence. The problem of incentive mechanism design and resource allocation in FL has been two key issues that hinder the smooth progress of the learning process. We study an edge intelligence network that supports multiple simultaneous FL services, and there are multiple task publishers seeking FL services and mobile device alliances providing services for them. We address these two key issues. First, how task publishers can incentivize device alliances to contribute resources to facilitate the execution of FL services. Second, how the device alliances allocate resources across multiple FL services that are in progress to enable bounded rational decision-making. In order to solve the former, we transform the incentive design under information asymmetry into a contract game problem. For the latter, we propose an evolutionary game model to adjust their resource allocation strategies, while considering the influence of bounded rationality and external factors on the decision-making of device alliances. The simulation results show that our incentive mechanism shows excellent performance in the case of information asymmetry. Additionally, We experimentally simulated the dynamic evolution of device alliance strategies.

关键词： Performance evaluation Federated learning Simulation Mechanism design Games Transforms Mobile handsets Resource management Contracts Edge computing

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Revisiting the Underlying Causes of RDMA Scalability Issues

Revisiting the Underlying Causes of RDMA Scalability Issues

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international symposium on parallel and distributed Processing with Applications, ISPA

作者： Junye Zhang Hui Yuan Zhe Wang Peng Yu Hexiang Song Di Qu Siyu Yan Xiaolong Zheng State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications China Huawei Technologies Co. Ltd China Shanghai Jiao Tong University China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Remote direct memory access (RDMA) networks are widely deployed in clouds and data centers for low latency and high throughput. Emerging applications like artificial intelligence training and serverless computing demand RDMA networks with high connection scalability. However, increasing connections can reduce throughput, as many academic research states. Conversely, some industry reports indicate the scalability issues are situation-specific. Despite this, existing work lacks a comprehensive analysis of RDMA scalability issue triggers and causes. In this paper, we revisit triggering conditions and underlying causes of RDMA scalability issues. First, we comprehensively analyze RDMA data flows and potential resource contentions, particularly with direct cache access. Second, we conduct extensive tests across varied measurement settings and RDMA NICs (RNICs). We identify triggering conditions including frequent switching of queue pair connections, rapid request posting, intensive memory access demands and inappropriate program configurations. We also systematically uncover causes of scalability issues, mainly due to RNIC cache misses, RNIC processing unit backpressure, host last-level cache misses, and software inefficiency. Finally, we provide guidelines to mitigate the RDMA scalability issues.

关键词： Training Scalability Systems architecture Serverless computing Switches Bandwidth Throughput Software Guidelines Testing

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