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ADTopk: All-Dimension Top-k Compression for High-Performance Data-parallel DNN Training 24

ADTopk: All-Dimension Top-k Compression for High-Performance...

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33rd international symposium on High-Performance parallel and distributed computing (HPDC)

作者： Ming, Zhangqiang Hu, Yuchong Zhou, Wenxiang Zheng, Xinjue Yao, Chenxuan Feng, Dan Huazhong Univ Sci & Technol Wuhan Hubei Peoples R China Huazhong Univ Sci & Technol Shenzhen Res Inst Shenzhen Guangdong Peoples R China

ISBN: (纸本)9798400704130

Data-parallel deep neural networks (DNN) training systems deployed across nodes have been widely used in various domains, while the system performance is often bottlenecked by the communication overhead among workers for synchronizing gradients. Top-k sparsification compression is the de facto approach to alleviate the communication bottleneck, which truncates the gradient to its largest.. elements before sending it to other nodes. However, we observe that the traditional Top-k still has performance issues: i) the gradient at each layer of a DNN is typically represented as a tensor of multiple dimensions, and the largest.. elements selected by the traditional Top-k are centered in only some of all dimensions and hence the training may miss many dimensions (we call dimension missing), which leads to low convergence performance;ii) the traditional Top-k performs the selection by globally sorting the gradient elements in each layer (we call single global sorting), which leads to a low GPU core parallelism and hence a low training throughput. In this paper, we propose an all-dimension Top-k sparsification scheme, called ADTopk, which selects the largest.. elements from all dimensions of the gradient tensor in each layer, meaning that each dimension must provide some elements, so as to avoid the dimension missing. Further, ADTopk enables each dimension to perform sorting locally within the elements of the dimension, and thus all dimensions can perform multiple local sortings independently and parallelly, instead of a single global sorting for the entire gradient tensor in each layer. On top of ADTopk, we further propose an interleaving compression scheme and an efficient threshold estimation algorithm so as to enhance the performance of ADTopk. We build a sparsification compression data-parallel DNN training framework and implement a compression library containing state-of-the-art sparsification algorithms. Experiments on a local cluster and Alibaba Cloud show that compa

关键词： High Performance Data-parallel DNN Training Gradient Sparsification Compression

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Ethereum Phishing Detection Using Hyperbolic Neural Networks and Temporal Information 22

Ethereum Phishing Detection Using Hyperbolic Neural Networks...

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22nd IEEE international symposium on parallel and distributed Processing with Applications, ISPA 2024

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

ISBN: (纸本)9798331509712

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. © 2024 IEEE.

关键词： Phishing

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A Scalable parallel Partition Tridiagonal Solver for Many-Core and Low B/F Processors 36

A Scalable Parallel Partition Tridiagonal Solver for Many-Co...

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36th IEEE international parallel and distributed Processing symposium (IEEE IPDPS)

作者： Mitsuda, Tatsuya Ono, Kenji Kyushu Univ Grad Sch Fukuoka Japan Kyushu Univ Fac Informat Sci & Elect Engn Fukuoka Japan Kyushu Univ Res Inst Informat & Technol Fukuoka Japan

ISBN: (纸本)9781665497473

Tridiagonal systems are among the most fundamental computations in science, engineering, and mathematics, and one solver used in such systems is Tree Partitioning Reduction (TPR), which is a divide-and-conquer method that solves large-scale linear equations by dividing them and then computing the parts in parallel within different local memory threads. Herein, we propose an improved TPR algorithm that has a parallel cyclic reduction flavor, with which we reduced the number of algorithm steps by approximately half while simultaneously increasing arithmetic intensity and cache reusability. A performance evaluation conducted on an Intel Skylake-SP microprocessor showed a high hit ratio for the L1 cache and that our solver was as much as 31 times faster on 32 threads for 262144 equations. In the case of a Nvidia Tesla P100 GPU, our method processed 10 MRow/s more than TPR and cuSPARSE.

关键词： Tridiagonal system GPU parallel computing Multi/many-core

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Secure Range Queries on Semantic Trajectories in Fog-based Cloud computing 22

Secure Range Queries on Semantic Trajectories in Fog-based C...

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22nd IEEE international symposium on parallel and distributed Processing with Applications, ISPA 2024

作者： Teng, Yiping Wang, Shiqing Li, Miao Tang, Yuyao Yu, Bingfeng Fan, Chunlong Shenyang Aerospace University School of Computer Liaoning Shenyang China

ISBN: (纸本)9798331509712

With the advances in positioning techniques, trajectories are emerged with semantic information such as location-based activities and sign-ins. Essential for applications such as trip recommendations, range queries on semantic trajectories are utilized to identify trajectories that satisfy specific spatio-temporal conditions as well as keyword-related criteria. To reduce the costs of query processing services, data owners often outsource the data services to public platforms, of which the fog-based cloud can offer enhanced storage and computing capabilities and support efficient access to user data. However, outsourcing data in plaintext may raise privacy concerns. To this end, we study the problem of secure range queries on semantic trajectories on the fog-based cloud platform. Initially, to effectively organize semantic trajectories, we design a secure index structure within fog-based cloud framework by distributing spatio-temporal data of trajectories across the fog servers based on semantic contents. Subsequently, we propose a secure range query scheme where keyword pruning prioritizes the fog servers containing query keywords, and then secure spatio-temporal pruning is performed in parallel to obtain the candidate trajectories satisfying spatio-temporal constraints. Finally, on cloud servers, secure timespan verification is applied to ensure that the final results include all query keywords within the specified time span. A comprehensive analysis of the proposed scheme is provided in terms of computational complexity and security guarantees. Leveraging the keyword-first-pruning strategy and parallel processing on fog servers, the query scheme is evaluated to show stable and efficient performance through extensive experiments on real datasets. © 2024 IEEE.

关键词： Cloud platforms

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LIBRA: Harvesting Idle Resources Safely and Timely in Serverless Clusters 23

LIBRA: Harvesting Idle Resources Safely and Timely in Server...

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32nd international symposium on High-Performance parallel and distributed computing (HPDC) part of the ACM Federated computing Research Conference (FCRC)

作者： Yu, Hanfei Fontenot, Christian Wang, Hao Li, Jian Yuan, Xu Park, Seung-Jong Louisiana State Univ Baton Rouge LA 70803 USA SUNY Binghamton Binghamton NY USA Univ Louisiana Lafayette Lafayette LA 70504 USA

ISBN: (纸本)9798400701559

Serverless computing has been favored by users and infrastructure providers from various industries, including online services and scientific computing. Users enjoy its auto-scaling and ease-of-management, and providers own more control to optimize their service. However, existing serverless platforms still require users to pre-define resource allocations for their functions, leading to frequent misconfiguration by inexperienced users in practice. Besides, functions' varying input data further escalate the gap between their dynamic resource demands and static allocations, leaving functions either over-provisioned or under-provisioned. This paper presents Libra, a safe and timely resource harvesting framework for multi-node serverless clusters. Libra makes precise harvesting decisions to accelerate function invocations with harvested resources and jointly improve resource utilization by profiling dynamic resource demands and availability proactively. Experiments on OpenWhisk clusters with real-world workloads show that Libra reduces response latency by 39% and achieves 3x resource utilization compared to state-of-the-art solutions.

关键词： serverless computing resource harvesting

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Auto-HPCnet: An Automatic Framework to Build Neural Network-based Surrogate for High-Performance computing Applications 23

Auto-HPCnet: An Automatic Framework to Build Neural Network-...

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32nd international symposium on High-Performance parallel and distributed computing (HPDC) part of the ACM Federated computing Research Conference (FCRC)

作者： Dong, Wenqian Kestor, Gokcen Li, Dong Florida Int Univ Miami FL 33199 USA Univ Calif Merced Miami FL 95343 USA Pacific Northwest Natl Lab Richland WA 99352 USA

ISBN: (纸本)9798400701559

High-performance computing communities are increasingly adopting Neural Networks (NN) as surrogate models in their applications to generate scientific insights. Replacing an execution phase in the application with NN models can bring significant performance improvement. However, there is a lack of tools that can help domain scientists automatically apply NN-based surrogate models to HPC applications. We introduce a framework, named Auto-HPCnet, to democratize the usage of NN-based surrogates. Auto-HPCnet is the first end-to-end framework that makes past proposals for the NN-based surrogate model practical and disciplined. Auto-HPCnet introduces a workflow to address unique challenges when applying the approximation, such as feature acquisition and meeting the application-specific constraint on the quality of final computation outcome. We show that Auto-HPCnet can leverage NN for a set of HPC applications and achieve 5.50x speedup on average (up to 16.8x speedup and with data preparation cost included) while meeting the application-specific constraint on the final computation quality.

关键词： Scientific Machine Learning Neural Architecture Search Surrogate Model Construction Bayesian Optimization

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Towards the Acceleration of the Sparse Blossom Algorithm for Quantum Error Correction

Towards the Acceleration of the Sparse Blossom Algorithm for...

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1st international Conference on Smart Energy Systems and Artificial Intelligence (SESAI)

作者： Venere, Marco Guerrini, Valentino Branchini, Beatrice Conficconi, Davide Sciuto, Donatella Santambrogio, Marco D. Politecn Milan Dipartimento Elettron Informaz & Bioingn Milan Italy

ISBN: (纸本)9798350364613;9798350364606

Quantum computing is a new computing paradigm that exploits laws of quantum mechanics to achieve an exponential speedup compared to classical logic. However, noise strongly limits current quantum hardware, reducing achievable performance. Quantum Error Correction (QEC) techniques are a valuable approach to reduce the effects of noise. Nevertheless, the high computational complexity of QEC algorithms is incompatible with the tight time constraints of quantum devices. Thus, hardware acceleration is paramount to achieving real-time QEC. This work represents the first step in the FPGA acceleration of the Sparse Blossom Algorithm (SBA), a state-of-the-art decoding algorithm for QEC. We provide a performance profiling and a design methodology for the hardware development of the SBA. We evaluate the execution time, and energy efficiency of our solution, attaining up to 2.75x speedup and 9.59x improvement in energy efficiency compared to the software baseline.

关键词： Quantum Error Correction Quantum computing Reconfigurable Architectures

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parallel Optimization for Robotics: An Undergraduate Introduction to GPU parallel Programming and Numerical Optimization Research

Parallel Optimization for Robotics: An Undergraduate Introdu...

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1st international Conference on Smart Energy Systems and Artificial Intelligence (SESAI)

作者： Plancher, Brian Columbia Univ Barnard Coll New York NY 10027 USA

ISBN: (纸本)9798350364613;9798350364606

While parallel programming, particularly on graphics processing units (GPUs), and numerical optimization hold immense potential to tackle real-world computational challenges across disciplines, their inherent complexity and technical demands often act as daunting barriers to entry. This, unfortunately, limits accessibility and diversity within these crucial areas of computer science. To combat this challenge and ignite excitement among undergraduate learners, we developed an application-driven course, harnessing robotics as a lens to demystify the intricacies of these topics making them tangible and engaging. Our course's prerequisites are limited to the required undergraduate introductory core curriculum, opening doors for a wider range of students. Our course also features a large final-project component to connect theoretical learning to applied practice. In our first offering of the course we attracted 27 students without prior experience in these topics and found that an overwhelming majority of the students fell that they learned both technical and soft skills such that they felt prepared for future study in these fields.

关键词： CS Education GPU Programming Numerical Optimization parallel computing Robotics Undergraduate Education

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AQMFL: An Adaptive Quantization Framework for Multi-modal Federated Learning in Heterogeneous Edge Devices 22

AQMFL: An Adaptive Quantization Framework for Multi-modal Fe...

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22nd IEEE international symposium on parallel and distributed Processing with Applications, ISPA 2024

作者： Tang, Haoyong Zhang, Kaixuan Zhang, Jiuwu Xie, Xin Tong, Xinyu Liu, Xiulong Tianjin University College of Intelligence and Computing China China

ISBN: (纸本)9798331509712

With the wide application of multi-modal fusion sensing in scenarios such as autonomous driving and human-computer interaction, the privacy security and communication burden caused by massive data uploading need to be solved urgently. Federated Learning (FL) has received significant attention as a privacy-preserving distributed machine learning paradigm. Recent Multi-Modal Federated Learning (MMFL) focuses on addressing modal heterogeneity to enhance accuracy and speed up convergence. However, it overlooks the huge communication overhead in updating complex multi-modal network models, especially in edge environments with limited bandwidth. At the same time, the state-of-the-art communication-efficient FL methods are not customized to the MMFL characteristics. In this paper, we propose the Adaptive Quantization framework for Multi-modal Federated Learning (AQMFL). AQMFL implements decision-level multi-modal fusion locally by using parallel training and model ensemble, supporting its adaptation to modal heterogeneity and flexible deployment. AQMFL can adaptively allocate the number of quantization levels of gradient according to the modal contribution and the heterogeneous communication ability of nodes, which speeds up the system convergence and achieves a better balance between accuracy and communication efficiency. Compared with the classical baselines, AQMFL can reduce the total communication overhead by up to 50.47% and the total training time by up to 52.11% while maintaining the accuracy. © 2024 IEEE.

关键词： Federated learning

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DMSA: Decentralized and Multi-keyword Selective Data Sharing and Acquisition 22

DMSA: Decentralized and Multi-keyword Selective Data Sharing...

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22nd IEEE international symposium on parallel and distributed Processing with Applications, ISPA 2024

作者： Lin, Moheng Shi, Peichang Fu, Xiang Jiang, Feng Yi, Guodong National University of Defense Technology National Key Laboratory of Parallel and Distributed Computing College of Computer Science Changsha410073 China Xiangjiang Lab Changsha410073 China

ISBN: (纸本)9798331509712

Blockchain technology has been extensively uti-lized in decentralized data-sharing applications, with the immutability of blockchain providing a witness for the circulation of data. However, current blockchain data-sharing solutions still fail to address the simultaneous screening needs of both the sender and receiver with multi-keywords. Without the capability to support bilateral simultaneous filtering, the disclosure of reasons for matching failures could inadvertently expose sensitive user data. Therefore, the challenge lies in enabling ciphertexts with multiple keywords and receivers with multiple interests to achieve mutual and simultaneous matching. Based on the technical foundations of SE (Searchable Encryption), MABE (Multi-Attribute Based Encryption), and polynomial fitting, this paper proposes a scheme called DMSA (Decentralized and Multi-keyword selective Sharing and selective Acquisition). This scheme can satisfy soundness, enabling ciphertexts carrying multiple keywords and receivers representing multiple interests to match each other simultaneously. We conducted a security analysis that confirms the security of DMSA against chosen-plaintext attacks. Our experimental results demonstrate a significant efficiency improvement, with a 67% increase over single-keyword data-sharing schemes and a 16% enhancement compared to the existing multi-keyword data-sharing solution. © 2024 IEEE.

关键词： Ciphertext

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