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检索条件"任意字段=International Conference on Advances in Parallel and Distributed Computing"

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24th international conference on Algorithms and Architectures for parallel Processing, ICA3PP 2024

24th International Conference on Algorithms and Architecture...

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24th international conference on Algorithms and Architectures for parallel Processing, ICA3PP 2024

ISBN: (纸本)9789819615506

The proceedings contain 131 papers. The special focus in this conference is on Algorithms and Architectures for parallel Processing. The topics include: MARO: Enabling Full MPI Automatic Refactoring in DSL-Based Programming Framework;SSC: An SRAM-Based Silence computing Design for On-chip Memory;TP-BFT: A Faster Asynchronous BFT Consensus with parallel Structure;LTP: A Lightweight On-Chip Temporary Prefetcher for Data-Dependent Memory Accesses;A Neural Network-Based PUF Protection Method Against Machine Learning Attack;Compression Format and Systolic Array Structure Co-design for Accelerating Sparse Matrix Multiplication in DNNs;multidimensional Intrinsic Identity Construction and Dynamic Seamless Authentication Schemes in IoT Environments;invisible Backdoor Attack with Image Contours Triggers;finestra: Multi-aggregator Swarm Learning for Gradient Leakage Defense;DIsFU: Protecting Innocent Clients in Federated Unlearning;multiple-Round Aggregation of Abstract Semantics for Secure Heterogeneous Federated Learning;dynamic Privacy Protection with Large Language Model in Social Networks;a Dynamic Symmetric Searchable Encryption Scheme for Rapid Conjunctive Queries;a Data Watermark Scheme Base on Data Converted Bitmap for Data Trading;distributed Incentive Algorithm for Fine-Grained Offloading in Vehicular Ad Hoc Networks;mitigating Over-Unlearning in Machine Unlearning with Synthetic Data Augmentation;AW-YOLOv9: Adverse Weather Conditions Adaptation for UAV Detection;efficient and Privacy-Preserving Ranking-Based Federated Learning;on-Chain Dynamic Policy Evaluation for Decentralized Access Control;DPG-FairFL: A Dual-Phase GAN-Based Defense Framework Against Image-Based Fairness Data Poisoning Attacks in Federated Learning.

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24th international conference on Algorithms and Architectures for parallel Processing, ICA3PP 2024

24th International Conference on Algorithms and Architecture...

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24th international conference on Algorithms and Architectures for parallel Processing, ICA3PP 2024

ISBN: (纸本)9789819615445

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Fast Approximation for Scheduling Malleable Jobs on parallel Batch Machines with Rejection 25th

Fast Approximation for Scheduling Malleable Jobs on Parall...

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25th international conference on parallel and distributed computing, Applications and Technologies, PDCAT 2024

作者： Xia, Fenghe Guo, Longkun Zhang, Xiaoyan School of Mathematics and Statistics Fuzhou University Fuzhou350116 China School of Mathematical Science Institute of Mathematics and Ministry of Education Key Laboratory of NSLSCS Nanjing Normal University Nanjing210023 China

ISBN: (纸本)9789819642069

The rapid growth of cloud computing has brought new challenges in parallel Batch Machine Scheduling (PBMS), particularly when incorporating malleability and rejection constraints. This has led to the parallel Batch Machine Scheduling with Malleability and Rejection problem (PBMSMR), which involves malleable jobs whose widths can be adjusted during execution within specified limits and incorporates job rejection subject to a penalty threshold. Based on an analysis of key properties of batch scheduling with job rejection, we develop an approximation algorithm for PBMSMR by employing a greedy approach that reorders and iteratively refines job sets to minimize the objective while respecting rejection constraints. The algorithm achieves a time complexity of O(n2logn) and an approximation ratio of (4-2Km), where K and m denote the capacities and the number of the machines, respectively. For jobs with identical release times, we fine-tune the algorithm to achieve an approximation ratio of (3+2(K-1)Km). Additionally, for PBMS with two machines of non-identical capacities and fixed-width jobs, we achieve a ratio of 175 with O(nlogn), improving upon the previous state-of-the-art approximation ratio of 5 with a runtime of O(n2). © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Job shop scheduling

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SpecInF: Exploiting Idle GPU Resources in distributed DL Training via Speculative Inference Filling 20th

SpecInF: Exploiting Idle GPU Resources in Distributed DL Tr...

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20th IFIP WG 10.3 international conference on Network and parallel computing, NPC 2024

作者： Lv, Cunchi Shi, Xiao Liang, Dong Tan, Wenting Zhao, Xiaofang Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Nanjing China Zhongguancun Laboratory Haidian China Nanjing Institute of InforSuperbahn Nanjing China

ISBN: (纸本)9789819628292

Deep Learning (DL), especially with Large Language Models (LLMs), brings benefits to various areas. However, DL training systems usually yield prominent idling GPU resources due to many factors, such as resource allocation and collective communication. To improve GPU utilization, we present SpecInF, which adopts a Speculative Inference Filling method to exploit idle GPU resources. It collocates each primary training instance with additional inference instances on the same GPU, detects the training bubbles and adaptively fills with online or offline inference workloads. Our results show that SpecInF can effectively enhance GPU utilization under mainstream parallel training modes, delivering additional up to 14× offline inference throughputs than TGS and 67% reduction in online inference p95 latency than MPS, while guaranteeing collocated training throughput. © IFIP international Federation for Information Processing 2025.

关键词： Resource allocation

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Low-Carbon Geographically distributed Cloud-Edge Task Scheduling 24th

Low-Carbon Geographically Distributed Cloud-Edge Task Schedu...

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24th international conference on Algorithms and Architectures for parallel Processing, ICA3PP 2024

作者： Zhu, Yingjie Qi, Ji Wang, Zehao Wei, Shengjie Chen, Yan Cao, Tuo Luo, Gangyi Qian, Zhuzhong State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Software Technology Co. Ltd. Jiangsu Suzhou China

ISBN: (纸本)9789819615506

Edge computing is a rapidly developing research area known for its ability to reduce latency and improve energy efficiency, and it also has a potential for green computing. Many geographically distributed edge servers are powered by renewable energy sources, due to the difficulties of using traditional power supplies or because of advancements in energy harvesting technologies. These green edge servers can cut down carbon emissions by processing tasks locally, but the inherent limitations of their computing capacity result in some tasks having to be uploaded to a data center to meet service-level agreement (SLA) requirements. To further reduce carbon emissions in cloud-edge systems, scheduling tasks to those low-carbon data centers while meeting latency constraints is highly beneficial. In this paper, we propose a low-carbon cloud-edge scheduling algorithm that utilizes Lyapunov optimization techniques and Markov approximation to address the long-term optimization problem of carbon emissions. Our algorithm guarantees provable performance, and simulation results demonstrate its effectiveness in striking a balance between carbon emissions and task latency. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Carbon sequestration

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HPC and GPU solutions for radio interferometry using RICK 33

HPC and GPU solutions for radio interferometry using RICK

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33rd Euromicro international conference on parallel, distributed, and Network-Based Processing, PDP 2025

作者： De Rubeis, Emanuele Gheller, Claudio Lacopo, Giovanni Tornatore, Luca Elahi, Pascal Jahan Cytowski, Maciej Taffoni, Giuliano Varetto, Ugo Università di Bologna Istituto di Radioastronomia Dipartimento di Fisica e Astronomia INAF via Gobetti 93/2 BolognaI-40129 Italy Istituto di Radioastronomia INAF Via P. Gobetti 101 Bologna40129 Italy Università degli studi di Trieste INAF Astronomical Observatory of Trieste Trieste Italy INAF Astronomical Observatory of Trieste via GB Tiepolo 11 Trieste34143 Italy Pawsey Supercomputing Centre 1 Bryce Avenue KensingtonWA6151 Australia

ISBN: (纸本)9798331524937

Since the last decade, radio astronomy has started a new era: the advent of the Square Kilometer Array (SKA), preceded by its pathfinders, will produce a huge amount of data that will be hard to process with a traditional approach. This means that the current state-of-the-art software for data reduction and imaging will have to be re-modeled to face such data challenge. In order to manage such an increase in data size and computational requirements, scientists need to exploit modern high-performance computing (HPC) architectures. In particular, heterogeneous systems, based on complex combinations of CPUs, accelerators, high-speed networks and composite storage devices need to be used in an efficient and effective way. In this paper, we present an overview on Radio Imaging Code Kernels (RICK;[1];[2];[3]), a code able to perform the most computationally demanding steps of w-stacking gridder algorithm exploiting distributed parallelism and GPU acceleration. GPU offloading is possible through CUDA, HIP, and OpenMP, aiming at the largest possible usability among multiple architectures. After detailing the (multi-)GPU approach to the problem and listing all the new code implementations, we analyze its performances considering both the computational and communication workload. We will show how the full, distributed GPU offload of the code, first of its kind and crucial to deal with increasingly large interferometric data, represents not only an extremely fast and optimized approach, but also the greenest one if compared to its parallel CPU counterpart. This code, now publicly available, has been tested with a wide variety of modern interferometers and SKA pathfinders. This represents, to date, the first example of radio imaging software fully enabled to GPUs, becoming a potential state-of-the-art approach for the upcoming SKA. Finally, we will also present the future perspectives about the code, planned to be converted into a library and possibly be used by any of the most

关键词： parallel architectures

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Advancing Evasion: distributed Backdoor Attacks in Federated Learning 25th

Advancing Evasion: Distributed Backdoor Attacks in Federate...

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25th international conference on parallel and distributed computing, Applications and Technologies, PDCAT 2024

作者： Wang, Jian Shen, Hong Ke, Wei Faculty of Applied Sciences Macao Polytechnic University China School of Engineering and Technology Central Queensland University Rockhampton Australia School of Software Technology Guangzhou Institute of Software Guangzhou China

ISBN: (纸本)9789819642069

Federated Learning (FL) is vulnerable to backdoor attacks through data poisoning if the data is not scrutinized, as malicious participants can inject backdoor triggers in normal samples, leading to poisoned updates. distributed backdoor attacks pose a greater threat than centralized ones, as they often use fixed pixel blocks as triggers, increasing the risk of detection. This paper presents a novel distributed backdoor attack strategy that leverages edge structure poisoning to circumvent existing defense mechanisms, employing a distributed poisoning strategy to evade current defense mechanisms, thereby enhancing the stealth of the attack. Experimental results on multiple benchmark datasets demonstrate that this method is more effective and stealthy compared to other backdoor attack methods. Furthermore, this paper also proposes targeted defense strategies based on the experimental results, offering a new perspective on the security of FL systems. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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Comprehensive Deadlock Prevention for GPU Collective Communication 25

Comprehensive Deadlock Prevention for GPU Collective Communi...

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20th European conference on Computer Systems, EuroSys 2025, co-located 30th ACM international conference on Architectural Support for Programming Languages and Operating Systems, ASPLOS 2025

作者： Pan, Lichen Liu, Juncheng Fu, Yongquan Yuan, Jinhui Zhang, Rongkai Li, Pengze Xiao, Zhen School of Computer Science Peking University China OneFlow Research China National Key Laboratory of Parallel and Distributed Computing College of Computer Science and Technology National University of Defense Technology China

ISBN: (纸本)9798400711961

distributed deep neural network training necessitates efficient GPU collective communications, which are inherently susceptible to deadlocks. GPU collective deadlocks arise easily in distributed deep learning applications when multiple collectives circularly wait for each other. GPU collective deadlocks pose a significant challenge to the correct functioning and efficiency of distributed deep learning, and no general effective solutions are currently available. Only in specific scenarios, ad-hoc methods, making an application invoke collectives in a consistent order across GPUs, can be used to prevent circular collective dependency and deadlocks. This paper presents DFCCL, a novel GPU collective communication library that provides a comprehensive approach for GPU collective deadlock prevention while maintaining high performance. DFCCL achieves preemption for GPU collectives at the bottom library level, effectively preventing deadlocks even if applications cause circular collective dependency. DFCCL ensures high performance with its execution and scheduling methods for collectives. Experiments show that DFCCL effectively prevents GPU collective deadlocks in various situations. Moreover, extensive evaluations demonstrate that DFCCL delivers performance comparable to or superior to NCCL, the state-of-the-art collective communication library highly optimized for NVIDIA GPUs. © 2025 Copyright held by the owner/author(s).

关键词： Deep neural networks

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Efficient Multi-Bounce Ray Tracing for Specular and Transparent Materials in NeRF 7

Efficient Multi-Bounce Ray Tracing for Specular and Transpar...

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7th IEEE international conference on Artificial Intelligence and eXtended and Virtual Reality, AIxVR 2025

作者： Liu, Haozhe Tan, Yu Wei Bhojan, Anand School of Computing National University of Singapore Singapore

ISBN: (纸本)9798331521578

With advancements in 3D reconstruction and computer graphics, Neural Radiance Fields (NeRF) has emerged as a powerful technique in novel view synthesis, holding potential for immersive extended reality (XR) and gaming applications. However, NeRF relies on volume rendering which cannot handle view-dependent light interactions like refraction and reflection well, which are crucial for realistic rendering in these areas. NeRF's hash encoding method also has high memory demands, limiting its utility for real-world scenes. We introduce a two-phase approach that improves NeRF's applicability for 3D environments. First, using object masks, we employ FlexiCubes to reconstruct specular and transparent objects with fewer polygons and model a neural field to capture refraction and reflection effects. Second, we propose a new frequency encoding filter that enhances visual quality while using just 10% of the memory required by hash encoding, enabling a multi-bounce ray-tracing module for reflection and refraction. Our method advances the integration of NeRF into realistic and memory-efficient frameworks suitable for XR and gaming experiences. © 2025 IEEE.

关键词： Encoding (symbols)

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AsymFB: Accelerating LLM Training Through Asymmetric Model parallelism 20th

AsymFB: Accelerating LLM Training Through Asymmetric Model P...

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20th IFIP WG 10.3 international conference on Network and parallel computing, NPC 2024

作者： Zhang, Jiawei Shao, En Wang, Leping Tan, Guangming Sun, Ninghui Institution of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9789819628292

Transformer-based large language models (LLMs) have become a dominant force in natural language processing, advancing both research and industry. As model sizes have grown from billions to hundreds of billions of parameters, training them on a single GPU is no longer feasible, making distributed training essential. Existing distributed training methods partition models into non-overlapping segments of equal-size to cut memory usage on each device, but this creates cross-device data dependencies. These dependencies lead to delays and reduced throughput, particularly during the forward phase. This paper introduces AsymFB, a novel model parallelism algorithm designed to address these limitations. By adopting an asymmetric partitioning approach, AsymFB assigns varying numbers of devices to different training phases—forward, recompute, and backward—optimizing resource allocation and minimizing performance penalties. Our approach demonstrates up to a 1.25× speedup compared to traditional methods, significantly improving the efficiency of training large-scale LLMs and mitigating common performance bottlenecks. © IFIP international Federation for Information Processing 2025.

关键词： Resource allocation

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