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An algorithm for threading assignment in large-scale wireless network mobile simulations 25

An algorithm for threading assignment in large-scale wireles...

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25th IEEE/ACM international symposium on distributed Simulation and Real Time Applications (DS-RT)

作者： Manzanilla-Salazar, Orestes Mellah, Hakim Malandra, Filippo Sanso, Brunilde Polytech Montreal Dept Elect Engn Montreal PQ Canada Univ Buffalo Dept Elect Engn Buffalo NY USA

ISBN: (纸本)9781665433266

When using parallel computing to run large-scale simulations, the parts of the system being simulated in different cores or threads often interact and exchange information, constraining the threads to be synchronized. Simulating wireless networks with mobility, when a user equipment (UE) ceases to be served by one Base Station (BS), to be served by a new one, a synchronization point may be required, if the new BS is being simulated in another thread. In a large-scale distributed wireless network with high mobility, the simulation speed-up obtained from multi-threading could be lost to the overhead burden for synchronizing the threads. We propose a heuristic approach to assign BSs to threads in such a way as to minimize the number of synchronization points. In a time interval of the simulation, accumulated interactions are interpreted as growing graphs. Advancing through the simulation time until the number of disconnected graphs is equal to the number of desired threads, showed to be a good strategy to determine the longest intervals that can be simulated without synchronization points while taking advantage of multi-threading. By means of simulation tests we show decrements of up to 100.0 %, in the number of synchronization points, in comparison to those required for the same simulation times when assigning BSs to threads in a random and balanced way.

关键词： Wireless networks mobility heuristics simulation co-simulation parallel computing large scale handovers

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A Task Admission Scheduling Strategy Based on Adaptive Time Slicing in Mobile Edge computing

A Task Admission Scheduling Strategy Based on Adaptive Time ...

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international symposium on parallel and distributed Processing with Applications, ISPA

作者： Yanpu Tang Zhuofan Liao Xiaoyong Tang School of Computer and Communication Engineering Changsha University of Science and Technology Changsha China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

The rise of Multi-access Edge computing (MEC) speeds up mobile user services and resolves service delays caused by long-distance transmission to cloud servers. However, in task-intensive scenarios, edge server processing limitations lead to buffer congestion, increasing latency and reducing Quality of Service (QoS). Furthermore, the challenges of edge server task processing are increased by the varying deadline requirements of different tasks, the time variability of task arrivals, and the real-time fluctuations of the network. In this paper, we propose an Adaptive Time Slice Admission Scheduling (ATSAS) strategy to solve these problems. Specifically, ATSAS proposes an Adaptive Time Slice Algorithm (ATSA) and a task Admission Scheduling Algorithm (ASA). ATSA dynamically adjusts the time slice based on real-time network conditions and task flow characteristics. ASA, assisted by ATSA, dynamically adjusts task priorities based on the remaining data amount, deadline requirements, real-time network conditions, and the location of the task buffer. After that, ASA makes different scheduling strategies for tasks. Through the coordination of ATSA and ASA, the proposed strategy ensures the high efficiency and fairness of processing tasks in different realistic scenarios. The simulation results show that, compared with the existing O2A and OTDS in different scenarios, the proposed ATSAS reduces the average number of waiting services in the edge server buffer by 38.63% and 35.34%, and accelerates the average response speed of tasks by about 39.76% and 32.41%.

关键词： distributed processing Multi-access edge computing Fluctuations Scheduling algorithms Simulation Heuristic algorithms Quality of service Dynamic scheduling Real-time systems Servers

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Funnel: An Efficient Sparse Attention Accelerator with Multi-Dataflow Fusion

Funnel: An Efficient Sparse Attention Accelerator with Multi...

引用

international symposium on parallel and distributed Processing with Applications, ISPA

作者： Shenghong Ma Jinwei Xu Jingfei Jiang Yaohua Wang Dongsheng Li National Key Laboratory of Parallel and Distributed Computing College of Computer National University of Defense Technology Changsha China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

The self-attention mechanism is the core component of Transformer, which provides a powerful ability to understand the sequence context. However, the self-attention mechanism also suffers from a large amount of redundant computation. Model sparsification can effectively reduce computational load, but the irregularity of non-zeros introduced by sparsification significantly decreases hardware efficiency. This paper proposes Funnel, an accelerator that dynamically predicts sparse attention patterns and efficiently processes unstructured sparse data. Firstly, we adopt a fast quantization method based on lookup table to minimize the cost of sparse patterns prediction. Secondly, we propose Funnel computing Unit (FCU), a hardware architecture that efficiently handles sparse attention through multi-dataflow fusion. Sampled Dense-Dense Matrix Multiplication (SDDMM) and Sparse-Dense Matrix Multiplication (SpMM) are core components of sparse attention mechanism. FCU unifies the computation ways of matrix inner product and row-wise product to support SDDMM and SpMM at the same time, which greatly reduces the storage and movement overhead of intermediate results. Lastly, we devise a lightweight buffer and data tiling strategy tailored to the proposed accelerator, aimed at enhancing data reuse. Experiments demonstrate that our accelerator achieves 0.10-0.25 sparsity with small accuracy loss. When computing the self-attention layer, it attains hardware efficiency ranging from 60% to 85%. Compared to CPU and GPU, it achieves 5.60x and 8.20x speedup. Compared to the state-of-the-art attention accelerators A 3 , SpAtten, FTRANS, and Sanger, it achieves 7.37x, 4.52x, 9.58x, and 3.08x speedup.

关键词： Quantization (signal) Graphics processing units Computer architecture Transformer cores Transformers Prediction algorithms Hardware Spatiotemporal phenomena Sparse matrices Sorting

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Having Energy Depletion in Mind to Make Service Fairness Practical

Having Energy Depletion in Mind to Make Service Fairness Pra...

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international symposium on parallel and distributed Processing with Applications, ISPA

作者： Tianwen Peng Long Chen Jiale Huang Jigang Wu School of Computer Science and Technology Guangdong University of Technology Guangzhou China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

The energy consumption of edge devices or nodes is critical to ensure a long lifetime of cooperative edge-AI service network, which has been somehow overlooked in the literature. Failure to accommodating the energy depletion can not only bring quality of service degradation of mobile terminal devices, but can also harm the connectivity of the multi-access edge computing network. This paper thus addresses the application service fairness problem under energy depletion constraints, to make the service fairness paradigm practical to suit for energy-limited edge nodes, e.g., the unmanned areal vehicles (UAVs), solar energy powered road side units (RSUs). The problem is formulated as a non-convex integer linear programming, which is NP-hard. Then a randomized rounding algorithm as well as a greedy algorithm are designed to maximize the minimum service type’s completion rate. Extensive simulation results have shown that compared to the algorithms without energy constraints, the proposed randomized rounding algorithm and greedy algorithm with energy constraints can reduce the average energy consumption by about 39.87% and 40.31% respectively, at the cost of a mild average system throughput degradation.

关键词： Greedy algorithms Degradation Energy consumption Multi-access edge computing Simulation Road side unit Solar energy Quality of service Integer linear programming Throughput

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A-MADDPG Based Partial Offloading in Digital Twin Edge Network (DITEN) Empowered IIoT

A-MADDPG Based Partial Offloading in Digital Twin Edge Netwo...

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international symposium on parallel and distributed Processing with Applications, ISPA

作者： Lingling Liao Ming Tao Renping Xie Chengxian Zhuang School of Computer Science and Technology Dongguan University of Technology Dongguan China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

As a typical deterministic network, the Industrial Internet of Things (IIoT) has strict requirements on the task processing delay. IIoT leverages edge computing to deliver low-latency services efficiently. However, this requires mobile Internet of Things (IoT) devices to execute reasonable task offloading decisions. To minimize disruption in IIoT operations and facilitate cost-effective execution, our approach capitalizes on the synergy of Digital Twin (DT) for enhanced virtual-real coordination in decision-making. We introduce an innovative solution for mobile inspection tasks within a DITEN-enhanced IIoT framework. This solution, grounded in A-MADDPG based partial offloading, not only addresses the challenges posed by IIoT but also transforms the problem into an optimization problem with constraints. By conceptualizing it as a Markov Decision Process (MDP), we integrate a carefully designed attention mechanism into the MADDPG algorithm, named as Attention enhanced Multi-Agent Deep Deterministic Policy Gradient(A-MADDPG), forging a robust and effective solution. Experimental results underscore the superiority of our proposed method, demonstrating remarkable convergence and outperforming existing baselines in reducing task processing latency.

关键词： Service robots Robot kinematics Transforms Inspection Digital twins Delays Optimization Industrial Internet of Things Edge computing Convergence

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Harnessing Deep Learning and HPC Kernels via High-Level Loop and Tensor Abstractions on CPU Architectures

Harnessing Deep Learning and HPC Kernels via High-Level Loop...

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international symposium on parallel and distributed Processing (IPDPS)

作者： Evangelos Georganas Dhiraj Kalamkar Kirill Voronin Abhisek Kundu Antonio Noack Hans Pabst Alexander Breuer Alexander Heinecke Intel Corporation NVIDIA Corporation Friedrich Schiller Universität Jena

ISBN: (数字)9798350387117

ISBN: (纸本)9798350387124

During the past decade, Deep Learning (DL) algorithms, programming systems and hardware have converged with the High Performance computing (HPC) counterparts. Nevertheless, the programming methodology of DL and HPC systems is stagnant, relying on highly-optimized, yet platform-specific and inflexible vendor-optimized libraries. Such libraries provide close-to-peak performance on specific platforms, kernels and shapes thereof that vendors have dedicated optimizations efforts, while they underperform in the remaining use-cases, yielding non-portable codes with performance glass-jaws. This work introduces a framework to develop efficient, portable DL and HPC kernels for modern CPU architectures. We decompose the kernel development in two steps: 1) Expressing the computational core using Tensor Processing Primitives (TPPs): a compact, versatile set of 2D-tensor operators, 2) Expressing the logical loops around TPPs in a high-level, declarative fashion whereas the exact instantiation (ordering, tiling, parallelization) is determined via simple knobs. We demonstrate the efficacy of our approach using standalone kernels and end-to-end workloads that outperform state-of-the-art implementations on diverse CPU platforms.

关键词： Deep learning Tensors Shape High performance computing Computer architecture Programming Libraries

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GraphMetaP: Efficient MetaPath Generation for Dynamic Heterogeneous Graph Models

GraphMetaP: Efficient MetaPath Generation for Dynamic Hetero...

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international symposium on parallel and distributed Processing (IPDPS)

作者： Haiheng He Dan Chen Long Zheng Yu Huang Haifeng Liu Chaoqiang Liu Xiaofei Liao Hai Jin National Engineering Research Center for Big Data Technology and System/Services Computing Technology and System Lab/Clusters and Grid Computing Lab Huazhong University of Science and Technology China Zhejiang Lab Hangzhou China

Metapath-based heterogeneous graph models (MHGM) show excellent performance in learning semantic and structural information in heterogeneous graphs. Metapath matching is an essential processing step in MHGM to find all metapath instances, bringing significant overhead compared to the total model execution time. Even worse, in dynamic heterogeneous graphs, metapath instances require to be rematched while graph updated. In this paper, we observe that only a small fraction of metapath instances change and propose GraphMetaP, an efficient incremental metapath maintenance method in order to eliminate the matching overhead in dynamic heterogeneous graphs. GraphMetaP introduces a novel format for metapath instances to capture the dependencies among the metapath instances. The format incrementally maintains metapath instances based on the graph updates to avoide the rematching metapath overhead for the updated graph. Furthermore, GraphMetaP uses the fold way to simplify the format in order to recover all metapath instances faster. Experiments show that GraphMetaP enables efficient maintenance of metapath instances on dynamic heterogeneous graphs and outperforms 172.4X on average compared to the matching metapath method.

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HSDP: Accelerating Large-scale Model Training via Efficient Sharded Data parallelism

HSDP: Accelerating Large-scale Model Training via Efficient ...

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international symposium on parallel and distributed Processing with Applications, ISPA

作者： Yanqi Hao Zhiquan Lai Wei Wang Shengwei Li Weijie Liu Keshi Ge Dongsheng Li National Key Laboratory of Parallel and Distributed Computing(PDL) College of Computer National University of Defense Technology Changsha China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Large deep neural network (DNN) models have demonstrated exceptional performance across diverse downstream tasks. Sharded data parallelism (SDP) has been widely used to reduce the memory footprint of model states. In a DNN training cluster, a device usually has multiple inter-device links that connect to other devices, like NVLink and InfiniBand. However, existing SDP approaches employ a single link at any given time, encountering challenges in efficient training due to significant communication overheads. We observe that the inter-device links can work independently without affecting each other. To reduce the fatal communication overhead of distributed training of large DNNs, this paper introduces HSDP, an efficient SDP training approach that enables the simultaneous utilization of multiple inter-device links. HSDP partitions models in a novel fine-grained manner and orchestrates the communication processes of partitioned parameters while considering inter-device links. This design enables concurrent communication execution and reduces communication overhead. To further optimize the training performance of HSDP, we propose a HSDP planner. The HSDP planner first abstracts the model partition and execution of HSDP into a communication parallel strategy, and builds a cost model to estimate the performance of each strategy. We then formulate the strategy searching as an optimization problem and solve it with an off-the-shelf solver. Evaluations on representative DNN workloads demonstrate that HSDP achieves up to 1.30× speedup compared to the state-of-the-art SDP training approaches.

关键词： Training Performance evaluation Costs distributed databases Artificial neural networks parallel processing Throughput Search problems Data models Optimization

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Adaptive Task Offloading in High-Speed Vehicular Networks Using Enhanced D3QN Algorithm

Adaptive Task Offloading in High-Speed Vehicular Networks Us...

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international symposium on parallel and distributed Processing with Applications, ISPA

作者： Panyang Zhou Xiaofei Xing Xiao Wang Tian Wang Guojun Wang School of Computer Science and Cyber Engineering Guangzhou University Guangzhou China Institute of Artificial Intelligence and Future Networks Beijing Normal University Zhuhai China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

With the development of Vehicle Edge computing (VEC), vehicles can offload tasks to Road Side Units (RSU) for processing. However, optimizing latency is a challenging problem in high-speed environments. This paper investigates the latency optimization problem for task offloading in high-speed environments, where multiple vehicles on the user end can choose to offload their tasks to either RSUs or Base Station (BS) for computation. Meanwhile, based on the vehicle’s location and speed, we predict the time a vehicle spends within the communication range of an RSU to select a more suitable edge node. Then, an improved Group Priority Sampling-Double Dueling Deep Q Network (GPS-D3QN) algorithm is used to optimize the proposed task offloading, resulting in an optimal offloading strategy that reduces latency. Through simulation, by varying the number of vehicles and RSUs, the proposed optimization scheme is verified to have an advantage in terms of delay compared to other schemes.

关键词： Base stations Adaptive systems Road side unit Computational modeling Collaboration Prediction algorithms Delays Optimization Edge computing Convergence

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Characterizing Job-Task Dependency in Cloud Workloads Using Graph Learning

Characterizing Job-Task Dependency in Cloud Workloads Using ...

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

作者： Gu, Zhaochen Tang, Sihai Jiang, Beilei Huang, Song Guan, Qiang Fu, Song Univ North Texas Comp Sci & Engg Dept Denton TX 76203 USA Allstate Big Data Dev Irving TX USA Kent State Univ Dept Comp Sci Kent OH 44242 USA

ISBN: (纸本)9781665435772

Modeling and scheduling diverse and dynamic workloads effectively has become a crucial issue due to the ever increasing scale and complexity of systems and applications in modern data centers. A large-scale cloud system consists of a large number of computing nodes, storage nodes and networking devices, running diverse workloads. Existing works analyzed execution traces in terms of resource usage by applying statistical methods. Cloud workloads, especially batch jobs, are composed of tens to thousands of tasks with complex dependency which can be represented by directed acyclic graphs (DAGs). Those workloads and their dependencies have not been thoroughly studied. Understanding the characteristics of batch cloud workload helps us foresee resource demands and execution time of new jobs and make better decisions in job scheduling. In this paper, we investigate batch jobs in production cloud computing environments with dependencies from the perspective of topological characteristics and structural patterns. We design a graph learning approach for job classification based on jobs' topological similarity. We evaluate our methods using traces collected from a production data center and discover insightful properties and patterns of batch jobs in real-world scenarios. To the best of our knowledge, this is the first such work that leverages graph learning to explore the topological structures for cloud workflow for characterization and analysis.

关键词： Cloud computing Workload characterization Graph learning Job dependency Classification

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