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

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FSD-Inference: Fully Serverless distributed Inference with Scalable Cloud Communication 40

FSD-Inference: Fully Serverless Distributed Inference with S...

引用

40th IEEE international conference on Data Engineering, ICDE 2024

作者： Oakley, Joe Ferhatosmanoglu, Hakan University of Warwick Department of Computer Science United Kingdom

ISBN: (纸本)9798350317152

Serverless computing offers attractive scalability, elasticity and cost-effectiveness. However, constraints on memory, CPU and function runtime have hindered its adoption for dataintensive applications and machine learning (ML) workloads. Traditional 'server-ful' platforms enable distributed computation via fast networks and well-established inter-process communication (IPC) mechanisms such as MPI and shared memory. In the absence of such solutions in the serverless domain, parallel computation with significant IPC requirements is challenging. We present FSD-Inference, the first fully serverless and highly scalable system for distributed ML inference. We explore poten-tial communication channels, in conjunction with Function-as-a-Service (FaaS) compute, to design a state-of-the-art solution for distributed ML within the context of serverless data-intensive computing. We introduce novel fully serverless communication schemes for ML inference workloads, leveraging both cloud-based publish-subscribe/queueing and object storage offerings. We demonstrate how publish-subscribe/queueing services can be adapted for FaaS IPC with comparable performance to object storage, while offering significantly reduced cost at high parallelism levels. We conduct in-depth experiments on benchmark DNNs of various sizes. The results show that when compared to server-based alternatives, FSD-Inference is significantly more cost-effective and scalable, and can even achieve competitive performance against optimized HPC solutions. Experiments also confirm that our serverless solution can handle large distributed workloads and leverage high degrees of FaaS parallelism. © 2024 IEEE.

关键词： Deep neural networks

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Research on Power Supply Enterprise Safety Precontrol System and Dynamic Quantitative Evaluation Method 3

Research on Power Supply Enterprise Safety Precontrol System...

引用

3rd IEEE international conference on distributed computing and Electrical Circuits and Electronics, ICDCECE 2024

作者： Zhang, Zhonghao Ye, Ronghui Liao, Binqiang Li, Chen Xu, Min Dongguan Power Supply Bureau Of Guangdong Power Grid Dongguan China Electric Power Research Institute Csg Guangzhou China

ISBN: (纸本)9798350318609

Based on a comprehensive analysis of different work and management indicators, as well as the quantitative assessment outcomes of the implemented personal safety responsibility system, this study proposes the utilization of computer intelligent warning technology within the power supply enterprise responsibility system. This technology enables the automatic analysis of the entire operational process of the power supply enterprise responsibility system, facilitating the examination of the responsibility system at various hierarchical levels within the organization. This paper aims to propose the establishment of evaluation indicators derived from the components of power supply enterprise safety responsibility system construction, and subsequently develop a multi-level responsibility evaluation index system. The dynamic evaluation method is employed for the computation of the evaluation value and the establishment of the pre-control algorithm. The evaluation data of the full staff safety responsibility system is monitored in real-time, and any abnormal detection is promptly addressed. Upon the detection of abnormalities, the determination of warning execution conditions and warning levels is conducted by considering the warning indicators associated with the abnormal data. Based on this criterion, a determination is made regarding the issuance of a warning. When a warning is issued, it triggers the generation of corresponding warning information based on the warning level. This real-time warning information is then stored in the database. © 2024 IEEE.

关键词： Electric power system control

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The Impact of Importance-Aware Dataset Partitioning on Data-parallel Training of Deep Neural Networks 23rd

The Impact of Importance-Aware Dataset Partitioning on Data-...

引用

23rd IFIP WG 6.1 international conference on distributed Applications and Interoperable Systems (DAIS) Held as Part of the 18th international Federated conference on distributed computing Techniques (DisCoTec)

作者： Sheikholeslami, Sina Payberah, Amir H. Wang, Tianze Dowling, Jim Vlassov, Vladimir KTH Royal Inst Technol Stockholm Sweden Hopsworks AB Stockholm Sweden

ISBN: (纸本)9783031352591;9783031352607

Deep neural networks used for computer vision tasks are typically trained on datasets consisting of thousands of images, called examples. Recent studies have shown that examples in a dataset are not of equal importance for model training and can be categorized based on quantifiable measures reflecting a notion of "hardness" or "importance". In this work, we conduct an empirical study of the impact of importance-aware partitioning of the dataset examples across workers on the performance of data-parallel training of deep neural networks. Our experiments with CIFAR-10 and CIFAR-100 image datasets show that data-parallel training with importance-aware partitioning can perform better than vanilla data-parallel training, which is oblivious to the importance of examples. More specifically, the proper choice of the importance measure, partitioning heuristic, and the number of intervals for dataset repartitioning can improve the best accuracy of the model trained for a fixed number of epochs. We conclude that the parameters related to importance-aware data-parallel training, including the importance measure, number of warmup training epochs, and others defined in the paper, may be considered as hyperparameters of data-parallel model training.

关键词： Data-parallel training Example importance distributed deep learning

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JASS: A Tunable Checkpointing System for NVM-based Systems 30

JASS: A Tunable Checkpointing System for NVM-based Systems

引用

30th international conference on High Performance computing, Data, and Analytics (HiPC)

作者： Singh, Akshin Sarangi, Smruti R. IIT Delhi Comp Sci & Engn New Delhi India

ISBN: (纸本)9798350383225

Checkpointing (or snapshotting) a system's state has always been a problem of great interest and has found a lot of use in ensuring system reliability, record-replay debugging, job migration and running high-throughput transaction systems. In the last few years ultra-fast hardware-assisted NVM-based checkpointing schemes have come up that can collect incremental full-system checkpoints in milliseconds. Unfortunately, such systems have large overheads in terms of their write amplification (increased number of writes). This, in turn, seriously reduces the reliability and lifetime of NVM devices. We propose the first tunable scheme in this space, JASS, where given a checkpoint latency (CL), we near-optimally minimize the write amplification (WA). This allows us to run parallel programs in a disciplined fashion. To realize this goal, we propose many novel hardware along the way such as a rigorous method of flushing pre-checkpoint messages in the NoC, a novel DRAM scrubber and locality predictor, and a control-theoretic algorithm to guarantee a CL while minimizing the WA. We reduce WA by 35-96% as compared to the nearest state-of-the-art competing method and improve performance of PARSEC benchmarks by 19.4%.

关键词： NVMs Checkpointing distributed cache flushing

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grid-Connected Wind Energy System with Statcom 2

Grid-Connected Wind Energy System with Statcom

引用

2nd international conference on Automation, computing and Renewable Systems, ICACRS 2023

作者： Deepak, Chandolu Sai Sreejyothi, Khammampati R. Sushma, Yemula Rakesh, Nirati Kumar, Kandunoori Sai Ganesh, Srikakula Departmet of EEE Teegala Krishna Reddy Engineering College India

ISBN: (纸本)9798350340235

An electrical grid-connected wind energy system tries to effectively incorporate wind power while maintaining constant voltage and frequency.A power electronics-based system called the STATCOM offers quick and adaptable reactive power correction. It helps moderate voltage fluctuations, improve power factor, and enhance grid stability. In this system, as wind turbines generate variable power due to wind fluctuations, the STATCOM instantly adjusts its output to regulate grid voltage. This dynamic control aids in maintaining a consistent power supply and mitigates the risk of voltage sags or surges, which can damage equipment and disrupt service. By injecting or absorbing reactive power as needed, the STATCOM also aids in balancing the grid's reactive power demand. The integration of STATCOM with the wind energy system enhances the overall performance of the grid-connected wind farm. It ensures efficient power transmission and contributes to grid reliability also stability. © 2023 IEEE.

关键词： distributed generation Frequency stability grid-connected Inverter control power control Power quality Renewable energy STATCOM Voltage regulation wind energy system

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RAPID: A Rapid Automatic parallelizer for Immense Deep Neural Networks

RAPID: A Rapid Automatic Parallelizer for Immense Deep Neura...

引用

2024 international conference on Cluster computing

作者： Tachon, Thibaut Wang, Haoran Li, Chong Huawei Technol France SASU Boulogne France Huawei Technol Co Ltd Beijing Peoples R China

ISBN: (纸本)9798350383461;9798350383454

Deep Neural Network (DNN) frameworks need parallelism plans to execute immense models. The computed plans often combine data, model, and pipeline parallelism. Unfortunately, due to the intractable property of the problem, the current parallelism planners often fail to derive plans for immense DNNs. They either rely on experts to generate plans manually or profiling for their evaluation, making planners expensive and sub-optimal. We propose RAPID, an automatic parallelism planner for immense DNNs driven by a hierarchical abstract machine model. This model enables the design of a symbolic-based cost model that achieves robust prediction of parallelism cost with symbolic simplification. RAPID divides the parallelization problem hierarchically and symmetrically into linear-time sub-problems. We prove that the composition of the sub-problem solutions is optimal. Large-scale cluster experiments show that RAPID can reduce the planning time of immense DNNs (e.g., BERT) by up to 67x compared to state-of-the-art planners;while exhibiting high performance that matches expert-optimized plans.

关键词： Auto-parallelism large-scale deep learning model parallel and distributed training

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iNUMAlloc: Towards Intelligent Memory Allocation for AI Accelerators with NUMA 21

iNUMAlloc: Towards Intelligent Memory Allocation for AI Acce...

引用

21st IEEE international Symposium on parallel and distributed Processing with Applications, 13th IEEE international conference on Big Data and Cloud computing, 16th IEEE international conference on Social computing and Networking and 13th international conference on Sustainable computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Xu, Yuanchao Qian, Ruyi Wang, Yida Huo, Qirun Capital Normal University College of Information Engineering Beijing China Skl of Computer Architecture Institute of Computing Technology Cas Beijing China

ISBN: (纸本)9798350329223

The amazing success of deep neural network benefits from the rise of big data. As deep learning models are becoming more scale than ever before, their requirements for memory bandwidth are growing at a tremendous pace. Some AI accelerators adopt non-uniform memory access (NUMA) architecture to mitigate this issue and hence complicate device memory allocation. Although extensive studies have been conducted on how to mitigate resource contention and reduce latency, almost all of them target on CPU-oriented NUMA systems but not on AI accelerators where memory allocation precedes task scheduling. The current memory allocator generally adopts an interleaved memory allocation strategy, which is very easy to implement but far from *** tackle this issue, this paper proposes iNUMAlloc, an intelligent memory allocator specialized for AI accelerators with NUMA architecture by combining program behavior and predictable hardware resources altogether. Preliminary evaluation shows that it can help to improve the accuracy and efficiency of memory allocation, thereby achieving stable execution time. © 2023 IEEE.

关键词： Deep neural networks

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Graph-based Multi-view Clustering for Web services 21

Graph-based Multi-view Clustering for Web services

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作者： Yang, Wang Zhenzhen, Yuan Guosheng, Kang Buqing, Cao Jianxun, Liu Yong, Xiao Hnust Xiangtan China Hunan University of Science and Technology Hunan Provincial Key Lab. for Services Computing and Novel Software Technology Xiangtan China School of Computer Science and Engineering China

ISBN: (纸本)9798350329223

The number of Web services on the Internet has been steadily increasing in recent years due to their growing popularity. Under the big data environment, how to effectively manage Web services is of significance for service discovery, service recommendation, etc. It is widely studied that Web services clustering is an effective way for service management. However, most of the current Web service clustering only extracts the information of Web services for clustering from one view, such as Web service content descriptions, networks in which Web services participate, and so on. Extracting information from Web services only unilaterally will not be able to provide a three-dimensional and comprehensive description of Web services, which may diminish the effect of Web service clustering. In addition, some Web service resources will be wasted if other information of Web services is not used at the same time. We find that multi-view clustering can simultaneously consider multiple information of a data at the same time, and multiple information can complement and enhance each other according to the characteristics of multiview clustering. Therefore, in this paper, we apply Web services to graph-based multi-view clustering in multi-view clustering to improve the performance of Web service clustering by simultaneously considering multiple feature information about Web services and distributing different weights to different information in the clustering process. © 2023 IEEE.

关键词： Web services

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distributed Boosting: An Enhancing Method on Dataset Distillation 24

Distributed Boosting: An Enhancing Method on Dataset Distill...

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33rd ACM international conference on Information and Knowledge Management (CIKM)

作者： Chen, Xuechao Meng, Wenchao Wang, Peiran Zhou, Qihang Zhejiang Univ Hangzhou Zhejiang Peoples R China Tsinghua Univ Beijing Peoples R China

ISBN: (纸本)9798400704369

Dataset Distillation (DD) is a technique for synthesizing smaller, compressed datasets from large original datasets while retaining essential information to maintain efficacy. Efficient DD is a current research focus among scholars. Squeeze, Recover and Relabel ((SReL)-L-2) and Adversarial Prediction Matching (APM) are two advanced and efficient DD methods, yet their performance is moderate with lower volumes of distilled data. This paper proposes an ingenious improvement method, distributed Boosting (DB), capable of significantly enhancing the performance of these two algorithms at low distillation volumes, leading to DB-(SReL)-L-2 and DB-APM. Specifically, DB is divided into three stages: Distribute&Encapsulate, Distill, and Integrate&Mix-relabel. DB-(SReL)-L-2, compared to (SReL)-L-2, demonstrates performance improvements of 25.2%, 26.9%, and 26.2% on full 224x224 ImageNet-1k at Images Per Class (IPC) 10, CIFAR-10 at IPC 10, and CIFAR-10 at IPC 50, respectively. Meanwhile, DB-APM, in comparison to APM, exhibits performance enhancements of 21.2% and 20.9% on CIFAR-10 at IPC 10, CIFAR-100 at IPC 1, respectively. Additionally, we provide a theoretical proof of convergence for DB. To the best of our knowledge, DB is the first method suitable for distributed parallel computing scenarios.

关键词： Dataset Distillation distributed Dataset Distillation

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Efficient Hybrid Fuzzing with Syntax-aware Input Trim Based on Lightweight Taint Analysis 21

Efficient Hybrid Fuzzing with Syntax-aware Input Trim Based ...

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作者： Gao, Long Zhao, Yiru Wan, Qihan Zhao, Lei Wuhan University China Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering China

ISBN: (纸本)9798350329223

Hybrid fuzzing as an automated vulnerability detection technique, has gained widespread attention in recent years. It combines the advantages of fuzzing and concolic execution. Yet, existing hybrid fuzzing techniques face practical problems such as the high performance overhead of concolic execution and the difficulty of utilizing the results of the solution in fuzzing. We noted that concolic execution often falls into repetitive loop structures when analyzing testcases generated by fuzzing, leading to the constant construction of redundant constraint expressions, which severely impacts the efficiency of concolic execution. Based on this observation, we design a testcases trimming method to improve the efficiency of concolic execution. The key idea is to use lightweight taint analysis to infer and restore the syntactic structure of the testcase, and through the concept of delta debugging, gradually confirm nodes that do not affect the program coverage path, and remove the corresponding data segments from the testcase. We apply our method to hybrid fuzzing, called TrimFuzz. Our evaluation shows that for testing programs that process highly structured data, TrimFuzz brings an average of 8.2% improvement in code coverage and triggers 115 more unique crashes compared to QSYM. © 2023 IEEE.

关键词： Efficiency

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