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检索条件"任意字段=Proceedings International Symposium on Databases in Parallel and Distributed Systems"

共 4873 条记录，以下是61-70 订阅

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proceedings - 2023 IEEE international Conference on parallel and distributed Processing with Applications, Big Data and Cloud Computing, Sustainable Computing and Communications, Social Computing and Networking, ISPA/BDCloud/SocialCom/SustainCom 2023

Proceedings - 2023 IEEE International Conference on Parallel...

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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

ISBN: (纸本)9798350329223

The proceedings contain 153 papers. The topics discussed include: transaction data management optimization based on multi-partitioning in blockchain systems;semi-asynchronous federated learning optimized for NON-IID data communication based on tensor decomposition;HKTGNN: hierarchical knowledge transferable graph neural network-based supply chain risk assessment;DQR-TTS: semi-supervised text-to-speech synthesis with dynamic quantized representation;deep reinforcement learning-based network moving target defense in DPDK;iNUMAlloc: towards intelligent memory allocation for AI accelerators with NUMA;and predictive queue-based low latency congestion detection in data center networks.

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Efficient erasure-coded multi-data block update methods for heterogeneous storage environments 22

Efficient erasure-coded multi-data block update methods for ...

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

作者： Wang, Hengyu Wei, Bing Luo, Ning Chen, Qian Wu, Yi Zhang, Shudong Zhou, Lijuan Hainan University School of Cyberspace Security Hainan China Hainan University School of International Communication and Arts Hainan China

ISBN: (纸本)9798331509712

Erasure-coded storage systems can achieve highly reliable data storage with low storage overhead. However, updating data blocks necessitates updating parity blocks, and updating multiple data blocks incurs heavy I/O and computational overhead, leading to prolonged update times in heterogeneous storage environments. This paper proposes an erasure-coded multi-data block update method for heterogeneous storage environments, named MBUS, to expedite data updates. MBUS blends re-encoding and delta-based updates and considers factors such as intermediate blocks, computational overhead, and I/O overhead across different storage media. It iteratively computes the minimal I/O time for updating each parity block dynamically. Once the computation equation for each parity block is determined, MBUS prioritizes computing common XOR expressions and reuses the results to reduce the number of XOR operations. MBUS caches a certain number of scheduling plans to minimize redundant computations in subsequent updates, thereby reducing computation time. Experimental results replaying real-world traces demonstrate that MBUS reduces update time by 64.43% compared to state-of-the-art techniques. © 2024 IEEE.

关键词： Computational Optimization Erasure Coding Heterogeneous Storage Environment I/O Optimization Multi-data Block Updates

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ADSwitch: Adaptive CPU Scheduling for distributed systems 21

ADSwitch: Adaptive CPU Scheduling for Distributed Systems

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作者： Zhong, Yidong Ao, Yang Li, Dingding Li, Jianguo Zeng, Deze South China Normal University School of Computer Science Guangdong Guangzhou510631 China China University of Geosciences School of Computer Science Hubei Wuhan430074 China

ISBN: (纸本)9798350329223

In traditional CPU scheduling systems, it is challenging to customize scheduling policies for datacenter workloads. Therefore, distributed cluster managers can only perform coarse-grained job scheduling rather than fine-grained CPU scheduling, although the latter is also critical to application performance in the clusters. Some scheduling systems have been proposed to facilitate the implementation and deployment of custom policies. However, these systems neither adequately capture the resource requirements of workloads nor cooperatively manage the CPU scheduling of distributed nodes, thus may failing to fully utilize CPU resources in the clusters. To tackle these issues, we present ADSwitch, a distributed CPU scheduling system that manages CPU resources adaptively in datacenter clusters. We train an efficient workload classification model based on a recurrent neural network (RNN), which enables ADSwitch to dynamically identify the demand of the running workload and switch to an appropriate scheduling policy through a reliable online update mechanism. We implement ADSwitch and evaluate it in a distributed cluster with synthetic workloads. The experimental results demonstrate that ADSwitch achieves up to 33% lower tail latency and 28% higher throughput than the existing scheduling systems without workload characteristic awareness. © 2023 IEEE.

关键词： Recurrent neural networks

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Accelerating MPI Collectives with Process-in-Process-based Multi-object Techniques 23

Accelerating MPI Collectives with Process-in-Process-based M...

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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： Huang, Jiajun Ouyang, Kaiming Zhai, Yujia Liu, Jinyang Si, Min Raffenetti, Ken Zhou, Hui Hori, Atsushi Chen, Zizhong Guo, Yanfei Thakur, Rajeev Univ Calif Riverside Riverside CA 92521 USA NVIDIA Corp Santa Clara CA USA Meta Platforms Inc Menlo Pk CA USA Argonne Natl Lab Argonne IL USA Natl Inst Informat Tokyo Japan

ISBN: (纸本)9798400701559

In the exascale computing era, optimizing MPI collective performance in high-performance computing (HPC) applications is critical. Current algorithms face performance degradation due to system call overhead, page faults, or data-copy latency, affecting HPC applications' efficiency and scalability. To address these issues, we propose PiP-MColl, a Process-in-Process-based Multi-object Interprocess MPI Collective design that maximizes small message MPI collective performance at scale. PiP-MColl features efficient multiple sender and receiver collective algorithms and leverages Process-in-Process shared memory techniques to eliminate unnecessary system call, page fault overhead, and extra data copy, improving intra- and inter-node message rate and throughput. Our design also boosts performance for larger messages, resulting in comprehensive improvement for various message sizes. Experimental results show that PiP-MColl outperforms popular MPI libraries, including OpenMPI, MVAPICH2, and Intel MPI, by up to 4.6X for MPI collectives like MPI_Scatter and MPI_Allgather.

关键词： mpi collective process-in-process distributed systems message passing interface parallel algorithms

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Carbon-Aware distributed Energy Management for Data Center Microgrids Based on Blockchain 22

Carbon-Aware Distributed Energy Management for Data Center M...

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

作者： Si, Xiaowei Wan, Jianxiong Liu, Chuyi Li, Leixiao Wang, Xiaolei Lu, Xinyu Inner Mongolia University of Technology Hohhot China Inner Mongolia Key Laboratory of Beijiang Cyberspace Security Hohhot China Ministry of Education of the People's Republic of China Research Center of Large-Scale Energy Storage Technologies China

ISBN: (纸本)9798331509712

In this poster, the energy and carbon management problem in Data Center Microgrid (DCMG) is modeled as a Decentralized Partially Observable Markov Decision Process, and the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm is adopted to learn the optimal operating policy. In addition, the system uses blockchain and smart contract to ensure data security and auditability in the power trading process. Finally, simulation results show that the proposed system significantly reduces the overall cost of the DCMGs compared with traditional systems. © 2024 IEEE.

关键词： Blockchain Data Center Microgrids (DCMG) Energy and carbon management MADDPG

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FHNTT: A flexible Number Theoretic Transform design based on hybrid-radix butterfly 22

FHNTT: A flexible Number Theoretic Transform design based on...

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

作者： Li, Li Li, Rengang Zhao, Yaqian Li, Ruyang Su, Zhiyuan Li, Xuelei Ieit Systems Co. Ltd Jinan China Electronic Information Industry Co. Ltd Beijing China

ISBN: (纸本)9798331509712

Emerging technologies, such as cloud computing and artificial intelligence, significantly arouse concern about data security and privacy. Homomorphic encryption (HE) is a promising invention, which enables computation on encrypted data without decrypting it so as to ensure data security and privacy. Nevertheless, computation within homomorphic encryption involves time-consuming operations, e.g., Number Theoretic Transform (NTT). The tremendous computation overhead is the critical obstacle in deploying HE applications widely. Besides, in order to meet the performance and security requirements of different applications, it is pivotal to design parametric NTT architecture. In this paper, we propose a flexible and parametric NTT accelerating scheme based on hybrid-radix butterfly, named FHNTT. Specifically, we construct high radix butterfly units and divide the computation of them into several stages such that every stage can be performed pipelined. The number of required twiddle factors declines with the increase of radix value. In addition, we adopt address offset strategy to reduce memory consumption. We implement FHNTT on FPGA due to its fine-grained parallel computing capabilities and customized architecture. Empirical results show that FHNTT has an improved performance compared with other NTT architectures and supports a wide range of parameters. Concretely, FHNTT achieves up to 1.99 × to 2.78 × improvement in latency over other FPGA implementations and the memory utilization rate is up to 94%. Moreover, the flexibility makes FHNTT applicable to multiple use cases. © 2024 IEEE.

关键词： FPGA Acceleration Number Theoretic Transform parallel Computing Parametric architecture

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Combining Lossy Compression with Multi-level Caching for Data Staging over Network

Combining Lossy Compression with Multi-level Caching for Dat...

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

作者： Aoyagi, Rei Takahashi, Keichi Shimomura, Yoichi Takizawa, Hiroyuki Tohoku Univ Grad Sch Informat Sci Sendai Miyagi Japan Tohoku Univ Cybersci Ctr Sendai Miyagi Japan

ISBN: (纸本)9798350364613;9798350364606

Researchers conduct post-processing on the simulation results by running an interactive data analysis tool on a High-Performance Computing (HPC) system installed at an HPC center and retrieving the post-processed results. Certain data analysis scenarios require to transfer the simulation results directly from the center. in such scenarios, a portion of the data would usually be streamed over the network to achieve interactivity. However, there still exist two challenges in maintaining interactivity: (1) limited network bandwidth and (2) long network latency. To tackle these challenges, we propose a system to enable interactive array analysis over the network. We employ error-bounded lossy compression to increase the effective network bandwidth. Furthermore, we employ multi-level caching to hide the network latency and combine prefetching to improve the cache hit ratio. The cache replacement and prefetching policies are designed considering the data access pattern of interactive analysis. We compared our proposed system with TileDB, one of the state-of-the-art array databases, by measuring the average latency for various access patterns. Compared to TileDB, the proposed system reduces the average latency by up to 91.6% by allowing 10% of error because the cache hit ratio was improved by more than 40% due to the proper cache replacement and prefetching policy and network transfer time was reduced more than 75% by using lossy compression.

关键词： HPC distributed Computing Data Staging Lossy Compression Interactive Analysis Bandwidth Software Caching

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Coded parallelism for distributed Deep Learning

Coded Parallelism for Distributed Deep Learning

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2023 IEEE international symposium on Information Theory, ISIT 2023

作者： Ji, Songting Zhang, Zhaoyang Yang, Zhaohui Jin, Richeng Yang, Qianqian College of Information Science and Electronic Engineering Zhejiang University Hangzhou310027 China Hangzhou310027 China Zhejiang University International Joint Innovation Center Haining314400 China

ISBN: (纸本)9781665475549

With the rapid development of deep learning, the parameters of modern neural network models, especially in the field of Natural Language Processing (NLP) are extremely huge. When the parameters of the model are larger even than the storage memory of a single device, it is necessary to split the original big learning model into different parts with each part assigned to one device, thus realizing joint model training over different devices (i.e., distributed training). In this paper, we aim to introduce the advanced coding scheme into the distributed parallel framework, which leads to the perfect combination of coding and the underlying calculation of neural networks. The proposed scheme is not only able to avoid the impact of poor computing power or low bandwidth and even dropped devices (stragglers) on system performance but also reduce the communication load between different devices, thereby greatly improving the performance of distributed parallel systems. © 2023 IEEE.

关键词： Deep learning

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DTS: A Simulator to Estimate the Training Time of distributed Deep Neural Networks 30

DTS: A Simulator to Estimate the Training Time of Distribute...

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30th international symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication systems (MASCOTS)

作者： Robinson, Wilfredo J. M. Esposito, Flavio Zuluaga, Maria A. St Louis Univ Dept Comp Sci St Louis MO 63103 USA Eurecom Data Sci Dept Biot France

ISBN: (纸本)9781665455800

Deep Neural Networks (DNNs) process big datasets achieving high accuracy on incredibly complex tasks. However, this progress has led to a scalability impasse, as DNNs require massive amounts of processing power and local memory to be trained, making them impossible or impractical to be used on a single device. This situation has led to the design of distributed training architectures, where the DNN and the training data can be split among multiple processors. How to choose the appropriate distributed training architecture, however, remains an open question. To help bring insights into this debate, in this work we design a distributed Training Simulator (DTS) that estimates the training time of a DNN in a distributed architecture through a mathematical model of the distributed architecture and resource-allocation heuristics. We illustrate the power of the proposed DTS through the implementation of five different distributed architectures, Pipeline Learning, Federated Learning, Split Learning, parallel Split Learning, and Federated Split Learning, and we validate the accuracy of the training estimates using three different datasets of varying complexity and two different DNNs. Finally, we present a trade-off analysis to demonstrate the coherence of DTS estimates for diverse high-performance computing scenarios by comparing these estimates with the behaviors of a real computer cluster.

关键词： big data distributed computing deep learning simulator

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proceedings - 23rd IEEE/ACM international symposium on Cluster, Cloud and Internet Computing, CCGrid 2023

Proceedings - 23rd IEEE/ACM International Symposium on Clust...

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23rd IEEE/ACM international symposium on Cluster, Cloud and Internet Computing, CCGrid 2023

ISBN: (纸本)9798350301199

The proceedings contain 59 papers. The topics discussed include: rethinking design paradigm of graph processing system with a CXL-like memory semantic fabric;a case study of data management challenges presented in large-scale machine learning workflows;an asynchronous dataflow-driven execution model for distributed accelerator computing;an empirical study of container image configurations and their impact on start times;how workflow engines should talk to resource managers: a proposal for a common workflow scheduling interface;Layercake: efficient inference serving with cloud and mobile resources;optimal sizing of a globally distributed low carbon cloud federation;predicting the performance-cost trade-off of applications across multiple systems;a deep learning pipeline parallel optimization method;HDFL: a heterogeneity and client dropout-aware federated learning framework;heterogeneous federated learning using dynamic model pruning and adaptive gradient;and measuring the impact of gradient accumulation on cloud-based distributed training.

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