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

共 9676 条记录，以下是271-280 订阅

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A New Exact State Reconstruction Strategy for Conjugate Gradient Methods with Arbitrary Preconditioners

A New Exact State Reconstruction Strategy for Conjugate Grad...

引用

IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Viktoria Mayer Wilfried N. Gansterer Faculty of Computer Science University of Vienna UniVie Doctoral School Computer Science DoCS Vienna Austria Faculty of Computer Science University of Vienna Vienna Austria

The likelihood of unanticipated node failures in large-scale parallel computers increases with growing numbers of nodes. Furthermore, global reduction operations become major bottlenecks due to their limited parallel ... 详细信息

ISBN: (数字)9798350364606

ISBN: (纸本)9798350364613

关键词： Computers Gradient methods Fault tolerance distributed processing Scalability Conferences Fault tolerant systems

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parallel Virtual Impedance Based Fluctuation Power Delivery Strategy For MMC Based SST 14

Parallel Virtual Impedance Based Fluctuation Power Delivery ...

引用

14th IEEE international symposium on Power Electronics for distributed Generation systems, PEDG 2023

作者： Li, Jaiyi Zhou, Jianqiao Zhang, Jianwen Shi, Gang Cai, Xu Wang, Yong Dong, Xiaofeng Bao, Wei Donghua University College of Information Science and Technology Shanghai China Shanghai Jiao Tong University Key Laboratory of Control of Power Transmission and Conversion Ministry of Education Shanghai China Beijing Sifang Automation CO. Ltd. Hvdc Transmission and Power Electronics Business Unit Beijing China Facilities Management Suzhou Power Supply Company Suzhou China State Grid Shanghai Municipal Electric Power Company Shanghai Electrical Apparatus Research Institute Group CO. Ltd. Shanghai China

ISBN: (纸本)9798350328233

Modular multilevel converter (MMC) based solid state transformer (MMC-SST) shows promise in hybrid AC/DC distribution grid. This paper introduces a topology consisting of cells made of directly coupled MMC sub-modules (SMs) and isolated SAB modules forming a multi-port SST for grid interconnection. Due to the large SM capacitors for low frequency fluctuation power handling, the device power density is low, which will limit its application. This paper proposes an MMC-SST SM capacitor optimization method based on fluctuation power delivery. It reduces the equivalent low frequency impedance of the SAB by parallel virtual impedance. Low frequency fluctuation power will be delivered into SAB circuit and naturally eliminated in low-voltage DC side due to three-phase symmetrical MMC-SST structure. Therefore, the SM capacitance could be optimized where the power fluctuation flowing into it are reduced. The feasibility of the proposed method is verified by a simulation model. © 2023 IEEE.

关键词： Capacitance

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Adaptive Per-File Lossless Compression of Floating-Point Data

Adaptive Per-File Lossless Compression of Floating-Point Dat...

引用

IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Andrew Rodriguez Noushin Azami Martin Burtscher Department of Computer Science Texas State University San Marcos Texas USA

ISBN: (数字)9798350364606

ISBN: (纸本)9798350364613

The large amount of floating-point data generated by scientific applications makes data compression essential for I/O performance and efficient storage. However, floating-point data is difficult to compress losslessly, and most compression algorithms are only effective on some files. In this paper, we study the benefit of compressing each file with a potentially different algorithm. For this purpose, we created AdaptiveFC, which is based on a tool that can chain data transformations together to generate millions of compression algorithms. AdaptiveFC uses a genetic algorithm to quickly identify an effective compressor in this vast search space for a given file. A comparison of AdaptiveFC to 15 leading lossless GPU compressors on 77 files from 6 datasets in the SDRBench suite shows that per-file compression yields higher compression ratios on average than any individual algorithm.

关键词： distributed processing Conferences Pipelines Graphics processing units distributed databases Data compression Libraries

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An Efficient Sampling-Based SpMM Kernel for Balancing Accuracy and Speed in GNN Inference

An Efficient Sampling-Based SpMM Kernel for Balancing Accura...

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international symposium on parallel and distributed Processing with Applications, ISPA

作者： Yingchen Song Yaobin Wang Chaoyu Xiong Tianhai Wang Pingping Tang School of Computer Science and Technology Southwest University of Science and Technology Mianyang China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

How to coordinate the design of sampling and Sparse-dense Matrix Multiplication (SpMM) is important in Graph Neural Network (GNN) acceleration. However, existing methods have an imbalance between accuracy and speed in performing GNN inference tasks due to irrational sampling strategies. To solve this problem, we propose an adaptive edge sampling strategy SpMM kernel. It considers the relationship between the number of non-zero elements in each matrix row and the shared memory width. The edge sampling scheme is adaptively selected according to the different situations of each row. Our method reduces the graph size by adaptive edge sampling to fit into the GPU’s shared memory, which decreases the computational cost and increases the data locality ultimately achieving a balance between accuracy and speed in GNN inference. We conducted experiments on NVIDIA RTX 4060 Ti GPU using representative GNN model and datasets. Experimental results show that our designed kernel outperforms the cuSPARSE SpMM kernel and GE-SpMM by up to 20.2× and 17.3× respectively, with less than 1% accuracy loss. Compared to ES-SpMM, it reduces the accuracy loss by 4.7% on average and achieves an average 1.26× speedup.

关键词： Proteins distributed processing Accuracy Social networking (online) Graphics processing units distributed databases Graph neural networks Kernel Recommender systems Faces

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The Art of Sparsity: Mastering High-Dimensional Tensor Storage

The Art of Sparsity: Mastering High-Dimensional Tensor Stora...

引用

IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Bin Dong Kesheng Wu Suren Byna Lawrence Berkeley National Laboratory The Ohio State University

ISBN: (数字)9798350364606

ISBN: (纸本)9798350364613

Sparse tensors are prevalent in many applications. While numerous approaches have emerged to optimize the organization of sparse tensors, with the goal of reducing storage requirements and enhancing access performance, a comprehensive examination of the associated time and space complexities has been notably lacking. This study bridges this gap by conducting both theoretical and empirical investigations into various strategies for storing sparse tensors. Our major findings are as follows: (1) Linear address-based organization provides the best balance between storage size and access time; (2) Sparse high-dimensional tensor data can be transformed into lower-dimensional tensors, facilitating efficient storage and access; (3) In the absence of dimension transformation, tree-structured organizations offer compelling performance in low-dimensional tensors and exceptional performance in high-dimensional tensors.

关键词： Bridges distributed processing Tensors Art Conferences distributed databases Organizations

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Design and Development of XiveNet: A Hybrid CAN Research Testbed

Design and Development of XiveNet: A Hybrid CAN Research Tes...

引用

international symposium on parallel and distributed Computing

作者： William Luke Lambert Sheikh Ghafoor Haley Burnell Brennan Huber Farah Kandah Anthony Skjellum Tennessee Technological University Auburn University

ISBN: (数字)9798350369199

ISBN: (纸本)9798350369205

We have developed an affordable distributed Internet of Things (IoT) testbed, named XiveNet, to conduct in-vehicle security research. This testbed merges the adaptability of simulators with the real-time ECU characteristics of actual vehicles. The testbed is made up of ECU chips found in vehicles, Raspberry Pis, and is combined with a bus master simulator. Our experiments with CAN (controller area network) traffic from actual vehicles (Oak Ridge National Laboratories Road Data Set) demonstrate that our testbed closely replicates the attributes of a real vehicle. We have further authenticated our testbed by deploying SecCAN, a secure CAN algorithm, and evaluating its security by injecting invalid frames. Furthermore, we examined ORNL’s timing-based intrusion detection on our testbed and successfully produced alerts. Additionally, we incorporated Named Data Networking (NDN) capable nodes, providing researchers with an additional resource to develop future in-vehicle security solutions. Finally, we have proposed a bitrate hopping technique focused on preventing the denial of service attack and conducted a preliminary investigation using the testbed. Our evaluation and validation indicate that the testbed provides the real-world vehicle environment with the flexibility of a simulation environment that supports a wide range of hardware and software configurations.

关键词： Roads Prevention and mitigation Laboratories Intrusion detection distributed databases Software Real-time systems Hardware Security Internet of Things

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$\eta-\rho$ Pareto Design of a Balancing Converter for Bipolar DC Grids

$\eta-\rho$ Pareto Design of a Balancing Converter for Bipol...

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IEEE international symposium on Power Electronics for distributed Generation systems (PEDG)

作者： Sachin Yadav Zian Qin Pavol Bauer DCE&S ESE Delft University of Technology Delft The Netherlands

This article provides the design procedure of a ±350 V series resonant balancing converter for bipolar DC grids. The process creates a $\eta-\rho$ Pareto front to design a 3 kW converter with natural convection... 详细信息

ISBN: (数字)9798350361001

ISBN: (纸本)9798350361018

关键词： Power system measurements Electric potential Density measurement databases Voltage Switches Power grids

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Understanding Data Storage and Ingestion for Large-Scale Deep Recommendation Model Training 22

Understanding Data Storage and Ingestion for Large-Scale Dee...

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49th IEEE/ACM Annual international symposium on Computer Architecture (ISCA)

作者： Zhao, Mark Agarwal, Niket Basant, Aarti Gedik, Bugra Pan, Satadru Ozdal, Mustafa Komuravelli, Rakesh Pan, Jerry Bao, Tianshu Lu, Haowei Narayanan, Sundaram Langman, Jack Wilfong, Kevin Rastogi, Harsha Wu, Carole-Jean Kozyrakis, Christos Pol, Parik Meta Menlo Pk CA 94025 USA Stanford Univ Stanford CA USA

ISBN: (纸本)9781450386104

Datacenter-scale AI training clusters consisting of thousands of domain-specific accelerators (DSA) are used to train increasingly-complex deep learning models. These clusters rely on a data storage and ingestion (DSI) pipeline, responsible for storing exabytes of training data and serving it at tens of terabytes per second. As DSAs continue to push training efficiency and throughput, the DSI pipeline is becoming the dominating factor that constrains the overall training performance and capacity. Innovations that improve the efficiency and performance of DSI systems and hardware are urgent, demanding a deep understanding of DSI characteristics and infrastructure at scale. This paper presents Meta's end-to-end DSI pipeline, composed of a central data warehouse built on distributed storage and a Data PreProcessing Service that scales to eliminate data stalls. We characterize how hundreds of models are collaboratively trained across geo-distributed datacenters via diverse and continuous training jobs. These training jobs read and heavily filter massive and evolving datasets, resulting in popular features and samples used across training jobs. We measure the intense network, memory, and compute resources required by each training job to preprocess samples during training. Finally, we synthesize key takeaways based on our production infrastructure characterization. These include identifying hardware bottlenecks, discussing opportunities for heterogeneous DSI hardware, motivating research in datacenter scheduling and benchmark datasets, and assimilating lessons learned in optimizing DSI infrastructure.

关键词： Machine learning systems databases distributed systems data ingestion data storage

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P-RECS '22: 5th international Workshop on Practical Reproducible Evaluation of Computer systems 22

P-RECS '22: 5th International Workshop on Practical Reproduc...

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31st international symposium on High-Performance parallel and distributed Computing (HPDC)

作者： Lofstead, Jay Maltzahn, Carlos Jimenez, Ivo Sandia Natl Labs Ctr Comp Res Albuquerque NM 87123 USA Univ Calif Santa Cruz Ctr Res Open Source Software Santa Cruz CA USA

ISBN: (纸本)9781450391993

Reproducibility in computer science is often seen as a difficult to impossible task requiring bespoke hardware and software that others simply cannot field. Because of this, alternative approaches to reproducibility for computer systems is required. This workshop offers experiences and innovations in practical approaches for reproducibility for computer systems. The workshop also serves as the home of the ACM Exploratory Interest Group on Reproducibility and a community gathering for those interested in reproducible computer science research.

关键词： reproducibility computer systems scientific software verification and validation

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HPDC 2022 - Proceedings of the 31st international symposium on High-Performance parallel and distributed Computing

HPDC 2022 - Proceedings of the 31st International Symposium ...

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31st international symposium on High-Performance parallel and distributed Computing, HPDC 2022

ISBN: (纸本)9781450391993

The proceedings contain 32 papers. The topics discussed include: DAOS: data access-aware operating system;FPVM: towards a floating point virtual machine;lifting and dropping VMs to dynamically transition between time- and space-sharing for large-scale HPC systems;access patterns and performance behaviors of multi-layer supercomputer I/O subsystems under production load;capri: compiler and architecture support for whole-system persistence;understanding memory failures on a petascale arm system;SchedInspector: a batch job scheduling inspector using reinforcement learning;Holmes: SMT interference diagnosis and CPU scheduling for job co-location;TLPGNN: a lightweight two-level parallelism paradigm for graph neural network computation on GPU;and TAC: optimizing error-bounded lossy compression for three-dimensional adaptive mesh refinement simulations.

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