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检索条件"任意字段=Proceedings of the first international symposium on Databases in parallel and distributed systems"

共 796 条记录，以下是11-20 订阅

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distributed Training of Neural Radiance Fields: A Performance Characterization

Distributed Training of Neural Radiance Fields: A Performanc...

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2024 IEEE international symposium on Performance Analysis of systems and Software, ISPASS 2024

作者： Zhao, Adrian Zhang, Louis Durvasula, Sankeerth Chen, Fan Jain, Nilesh Panneer, Selvakumar Vijaykumar, Nandita University of Toronto Canada Intel Labs United States Vector Institute Canada

ISBN: (纸本)9798350376388

Implicit neural representation is an emerging method that leverages deep neural networks and learned parameters to represent 3D scenes efficiently and accurately. Neural radiance field (NeRF) is a state-of-art implicit representation that achieves photorealistic 3D reconstruction with compact neural network models. However, as the complexity and scale of the scene increase, training NeRF models with a single GPU proves insufficient for achieving fast training and high-quality reconstruction. To address this challenge, prior works proposed distributed NeRF training methods. This is the first work to conduct a detailed evaluation of two major distributed NeRF training methods and their tradeoffs: distributed data parallel (DDP) and spatial segmentation (SS). We find that DDP training requires cross-device synchronization during training, while SS training incurs additional fusion overhead during inference. Our analysis also reveals that sampling input images is a common key bottleneck in distributed NeRF training. At the beginning of each training iteration, the CPU generates input batches for all GPUs in the cluster by sampling all images in the dataset, causing significant stalls that constitute up to 43.3% of the total training time. To alleviate this bottleneck, we propose a pipelined input sampling strategy that precomputes input samples on the CPU concurrently with model training on the GPUs. Our evaluation demonstrates an average speedup in training time by 1.95×( up to 2.24×). © 2024 IEEE.

关键词： Deep neural networks

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A new Ad-Hoc parallel file system for HPC environments based on the Expand parallel file system 22

A new Ad-Hoc parallel file system for HPC environments based...

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22nd international symposium on parallel and distributed Computing, ISPDC 2023

作者： Garcia-Carballeira, Felix Camarmas-Alonso, Diego Caderon-Mateos, Alejandro Carretero, Jesus Universidad Carlos Iii de Madrid Computer Science and Engineering Departament Leganes Spain

ISBN: (纸本)9798350341270

An Ad-Hoc File System dynamically virtualizes storage on compute nodes into a fast storage volume to reduce congestion on parallel file systems used as backends in HPC environments and improve data locality. This paper presents Expand Ad-Hoc, a version of the Expand parallel file system, for use as an Ad-Hoc storage system for HPC environments. Such an update seeks to take better advantage of new storage technologies (on SSDs local to nodes, for example) and to adapt to new demands of current parallel applications (e.g., optimizing data access by analyzing data locality). The paper describes the new system's features and presents a first evaluation comparing the performance of Expand with another Ad-Hoc File System (GekkoFS), and GPFS. The first results are quite satisfactory and demonstrate the good performance of Expand as an Ad-Hoc storage system. © 2023 IEEE.

关键词： Digital storage

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Sampling Policies for Near-Optimal Device Choice in parallel Simulations on CPU/GPU Platforms 28

Sampling Policies for Near-Optimal Device Choice in Parallel...

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28th IEEE/ACM international symposium on distributed Simulation and Real Time Applications, DS-RT 2024

作者： Andelfinger, Philipp Pellegrini, Alessandro Marotta, Romolo University of Rostock Germany Tor Vergata University of Rome Italy

ISBN: (纸本)9798331527211

Heterogeneous hardware platforms comprised of CPUs, GPUs, and other accelerators offer the opportunity to choose the best-suited device for executing a given scientific simulation in order to minimize execution time and energy consumption. To this end, the recently proposed "Follow the Leader"approach dynamically selects a suitable device based on runtime performance measurements during speculative discrete-event simulations. A currently active "leader"device is periodically challenged by a "follower"device in order to negotiate the new leader. The optimality of the device choices and the associated overhead depends critically on the challenge frequency and timing. Here, we explore policies to schedule challenges with the goal of attaining Pareto-optimal combinations of execution time and energy consumption. Several heuristics are first evaluated in an abstract fashion using a "meta-simulation"by mimicking the progress and energy consumption of an idealized co-execution. In this setting, we optimize the heuristics' tuning parameters to assess their relative merits in near-optimal configurations when compared to challenge timings based on perfect knowledge. We find that under challenging stochastic workloads based on a class of mean-reverting random walks, the best heuristics can closely approximate the execution time and energy consumption achievable under an optimal device choice. Empirical support for this observation is given by measurements of a CPU/GPU co-execution of the Time Warp algorithm on physical hardware. © 2024 IEEE.

关键词： Stochastic systems

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21st international symposium on Automated Technology for Verification and Analysis, ATVA 2023

21st International Symposium on Automated Technology for Ver...

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21st international symposium on Automated Technology for Verification and Analysis, ATVA 2023

ISBN: (纸本)9783031453311

The proceedings contain 38 papers. The special focus in this conference is on Automated Technology for Verification and Analysis. The topics include: Model Checking Strategies from Synthesis over Finite Traces;reactive Synthesis of Smart Contract Control Flows;synthesis of distributed Protocols by Enumeration Modulo Isomorphisms;controller Synthesis for Reactive systems with Communication Delay by Formula Translation;statistical Approach to Efficient and Deterministic Schedule Synthesis for Cyber-Physical systems;compositional High-Quality Synthesis;learning Provably Stabilizing Neural Controllers for Discrete-Time Stochastic systems;an Automata-Theoretic Approach to Synthesizing Binarized Neural Networks;syntactic vs Semantic Linear Abstraction and Refinement of Neural Networks;learning Nonlinear Hybrid Automata from Input–Output Time-Series Data;using Counterexamples to Improve Robustness Verification in Neural Networks;a Novel Family of Finite Automata for Recognizing and Learning ω -Regular Languages;on the Containment Problem for Deterministic Multicounter Machine Models;parallel and Incremental Verification of Hybrid Automata with Ray and Verse;an Automata Theoretic Characterization of Weighted first-Order Logic;Graph-Based Reductions for Parametric and Weighted MDPs;scenario Approach for Parametric Markov Models;Fast Verified SCCs for Probabilistic Model Checking.

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21st international symposium on Automated Technology for Verification and Analysis, ATVA 2023

21st International Symposium on Automated Technology for Ver...

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21st international symposium on Automated Technology for Verification and Analysis, ATVA 2023

ISBN: (纸本)9783031453281

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INODE - Intelligence Open Data Exploration 4

INODE - Intelligence Open Data Exploration

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4th international Workshop on systems and Network Telemetry and Analytics (SNTA)

作者： Stockinger, Kurt Zurich Univ Appl Sci Winterthur Switzerland

This article describes the keynote speech on INODE presented at Fourth international Workshop on systems and Network Telemetry and Analytics (SNTA) which is collocated with international ACM symposium on High -Perform... 详细信息

ISBN: (纸本)9781450383868

关键词： Natural language interfaces to databases knowledge graph generation question answering query processing.

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Cybersecurity Insights: Analyzing IoT Data Through Statistical and Visualization Techniques

Cybersecurity Insights: Analyzing IoT Data Through Statistic...

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parallel Computing and distributed systems (PCDS), international symposium on

作者： Jing Li Mohd Shahizan Othman Hewan Chen Lizawati Mi Yusuf Faculty of Computing University Technology Malaysia Johor Bahru Malaysia Research Center of Digital Twins China Jiliang University Hang Zhou China

ISBN: (数字)9798350349658

ISBN: (纸本)9798350349665

The rising frequency of cyberattacks on Internet of Things (IoT) networks calls for sophisticated approaches to threat analysis and mitigation. The importance of data analysis-especially visualization-in identifying patterns that can guide successful mitigation efforts is emphasized in this research. To address class imbalance issues, we first developed thorough data preparation and basic class analysis. Next, we used the BoT-IoT and TON-IoT datasets to visualize an analysis of 5-tuple network properties. The Spearman and Pearson correlation coefficients were used to statistically validate these properties. Furthermore, class overlapping was revealed using unsupervised techniques using PCA and T-SNE, which shed light on the characteristics of IoT data. Our results show how these visualization methods can help reveal patterns in IoT datasets and illuminate the nature of the representative IoT data, in addition, this approach contributes to the development of robust cybersecurity safeguards for IoT security in the future.

关键词： Correlation coefficient Prevention and mitigation Data visualization distributed databases parallel processing Internet of Things Computer crime Principal component analysis

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Bandwidth Characterization of DeepSpeed on distributed Large Language Model Training

Bandwidth Characterization of DeepSpeed on Distributed Large...

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2024 IEEE international symposium on Performance Analysis of systems and Software, ISPASS 2024

作者： Hanindhito, Bagus Patel, Bhavesh John, Lizy K. The University of Texas at Austin AustinTX United States Dell Technologies Round RockTX United States

ISBN: (纸本)9798350376388

The exponential growth of the training dataset and the size of the large language model (LLM) significantly outpaces the incremental memory capacity increase in the graphics pro-cessing units (GPUs). Thousands of GPUs are needed to handle state-of-the-art models, which require building an expensive AI GPU cluster that is out of reach for most researchers. This not only makes the cost to train the model more costly but also signifies the environmental impact. To improve the efficiency and scalability of existing infrastructure to handle increasingly demanding training tasks, Microsoft released DeepSpeed, an open-source optimization library for PyTorch that can easily be integrated into existing training flow with minimal code changes. This paper presents a comprehensive third-party evaluation of DeepSpeed for training GPT-2-like LLM on mainstream GPU clusters that are more accessible to everyone. The evaluation includes memory usage analysis and bandwidth characterization in addition to the achieved model size and the attained compute throughput to help compare horizontal and vertical scaling. first, we examine the DeepSpeed ZeRO in single- and dual-node training against the popular distributed training libraries: PyTorch distributed Data-parallel (DDP) with data parallelism and Megatron-LM with data and model parallelism. While DDP achieves higher throughput due to less communication, the model size is limited to a single GPU memory capacity. In single-node training, Megatron-LM can fit a 4x larger model than the DDP, while ZeRO can handle a model with 0.8x-l.2x size of the Megatron-LM. Both Megatron-LM and ZeRO are reasonably competitive in terms of throughput. However, in dual-node training, Megatron- Lmsees a significant drop in throughput due to the excessive inter-node communication, achieving only 25 %-30 % of the throughput offered by ZeRO. Secondly, we evaluate ZeRO-Offload to consolidate multi-node training into single-node. With CPU offloading, ZeRO-Offloa

关键词： Bandwidth

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Collaborative Recommendation with Knowledge Graph Enhancement and Contrastive Learning

Collaborative Recommendation with Knowledge Graph Enhancemen...

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international symposium on parallel and distributed Processing with Applications, ISPA

作者： Huiyi Zhong Pinghua Chen Yunhua Chen Honghong Zhou School of Computer Science and Technology GuangDong University of Technology Guangzhou China Guangdong Science & Technology Innovation Monitoring and Research Center Guangzhou China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Recommendation systems aim to provide personalized recommendations by analyzing user preferences. However, they encounter significant challenges of data sparsity and distribution skewness, which can impair their accuracy. To address these issues, we propose a collaborative recommendation approach integrating Knowledge Graph Enhancement and Contrastive Learning(KGECL). In this approach, the Knowledge Graph (KG) enhancement module first generates structurally diverse subgraphs via random perturbations and then calculates their structural stability scores to guide the generation of the user-item interaction(U-I) graph. The U-I graph enhancement module generates contrastive views from perturbed graphs using Variational Graph Autoencoders (VGAE)to alleviate the impact of skewed data distribution and sparsity. Finally, the enhanced KG is concatenated with the U-I graph to jointly construct embeddings for both users and items. Experimental results on three public datasets (MIND, yelp2018, and amazon-book) demonstrate that KGECL outperforms baseline models in terms of Recall and Normalized Discounted Cumulative Gain (NDCG), achieving the desired improvements.

关键词： distributed processing Perturbation methods Noise Collaboration distributed databases Knowledge graphs Contrastive learning Data models Structural engineering Recommender systems

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Controlled Copying of Persistent Data Between end Users' SGX Enclaves over an Untrusted Network

Controlled Copying of Persistent Data Between end Users' SGX...

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parallel Computing and distributed systems (PCDS), international symposium on

作者： Jun Ishiguro Yasushi Shinjo Akifumi Soyama Department of Computer Science University of Tsukuba Tsukuba Ibaraki Japan

ISBN: (数字)9798350349658

ISBN: (纸本)9798350349665

It is difficult to protect digital rights while allowing data to be freely copied over the Internet. This paper describes controlled copying of persistent data between two end users's PCs without centralized servers. In the proposed method, a user accesses protected data through the program running in a trusted execution environment (TEE) of the user's PC. When a user sends data to another user, the program in the sender's TEE first encrypts the data and sends it to the program in the receiver's TEE without access permission. Next, the sender's and receiver's programs perform a distributed transaction, and the sender may pass on some of its access rights to the receiver. For example, in the case of data moving, the sender loses access completely and the receiver gains access. In the case of data leasing, the receiver gains access until the end of the lease period, after which the sender is able to access the data again. The proposed method works on an untrusted network, untrusted local persistent storage, and trusted local monotonic counters. Unlike typical digital rights management (DRM) mechanisms, the proposed method enables the resell of data among users while avoiding censorship or privacy violations. The proposed method has been implemented using Intel Software Guard Extensions (SGX). The proposed method can defend against attacks on programs, storage, and networks. Experimental results show that data moving over the Internet could be performed in a useful time.

关键词： Access control Data privacy distributed databases Receivers Permission parallel processing Software Internet Servers Digital rights management

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