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

引用

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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NetDS: distributed Search Framework with Hybrid Acceleration Methods

NetDS: Distributed Search Framework with Hybrid Acceleration...

引用

international symposium on parallel and distributed Processing with Applications, ISPA

作者： Penghao Zhang Zhiguo Hu School of Computer and Information Technology (School of Big Data) Shanxi University Taiyuan China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Approximate neighbour nearest search has achieved great success for indexing similar high-dimensional data in distributed search systems. As the scale of data vectors grows, distributed search require large storage, low latency, and high throughput on processing vectors. To achieve this, researchers tend to load balance data with more machines and implement efficient distributed frameworks, but they need to pay huge storage overhead, which leads to inefficient network *** address this gap, we propose NetDS, which exploits the computational capacity of in-network computation and the storage capacity of solid-state drives. NetDS utilizes a multi-level constrained balanced tree to process data vectors and construct multi-level tables. Then, NetDS proposes a heuristic neighbour graph to solve the boundary data problem. NetDS also offloads central tables and graphs into switches to accelerate vector classification. Finally, NetDS designs hybrid storage and query pre-match methods to accelerate the ANNS distributed system. We deploy NetDS on a programmable switch and evaluate it. NetDS completes the data and query processing in a shorter time than other typical distributed frameworks.

关键词： Lead acid batteries Solid state drives Query processing distributed databases Switches Throughput Vectors Computational efficiency Servers Low latency communication

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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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Configuring the parallel Committees Database Architecture through Bayesian Optimization

Configuring the Parallel Committees Database Architecture th...

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Blockchain Computing and Applications (BCCA), international Conference on

作者： Siamak Solat Ahmed Mabrouk Université Paris Cité (Formerly Known as Université de Paris) Paris France CSAI Lab CRIGEN ENGIE Group Stains France

ISBN: (数字)9798350351538

ISBN: (纸本)9798350351545

distributed databases are often used when scalability, fault tolerance, and high availability are crucial. They excel in scenarios where traditional, centralized databases may struggle to handle the increasing volume of data or traffic. Common use cases include large-scale web applications, cloud computing environments, and systems requiring geographic distribution for improved performance and resilience. The "parallel Committees" architecture was recently proposed, designed, and published in [1] as an innovative and novel approach for distributed databases. The configuration of the parallel Committees architecture must be precisely tailored based on a set of input parameters. In this paper, we propose an approach to configure the parallel Committees database architecture through Bayesian optimization, aiming to identify the optimal parameter configuration for each specific use case.

关键词： Fault tolerance Cloud computing Scalability Fault tolerant systems Computer architecture Bayes methods Blockchains Optimization Resilience

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distributed Training of Neural Radiance Fields: A Performance Characterization

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

引用

IEEE international symposium on Performance Analysis of systems and Software

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

ISBN: (数字)9798350376388

ISBN: (纸本)9798350376395

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 $1.95\times($ up to $2.24\times)$ .

关键词： Training Solid modeling Three-dimensional displays Graphics processing units distributed databases parallel processing Neural radiance field

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New parallel Order Maintenance Data Structure

New Parallel Order Maintenance Data Structure

引用

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

作者： Bin Guo Emil Sekerinski Department of Computer Science Trent University Peterborough Canada Department of Computing and Software McMaster University Hamilton Canada

ISBN: (数字)9798350349658

ISBN: (纸本)9798350349665

The Order-Maintenance (OM) data structure is to keep a fully ordered list of items, supporting operations including insertions, deletions, and comparisons. As a crucial data structure, OM is widely used in various applications. With the increasing availability of multicore processors, there is a strong incentive to parallelize this essential data structure. This paper introduces a novel parallel OM data structure.

关键词： Program processors Multicore processing parallel processing Data structures Maintenance

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Convergence of HPC and Big Data in extreme-scale data analysis through the DCEx programming model 34

Convergence of HPC and Big Data in extreme-scale data analys...

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34th IEEE international symposium on Computer Architecture and High Performance Computing (SBAC-PAD)

作者： Garcia-Blas, Javier Fernandez Munoz, Javier Carretero, Jesus Marozzo, Fabrizio Talia, Domenico Trunfio, Paolo Fernandez-Pena, Alberto Martin de Blas, Daniel Univ Carlos III Madrid Comp Sci & Engn Dept Leganes Spain Univ Calabria DIMES Dept Arcavacata Di Rende Italy Univ Carlos III Madrid Inst Invest Sanitaria Gregorio Maranon Dept Bioingn & Ingn Aerosp Madrid Spain

ISBN: (数字)9781665451550

ISBN: (纸本)9781665451550

High-level programming models can help application developers to access and use resources without the need to manage low-level architectural entities, as a parallel programming model defines a set of programming abstractions that simplify the way by which a programmer structures and expresses her/his algorithm. Early proposals of Exascale programming tools are based on the adaptation of traditional parallel programming languages and hybrid solutions. This incremental approach is too conservative, often resulting in very complex code. This paper describes the design features, the programming constructs, and the runtime mechanisms of the Data Centric programming model for Exascale systems (DCEx). DCEx is based on structuring applications into data-parallel blocks. Blocks are units of shared-and distributed-memory parallel computation, communication, and migration in the memory/storage hierarchy. Blocks and their message queues are mapped onto processes and placed in memory/storage by the DCEx runtime. Those data-parallel blocks are orchestrated by using distributed parallel patterns that simplify the development cost. DCEx aims to reach the convergence of traditional HPC programming models, mainly based on MPI, with the emerging technologies based on the data intensive paradigms. To demonstrate the potential of DCEx, we carried out an experimental evaluation developing a real-world diffusion-weighted magnetic resonance imaging data processing application in a neuroimaging research context.

关键词： Big Data HPC convergence Programming model PGAS parallel patterns

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DAUSK: A Transactional Graph Structure for Skewed Dynamic Graph Storage

DAUSK: A Transactional Graph Structure for Skewed Dynamic Gr...

引用

international symposium on parallel and distributed Processing with Applications, ISPA

作者： Anna Ding Yujun Li Yuening Feng Sicong Hu Mengshu Hou Yueyang Zhan University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798331509712

ISBN: (纸本)9798331509729

Dynamic graphs are widely used in real-world applications and exhibit structural skew, which leads to skewed updates. Existing systems that support transactional updates often fail to fully consider this phenomenon, struggling to balance update and query performance. Inspired by the concepts of hierarchical thinking and uneven rebalancing on PMA, we propose DAUSK, a high-performance in-memory structure for dynamic graph storage that supports efficient graph analytics and rapid transactional updates. DAUSK addresses the skewed characteristics of graphs by employing two types of data structures: an unrolled skip list for frequently updated high-degree vertices, allowing asymptotically faster searches and updates, and a compact array-like structure called UPMA for medium and low-degree vertices, enabling efficient sequential scans. At the same time, DAUSK uses a vertex-centric strategy to partition UPMA and applies uneven gap allocation based on vertex degrees. Futhermore, considering the characteristics of transactions on graphs, DAUSK uses the well-established 2PL concurrency protocol to support millions of transactional updates per second. Experimental results demonstrate that DAUSK is up to 10.17×, 3.29× and 1.52× faster in ingesting graph updates compared to three state-of-the-art transactional graph systems, i.e., LiveGraph, Teseo and Sortledton. As for graph analytics, DAUSK achieves comparable, if not superior, performance compared to the other three graph systems.

关键词： Concurrent computing distributed processing Analytical models Protocols Memory management distributed databases Data models Safety Resource management Arrays

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Accelerating Extra Dimensional PageWalks for Confidential Computing 23

Accelerating Extra Dimensional PageWalks for Confidential Co...

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56th IEEE/ACM international symposium on Microarchitecture (MICRO)

作者： Du, Dong Yang, Bicheng Xia, Yubin Chen, Haibo Shanghai Jiao Tong Univ Inst Parallel & Distributed Syst Engn Res Ctr Domain Specif Operating Syst MoE Shanghai Peoples R China Shanghai Jiao Tong Univ Shanghai AI Lab Inst Parallel & Distributed Syst Engn Res Ctr Domainspecif Operating Syst MoE Shanghai Peoples R China

ISBN: (纸本)9798400703294

To support highly scalable and fine-grained computing paradigms such as microservices and serverless computing better, modern hardware-assisted confidential computing systems, such as Intel TDX and ARM CCA, introduce permission table to achieve finegrained and scalable memory isolation among different domains. However, it also adds an extra dimension to page walks besides page tables, leading to significantly more memory references (e.g., 4 -> 12 for RISC-V Sv39)(1). We observe that most costs (about 75%) caused by the extra dimension of page walks are used to validate page table pages. Based on this observation, this paper proposes HPMP (Hybrid Physical Memory Protection), a hardware-software co-design (on RISC-V) that protects page table pages using segment registers and normal pages using permission tables to balance scalability and performance. We have implemented HPMP and Penglai-HPMP (a TEE system based on HPMP) on FPGA with two RISC-V cores (both in-order and out-of-order). Evaluation results show that HPMP can reduce costs by 23.1%-73.1% on BOOM and significantly improve performance on real-world applications, including serverless computing (FunctionBench) and Redis.

关键词： Hardware-software codesign

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Brief Announcement: distributed Algorithms for Minimum Dominating Set Problem and Beyond, a New Approach 36

Brief Announcement: Distributed Algorithms for Minimum Domin...

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36th international symposium on distributed Computing, DISC 2022

作者： Alipour, Sharareh Salari, Mohammadhadi Tehran Institute for Advanced Studies Iran Simon Fraser University Burnaby Canada

ISBN: (纸本)9783959772556

In this paper, we study the minimum dominating set (MDS) problem and the minimum total dominating set (MTDS) problem. We propose a new idea to compute approximate MDS and MTDS. This new approach can be implemented in a distributed model or parallel model. We also show how to use this new approach in other related problems such as set cover problem and k-distance dominating set problem. © Sharareh Alipour and Mohammadhadi Salari.

关键词： distributed computer systems

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