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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121-130 订阅

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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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Embedded RDMA Lite for FPGA Interconnect in Active Electronically Scanned Array RADAR

Embedded RDMA Lite for FPGA Interconnect in Active Electroni...

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IEEE international symposium on Phased Array systems and Technology

作者： Pierre-Emile Duhamel Haixiao Yue Jeffrey Okurowski Dan Pritsker Altera® an Intel Company San Jose CA

ISBN: (数字)9798350392142

ISBN: (纸本)9798350392159

With new advances in Field Programmable Gate Array (FPGA) technology, such as the Agilex™ 9 Direct RFSeries FPGA, which combines high speed data converters with a large FPGA device, distributed capture and processing of radar data may become more common. For some radar algorithms transfer of data between the distributed FPGA devices may be necessary. Here we describe the implementation of our Embedded RDMA Lite core design that allows DSP designs on an FPGA to read and write data across a network as easily as using a local DMA operation. We describe how network data transfers may be necessary in a distributed Space Time Adaptive Processing (STAP) application and show an example design that uses the Embedded RDMA Lite core to transfer part of a radar data cube for STAP processing.

关键词： Phased arrays Array signal processing Spaceborne radar distributed databases Adaptive arrays Signal processing algorithms Radar parallel processing Logic gates Field programmable gate arrays

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Access Patterns and Performance Behaviors of Multi-layer Supercomputer I/O Subsystems under Production Load 22

Access Patterns and Performance Behaviors of Multi-layer Sup...

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

作者： Bez, Jean Luca Karimi, Ahmad Maroof Paul, Arnab K. Xie, Bing Byna, Suren Carns, Philip Oral, Sarp Wang, Feiyi Hanley, Jesse Lawrence Berkeley Natl Lab Berkeley CA 94720 USA Oak Ridge Natl Lab Oak Ridge TN USA Birla Inst Technol Sci Pilani Rajasthan India Argonne Natl Lab Argonne IL USA BITS Pilani Pilani Goa India

ISBN: (纸本)9781450391993

Scientific computing workloads at HPC facilities have been shifting from traditional numerical simulations to AI/ML applications for training and inference while processing and producing ever-increasing amounts of scientific data. To address the growing need for increased storage capacity, lower access latency, and higher bandwidth, emerging technologies such as non-volatile memory are integrated into supercomputer I/O subsystems. With these emerging trends, we need a better understanding of the multilayer supercomputer I/O systems and ways to use these subsystems efficiently. In this work, we study the I/O access patterns and performance characteristics of two representative supercomputer I/O subsystems. Through an extensive analysis of year-long I/O logs on each system, we report new observations in I/O reads and writes, unbalanced use of storage system layers, and new trends in user behaviors at the HPC I/O middleware stack.

关键词： Access Patterns parallel File systems In-System Storage High-Performance Computing Production System

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An In-Situ Sliding Window Approximate Inner-Product Scheme Based on parallel distributed Arithmetic for Ultra-Low Power Fault-Tolerant Applications

An In-Situ Sliding Window Approximate Inner-Product Scheme B...

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IEEE international Midwest symposium on Circuits and systems (MWSCAS)

作者： Rizk, Dominick Rizk, Rodrigue Rizk, Frederic Kumar, Ashok Univ Louisiana Lafayette Ctr Adv Comp Studies Lafayette LA 70504 USA

ISBN: (纸本)9781665424615

Approximate computing (AC) provides an efficient solution for reducing power, area, and complexity of digital systems. When backed with distributed arithmetic (DA), AC leverages the ability to implement ultra-efficient inner-product units in terms of area, power, and delay. Such units can be used in any inherently resilient application. This paper presents a novel scheme of approximate inner-product based on parallel DA for low-power fault-tolerant applications backed with a novel in-situ sliding window algorithm. Our model eliminates the need for an explicit error correction scheme, which further reduces the overhead while improving the accuracy. The experimental results show that our model achieves a state-of-the-art performance in terms of power delay product (PDP), area power product (APP) with a reduction of 39.26% and 48.83%, respectively.

关键词： Approximate computing distributed arithmetic DSP inner-product LUT memory VLSI SOP

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Adaptive Prefetching for Fine-grain Communication in PGAS Programs

Adaptive Prefetching for Fine-grain Communication in PGAS Pr...

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international symposium on parallel and distributed Processing (IPDPS)

作者： Thomas B. Rolinger Alan Sussman NVIDIA Austin TX USA University of Maryland College Park MD USA

ISBN: (数字)9798350387117

ISBN: (纸本)9798350387124

Applications that require distributed-memory systems and exhibit irregular memory access patterns present both productivity and performance challenges. This is largely due to the fine-grain communication that arises from irregular memory accesses to distributed data. The Partitioned Global Address Space (PGAS) model provides a globally shared address space and one-sided communication, making it well suited for implementing irregular codes. However, while the PGAS model provides productivity advantages, irregular applications have difficulty achieving high performance due to the cost of fine-grain remote accesses. One way to improve the performance of such PGAS programs is to prefetch remote data before it is needed, thereby hiding communication latency. The challenges of applying prefetching are computing the prefetch distance (i.e., how far ahead to issue prefetches), determining when prefetching will be profitable, and modifying the program to perform prefetching. In this work, we present an adaptive prefetching optimization that can be applied to PGAS programs with irregular memory access patterns. We target the Chapel parallel programming language, which implements a PGAS model. Our optimization leverages runtime information to adjust the prefetch distance and to pause/resume prefetching when that is likely to provide performance gains. Furthermore, we develop compiler support to automatically apply the optimization without requiring user intervention. We evaluate the optimization across five different distributed-memory systems and four workloads. We observe runtime speed-ups of 0.5 – 377x for the Chapel workloads when compared to not prefetching, and 3 – 215x when compared to implementations written in UPC, OpenSHMEM, and one-sided MPI.

关键词： Productivity distributed processing Runtime parallel programming Prefetching Memory management distributed databases

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Extending the Mochi Methodology to Enable Dynamic HPC Data Services

Extending the Mochi Methodology to Enable Dynamic HPC Data S...

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

作者： Matthieu Dorier Philip Carns Robert Ross Shane Snyder Rob Latham Amal Gueroudji George Amvrosiadis Chuck Cranor Jerome Soumagne Argonne National Laboratory Carnegie Mellon University Intel Corporation

ISBN: (数字)9798350364606

ISBN: (纸本)9798350364613

High-performance computing (HPC) applications and workflows are increasingly making use of custom data services to complement traditional parallel file systems with fast transient data management capabilities tailored to application-specific needs. In the Mochi project we provide methodologies and tools that enable rapid development of custom HPC data services, including a collection of composable software components that can be combined to build complex distributed data services. Our initial version of Mochi targeted data services deployed with static configurations with a fixed number of nodes and minimal fault tolerance. However, there is a growing need for dynamic services that can adapt while running in response to changing workloads and system conditions. In this paper we present our work to extend the Mochi architecture to support the development of dynamic data services. We achieve this by providing new Mochi components that support unified bootstrapping and online reconfiguration, fault detection, monitoring, and consensus. We also provide a methodology for deriving service-wide resilience from the resilience of each of the service's components.

关键词： Productivity High performance computing distributed databases Authentication Software Encryption Security

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Sparse Matrix-Vector Multiplication Cache Performance Evaluation and Design Exploration 29

Sparse Matrix-Vector Multiplication Cache Performance Evalua...

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29th international symposium on the Modeling, Analysis, and Simulation of Computer and Telecommunication systems (MASCOTS)

作者： Cui, Jianfeng Lu, Kai Liu, Sheng Natl Univ Def Technol Sch Comp Changsha 410073 Hunan Peoples R China Natl Univ Def Technol Sci & Technol Parallel & Distributed Proc Lab Changsha 410073 Hunan Peoples R China

ISBN: (纸本)9781665458382

In this paper, we conducted a group of evaluations on the SpMV kernel with sequential implementation to investigate cache performance on single-core platforms. We verified a similar pattern inside a suite of sparse matrices covering various domains, which makes cache hit rate extraordinary inspiring in a sequential environment. This implicit regularity drove us to propose a cache space splitting approach, aiming at a better locality in dense vector accessing and utilization of large cache capacity in modern processors. Finally, we explored the design space of cache on Matrix 3000 GPDSP and proposed a group of cache parameters, based on our experimental results.

关键词： SpMV cache sparse matrix PIN simulation

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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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A distributed Storage System for System Logs Based on Hybrid Compression Scheme 21

A Distributed Storage System for System Logs Based on Hybrid...

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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

作者： Chang, Baoming Zhou, Fengxi Wang, Zhaoyang Wen, Yu Zhang, Boyang Institute of Information Engineering Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences School of Cyber Security Beijing China

ISBN: (纸本)9798350329223

Modern enterprises are facing a massive threat from Advanced Persistent Threats (APTs), which have risen to be one of the most dangerous challenges in recent years. Since system logs capture the complex causality dependencies between system entities, they have become the primary data source for countering APTs. However, as modern computer systems get more complicated, system logs can pile up in large quantities. Besides, APTs are sophisticated and persistent cyber attacks that can remain hidden in the target for a long time and constantly steal private data. System logs need to be collected and stored for a long duration to enable a complete analysis of APTs. Such a vast amount of log data is challenging for enterprises to store and manage. There are two mainstream solutions for reducing storage overhead. Data compression methods provide an intuitive idea. However, they are designed for general text and lack optimization for system logs. Another solution considers log reduction, which removes redundant system events recorded in system logs by predefined rules. Unfortunately, they are tailored for specific kinds of redundant information, resulting in limited applicability. Realizing that these two solutions adopt two distinct perspectives to reduce storage overhead, they are complementary. Data compression methods shrink the size of log data from their binary form. Log reduction starts from the semantic information of system logs and removes redundant information to reduce storage overhead. Combining both methods maximizes storage efficiency. In this paper, we propose a distributed storage system based on a hybrid compression scheme. To address the above deficiencies, we first identify and merge redundant system events by analyzing and tracing the information flow rather than based on rules. Then, we apply log parsing to preprocess log entries for further storage efficiency. Besides, we design a distributed architecture to optimize compression and eliminate repeated

关键词： Digital storage

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NetDS: distributed Search Framework with Hybrid Acceleration Methods

NetDS: Distributed Search Framework with Hybrid Acceleration...

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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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