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共 4889 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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parallel Data Stream Anonymization Methods: A Review 2

Parallel Data Stream Anonymization Methods: A Review

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2nd international Conference on Artificial Intelligence and Smart Energy, ICAIS 2022

作者： Trivedi, Brinit Degadwala, Sheshang Vyas, Dhairya Sigma Institute of Engineering Dept. of Comp. Eng Gujarat Vadodara India Shree Drashti Infotech Llp Gujarat Vadodara India

ISBN: (纸本)9781665400527

Sustainable stream processing algorithms have gained popularity in recent years. Flow control is a way of searching and modifying real-time data streams. Missing values are ubiquitous in real-world data streams, making data stream privacy challenging to safeguard. On the other hand, most privacy preservation methods need not take absent values into account when developed. They can anonymize data in certain study, however this results in data loss. This research proposes a unique parallel distributed approach for protecting privacy while using incomplete data streams. This method uses a production computational system to continually anonymize data streams, using clustering to construct each tuple. It clusters data in partial and complete forms using variable and array dimensions as similarity metrics. In order to prevent values and outliers' pollution, a generalization approach that is based on more than matches is used. The experiments used real data to compare current systems with varied settings. This research will cover several anonymization mechanisms and their advantages. There are also drawbacks. Finally, the future of continuous data anonymization research has been explored. © 2022 IEEE.

关键词： Measurement Data privacy Privacy Pollution distributed databases Production Real-time systems

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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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