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Knowledge-aware Graph Attention Network with distributed & Cross Learning for Collaborative Recommendation

Knowledge-aware Graph Attention Network with Distributed & C...

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IEEE international conference on Big Data and Cloud Computing (BdCloud)

作者： Yang Dai Shunmei Meng Qiyan Liu Xiao Liu School of Cyberspace Security NanJing University of Science and Technology Nanjing China State Key Lab. for Novel Software Technology NanJing University Nanjing China University of Toronto Toronto Canada

Nowadays, side-information is widely used to rein-force the user-item interaction and helps to handle the sparsity issue and cold start problem of conventional recommendation algorithms. Due to the overlook of the relationship between items and entities and the higher-order connectivity information, most existing approaches are hard to get users' deep preferences. In this paper, we propose KGANCL, a Knowledge-aware Graph Attention Network with distributed & Cross Learning. It focuses on using different forms of the knowledge graph to strengthen both users' and items' embedding representations, respectively. Firstly, Graph Attention Network is adopted for user embedding learning, which can give different importance scores to different neighbors, and a user-item KG graph is used to integrate adjacent information to enhance the representation. Secondly, a cross module is used for item embedding learning, which shares the high-order interaction between the recommender system and the knowledge graph. We also use the idea of distributed processing for embeddings in different entities to improve the learning efficiency. Experimental results demonstrate that KGANCL can provide better recommendations compared with the state-of-the-art baseline models. Our model can also maintain superior prediction accuracy even in little-known interaction scenarios.

关键词： Adaptation models distributed processing Computational modeling Scalability Collaboration Knowledge graphs Computer architecture

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PTangle: A parallel Detector for Unverified Blockchain Transactions 20th

PTangle: A Parallel Detector for Unverified Blockchain Trans...

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20th international conference on Algorithms and Architectures for parallel processing (ICA3PP)

作者： Victor, Ashish Christopher Jayanthi, Akhilarka Gopalakrishnan, Atul Anand Nagpal, Rahul PES Univ Dept Comp Sci & Engn Bangalore Karnataka India

ISBN: (纸本)9783030602482;9783030602475

Tangle is a novel directed acyclic graph (DAG)-based distributed ledger preferred over traditional linear ledgers in blockchain applications because of better transaction throughput. Earlier techniques have mostly focused on comparing the performance of graph chains over linear chains and incorporating the Markov Chain Monte Carlo process in probabilistic traversals to detect unverified transactions in DAG chains. In this paper, we present a parallel detection method for unverified transactions. Experimental evaluation of the proposed parallel technique demonstrates a significant, scalable average speed-up of close to 70%, and a peak speed-up of approximately 73% for a large number of transactions.

关键词： Blockchain DAG Tangle Markov Chain Monte Carlo parallelism

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Some Novelties in Map Reducing techniques to Retrieve and Analyze Big Data for Effective processing 4th

Some Novelties in Map Reducing Techniques to Retrieve and An...

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4th international conference on Information and Communication Technology for Intelligent Systems, ICTIS 2020

作者： Bhat, Prashant Hegde, Prajna School of Computational Sciences and IT Garden City University Bangalore India

ISBN: (纸本)9789811570612

As we are living in the era of digital data, it has become necessary to make use of it in intelligent ways. The data generation is not only increasing, but the rate with which data generates is also increasing. Huge data obtained from many sources can be processed and analyzed so as to get useful information. But, the problem is with volume of data, velocity with which data increases, and also different variety and complex structure of data. Storing such large amount of data and the process of retrieving huge data when required are time consuming. One of the solutions for effective processing of Big Data is parallel processing. The software solutions like Hadoop provides way to store and also to implement parallel processing of Big Data. In most of the situations, Big Data cannot be stored in a single system. distributed File System that can run on different clusters can be used to process Big Data. By using MapReduce model, large dataset can be computed on commodity hardware clusters. This paper presents a novel work of implementing MapReduce technique to analyze and retrieve data. An attempt is made to retrieve data by adopting MapReduce technique. A task is divided into number of sub-tasks, and these sub-tasks can be processed simultaneously by different processors or number of commodity hardware. A novel and effective way of implementing MapReduce is represented in this paper. In other words, this work examines the method and the outcome of MapReduce technique, which is a solution to the problem of processing huge amount of data. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： MapReduce

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Intermediate Value Size Aware Coded MapReduce 26

Intermediate Value Size Aware Coded MapReduce

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26th IEEE international conference on parallel and distributed Systems (IEEE ICPADS)

作者： Dong, Yamei Tang, Bin Ye, Baoliu Qu, Zhihao Lu, Sanglu Nanjing Univ Natl Key Lab Novel Software Technol Nanjing 210023 Peoples R China Hohai Univ Sch Comp & Informat Nanjing 211100 Peoples R China

ISBN: (纸本)9781728190747

MapReduce is a commonly used framework for parallel processing of data-intensive tasks, but its performance usually suffers from heavy communication load incurred by the shuffling of intermediate values (IVs) among computing servers. Recently, the Coded MapReduce framework is proposed which uses a coding scheme named coded distributed computing (CDC) to trade the communication load with extra computation in MapReduce. CDC can achieve the optimal computation-communication tradeoff when all the IVs have the same size. However, in many practical applications, the sizes of IVs can vary over a large range, leading to inferior performance. In this paper, we introduce a generalized CDC scheme which takes the sizes of IVs into account and then propose a combinatorial optimization problem aiming to minimize the communication load when the computation load is fixed. We show that the problem is NP-hard, and further propose a very efficient algorithm which achieves an approximation ratio of 2. Experiments conducted on Alibaba Cloud show that, compared to the original CDC scheme, our proposed IV size aware approach can significantly reduce the communication load and achieve a lower total execution time.

关键词： MapReduce coded distributed computing intermediate values communication load

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MLCNN: Cross-Layer Cooperative Optimization and Accelerator Architecture for Speeding Up Deep Learning applications

MLCNN: Cross-Layer Cooperative Optimization and Accelerator ...

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international Symposium on parallel and distributed processing (IPDPS)

作者： Beilei Jiang Xianwei Cheng Sihai Tang Xu Ma Zhaochen Gu Song Fu Qing Yang Mingxiong Liu University of North Texas Los Alamos National Laboratory

The ever-increasing number of layers, millions of parameters, and large data volume make deep learning workloads resource-intensive and power-hungry. In this paper, we develop a convolutional neural network (CNN) acceleration framework, named MLCNN, which explores algorithm-hardware co-design to achieve cross-layer cooperative optimization and acceleration. MLCNN dramatically reduces computation and on-off chip communication, improving CNN's performance. To achieve this, MLCNN reorders the position of nonlinear activation layers and pooling layers, which we prove results in a negligible accuracy loss; then the convolutional layer and pooling layer are co-optimized by means of redundant multiplication elimination, local addition reuse, and global addition reuse. To the best of our knowledge, MLCNN is the first of its kind that incorporates cooperative optimization across convolutional, activation, and pooling layers. We further customize the MLCNN accelerator to take full advantage of cross-layer CNN optimization to reduce both computation and on-off chip communication. Our analysis shows that MLCNN can significantly reduce (up to 98%) multiplications and additions. We have implemented a prototype of MLCNN and evaluated its performance on several widely used CNN models using both an accelerator-level cycle and energy model and RTL implementation. Experimental results show that MLCNN achieves 3.2x speedup and 2.9x energy efficiency compared with dense CNNs. MLCNN's optimization methods are orthogonal to other CNN acceleration techniques, such as quantization and pruning. Combined with quantization, our quantized MLCNN gains a 12.8x speedup and 11.3x energy efficiency compared with DCNN.

关键词： Deep learning Quantization (signal) Recurrent neural networks Power demand Statistical analysis Computational modeling Optimization methods

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G-Morph: Induced Subgraph Isomorphism Search of Labeled Graphs on a GPU 27th

G-Morph: Induced Subgraph Isomorphism Search of Labeled Grap...

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27th international European conference on parallel and distributed Computing (Euro-Par)

作者： Rowe, Bryan Gupta, Rajiv Univ Calif Riverside CSE Dept Riverside CA 92521 USA

ISBN: (纸本)9783030856656;9783030856649

Induced subgraph isomorphism search finds the occurrences of embedded subgraphs within a single large data graph that are strictly isomorphic to a given query graph. Labeled graphs contain object types and are a primary input source to this core search problem, which applies to systems like graph databases for answering queries. In recent years, researchers have employed GPU parallel solutions to this problem to help accelerate runtimes by utilizing the filtering-and-joining framework, which first filters vertices that cannot be part of the solution then joins partial solutions with candidate edges until full isomorphisms are determined. However, the performance of current GPU-based solutions is hindered by limited filtering effectiveness and presence of extraneous computations. This paper presents G-Morph, a fast GPU-based induced subgraph isomorphism search system for labeled graphs. Our filtering-and-joining system parallelizes both phases by upfront eliminating irrelevant vertices via a novel space-efficient vertex signature hashing strategy and efficiently joining partial solutions through use of a novel sliding window algorithm (slide-join) that provides overflow protection for larger input. Together these techniques greatly reduce extraneous computations by reducing Cartesian products and improving edge verification while supporting large scan operations (split-scan). G-Morph outperforms the state-of-the-art GPU-based GSI and CPU-based VF3 systems on labeled real-world graphs achieving speedups of up to 15.78x and 43.56x respectively.

关键词： Graph databases Subgraph isomorphism Breadth-first search GPU programming parallel computing

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PARMA-CC: parallel Multiphase Approximate Cluster Combining 20

PARMA-CC: Parallel Multiphase Approximate Cluster Combining

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21st international conference on distributed Computing and Networking (ICDCN)

作者： Keramatian, Amir Gulisano, Vincenzo Papatriantafilou, Marina Tsigas, Philippas Chalmers Univ Technol Gothenburg Sweden

ISBN: (纸本)9781450377515

Clustering is a common component in data analysis applications. Despite the extensive literature, the continuously increasing volumes of data produced by sensors (e.g. rates of several MB/s by 3D scanners such as LIDAR sensors), and the time-sensitivity of the applications leveraging the clustering outcomes (e.g. detecting critical situations, that are known to be accuracy-dependent), demand for novel approaches that respond faster while coping with large data sets. The latter is the challenge we address in this paper. We propose an algorithm, PARMA-CC, that complements existing density-based and distance-based clustering methods. PARMA-CC, is based on approximate, data parallel cluster combining, where parallel threads can compute summaries of clusters of data (sub)sets and, through combining, together construct a comprehensive summary of the sets of clusters. By approximating clusters with their respective geometrical summaries, our technique scales well with increased data volumes, and, by computing and efficiently combining the summaries in parallel, it enables latency improvements. PARMA-CC combines the summaries using special data structures that enable parallelism through in-place data processing. As we show in our analysis and evaluation, PARMA-CC can complement and outperform well-established methods, with significantly better scalability, while still providing highly accurate results in a variety of data sets, even with skewed data distributions, which cause the traditional approaches to exhibit their worst-case behaviour. In the paper we also describe how PARMA-CC can facilitate time-critical applications through appropriate use of the summaries.

关键词： approximate parallel clustering cluster combining model fitting

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distributed Threat Intelligence at the Edge Devices: A Large Language Model-Driven Approach

Distributed Threat Intelligence at the Edge Devices: A Large...

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IEEE Annual international Computer Software and applications conference (COMPSAC)

作者： Syed Mhamudul Hasan Alaa M. Alotaibi Sajedul Talukder Abdur R. Shahid School of Computing Southern Illinois University Carbondale IL USA Secure and Trustworthy Intelligent Systems (SHIELD) Lab Center for Research and Education in AI and Cybersecurity (CARE-AI-C)

ISBN: (数字)9798350376968

ISBN: (纸本)9798350376975

With the proliferation of edge devices, there is a significant increase in attack surface on these devices. The decen-tralized deployment of threat intelligence on edge devices, coupled with adaptive machine learning techniques such as the in-context learning feature of Large Language Models (LLMs), represents a promising paradigm for enhancing cybersecurity on resource-constrained edge devices. This approach involves the deployment of lightweight machine learning models directly onto edge devices to analyze local data streams, such as network traffic and system logs, in real-time. Additionally, distributing computational tasks to an edge server reduces latency and improves responsiveness while also enhancing privacy by processing sensitive data locally. LLM servers can enable these edge servers to autonomously adapt to evolving threats and attack patterns, continuously updating their models to improve detection accuracy and reduce false positives. Furthermore, collaborative learning mechanisms facilitate peer-to-peer secure and trustworthy knowledge sharing among edge devices, enhancing the collective intelligence of the network and enabling dynamic threat mitigation measures such as device quarantine in response to detected anomalies. The scalability and flexibility of this approach make it well-suited for diverse and evolving network environments, as edge devices only send suspicious information such as network traffic and system log changes, offering a resilient and efficient solution to combat emerging cyber threats at the network edge. Thus, our proposed framework can improve edge computing security by providing better security in cyber threat detection and mitigation by isolating the edge devices from the network.

关键词： Adaptation models Image edge detection Computational modeling Prevention and mitigation Telecommunication traffic Threat assessment Software

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SUPERVISED MULTI-VIEW distributed HASHING

SUPERVISED MULTI-VIEW DISTRIBUTED HASHING

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IEEE international conference on Image processing (ICIP)

作者： Tang, Yunpeng Shen, Xiaobo Ji, Zexuan Wang, Tao Fu, Peng Sun, Quan-Sen Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing Jiangsu Peoples R China

ISBN: (纸本)9781728163956

Multi-view hashing efficiently integrates multi-view data for learning compact hash codes, and achieves impressive large-scale retrieval performance. In real-world applications, multi-view data are often stored or collected in different locations, where hash code learning is more challenging yet less studied. To fulfill this gap, this paper proposes a novel supervised multi-view distributed hashing (SMvDisH) for hash code learning from multi-view data in a distributed manner. SMvDisH yields the discriminative latent hash codes by joint learning of latent factor model and classifier. With local consistency assumption among neighbor nodes, the distributed learning problem is divided into a set of decentralized subproblems. The subproblems can be solved in parallel, and the computational and communication costs are low. Experimental results on three large-scale image datasets demonstrate that SMvDisH achieves competitive retrieval performance and trains faster than state-of-the-art multi-view hashing methods.

关键词： Hashing distributed learning multi-view data

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Optimization of Ray-Tracing Algorithm for Simulation of PMD Sensors 2nd

Optimization of Ray-Tracing Algorithm for Simulation of PMD ...

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2nd international conference on Machine Learning and Information processing, ICMLIP 2020

作者： Lade, Sangita Kulkarni, Purva Saraf, Prasad Nartam, Purva Patil, Aniket Vishwakarma Institute of Technology Pune India IFM Engineering Private Ltd Pune India

ISBN: (纸本)9789813348585

PMD sensors are based on time of flight (TOF) technology which deliver speedy images with depth data to suit real-time applications. Simulation of ray tracing for real-time image generation is an important step for testing photonic mixer devices (PMD) sensors. These ray-tracing applications for image simulation are embarrassingly parallel in nature. Hence, to speed up this task, GPUs embody the perfect hardware architecture required. This paper proposes various optimization techniques for a ray-tracing algorithm using NVIDIA CUDA-enabled GPUs. The ray-tracing tasks are subjected to per pixel of the PMD sensor. Even simple parallel algorithms of ray tracing have considerable scope of optimization with respect to memory, instructions, control flow and concurrency. In this paper, various optimization approaches are proposed which are generic across all CUDA GPUs along with the merits and demerits. The optimizations are carried out for ray-tracing algorithm for simulation of PMD sensors using NVIDIA GeForce 840 M GPU of the Maxwell architecture family. The speed-up obtained from optimized parallel algorithm is 2.29 times of its serial algorithm and is 1.048 times of its parallel algorithm. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Graphics processing unit

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