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RIMR: Reverse Influence Maximization Rank

RIMR: Reverse Influence Maximization Rank

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1st international conference on Smart Energy systems and Artificial Intelligence (SESAI)

作者： Vap, Jay Kogge, Peter Univ Notre Dame Dept Comp Sci & Engn Notre Dame IN 46556 USA

ISBN: (纸本)9798350364613;9798350364606

The ubiquitous usage of social media and other internet platforms has led to the widespread proliferation of naturally occurring social networks. This has caused a revolution in marketing and information dissemination due to the rise of influencers, and their innate ability to broadcast information to large audiences. Influence maximization, a key problem in identifying optimal influential individuals, can be used to identify influencers in a social network, or can be superimposed on other settings which exhibit relational information. Popular methods, such as greedy algorithms, face scalability issues, performing poorly on larger networks, and incurring a large memory overhead. To address these challenges, this paper introduces a novel methodology, RIMR, which leverages statistical modeling and parallel programming to circumvent the need for storing large collections of data, achieving scalability and efficiency. Experimental results demonstrate the effectiveness and scalability of the proposed approach compared to state-of-the-art methods, showcasing significant speedups. This paper provides insights into the development of the proposed statistical process, the heterogeneous algorithm implementation, and a prototype demonstrating its efficacy.

关键词： GPUs Heterogeneous Architectures Influence Maximization parallel Algorithms

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Proceedings - 2024 IEEE international conference on Joint Cloud Computing, JCC 2024

Proceedings - 2024 IEEE International Conference on Joint Cl...

引用

15th IEEE international conference on Joint Cloud Computing, JCC 2024

ISBN: (纸本)9798350387339

The proceedings contain 13 papers. The topics discussed include: a study on the performance of distributed storage systems in edge computing environments;RESCAPE: a resource estimation system for microservices with graph neural network and profile engine;PrometheusMigrate: efficient live migration of confidential virtual machine with software abstraction;the cost perspective of adopting large language model-as-a-service;DCSA: the deployment mechanism of chained serverless applications in JointCloud environment;parallel computation in dynamic fog computing networks: a multi-armed bandit learning-based decentralized matching approach;and IBRI: an IoT solution for building collapse risk identification in smart cities.

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Improved distributed Algorithms for Random Colorings 27

Improved Distributed Algorithms for Random Colorings

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27th international conference on Principles of distributed systems, OPODIS 2023

作者： Carlson, Charlie Frishberg, Daniel Vigoda, Eric Department of Computer Science University of California Santa BarbaraCA United States Department of Computer Science and Software Engineering California Polytechnic State University San Luis ObispoCA United States

ISBN: (纸本)9783959773089

Markov Chain Monte Carlo (MCMC) algorithms are a widely-used algorithmic tool for sampling from high-dimensional distributions, a notable example is the equilibirum distribution of graphical models. The Glauber dynamics, also known as the Gibbs sampler, is the simplest example of an MCMC algorithm;the transitions of the chain update the configuration at a randomly chosen coordinate at each step. Several works have studied distributed versions of the Glauber dynamics and we extend these efforts to a more general family of Markov chains. An important combinatorial problem in the study of MCMC algorithms is random colorings. Given a graph G of maximum degree ∆ and an integer k ≥ ∆ + 1, the goal is to generate a random proper vertex k-coloring of G. Jerrum (1995) proved that the Glauber dynamics has O(n log n) mixing time when k > 2∆. Fischer and Ghaffari (2018), and independently Feng, Hayes, and Yin (2018), presented a parallel and distributed version of the Glauber dynamics which converges in O(log n) rounds for k > (2 + Ε)∆ for any Ε > 0. We improve this result to k > (11/6 − δ)∆ for a fixed δ > 0. This matches the state of the art for randomly sampling colorings of general graphs in the sequential setting. Whereas previous works focused on distributed variants of the Glauber dynamics, our work presents a parallel and distributed version of the more general flip dynamics presented by Vigoda (2000) (and refined by Chen, Delcourt, Moitra, Perarnau, and Postle (2019)), which recolors local maximal two-colored components in each step. © Charlie Carlson, Daniel Frishberg, and Eric Vigoda

关键词： Coloring

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GIM (Ghost In the Machine): A Coarse -Grained Reconfigurable Compute -In -Memory Platform for Exploring Machine -Learning Architectures

GIM (Ghost In the Machine): A Coarse -Grained Reconfigurable...

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1st international conference on Smart Energy systems and Artificial Intelligence (SESAI)

作者： Borowicz, Maya Ding, James Fan, Winnie Gao, Zhongqi Jackson, Davis Lu, Ares Rohlfsen, Sophia Simar, Ray Rice Univ Houston TX 77005 USA

ISBN: (纸本)9798350364613;9798350364606

Machine-learning (ML) algorithms are finding wide adoption across a rich spectrum of application domains with diverse requirements in terms of performance, power, and cost. These diverse requirements are making it necessary to explore a large space of ML architectures and reexamine fundamental computational structures, a process of exploration that is very expensive. To get around the costly computations associated with large data sets and long training times, there have been increasing investments in specialized fixed-function hardware. However, this specialized hardware is expensive and hard to generalize to address the spectrum of applications. For our experiments, we focus on a novel, highly parallel, superset ML architecture, and use it to test the capabilities of new coarse-grained FPGAs containing hundreds and thousands of DSP slices with dedicated local storage. These new coarse-grained architectures allow us to achieve ASIC-like clock rate and reductions in power while exploring novel and common ML architectures.

关键词： AMD architecture back propagation coarse grained compute in memory DSP FPGA machine learning neural network parallel processing Rice University RISC V Simulink systolic UltraScale Zync

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Exploring Mapping Strategies for Co-allocated HPC Applications

Exploring Mapping Strategies for Co-allocated HPC Applicatio...

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29th international conference on parallel and distributed Computing (Euro-Par)

作者： Vardar, Ioannis Hunold, Sascha Swartvagher, Philippe Traff, Jesper Larsson TU Wien A-1040 Vienna Austria

ISBN: (纸本)9783031488023;9783031488030

In modern HPC systems with deep hierarchical architectures, large-scale applications often struggle to efficiently utilize the abundant cores due to the saturation of resources such as memory. Co-allocating multiple applications to share compute nodes can mitigate these issues and increase system throughput. However, co-allocation may harm the performance of individual applications due to resource contention. Past research suggests that topology-aware mappings can improve the performance of parallel applications that do not share resources. In this work, we implement application-oblivious, topology-aware process-to-core mappings via different core enumerations that support the co-allocation of parallel applications. We show that these mappings have a significant impact on the available memory bandwidth. We explore how these process-to-core mappings can affect the individual application duration as well as the makespan of job schedules when they are combined with co-allocation. Our main objective is to assess whether co-allocation with a topology-aware mapping can be a viable alternative to the exclusive node allocation policies that are currently common in HPC clusters.

关键词： High Performance Computing parallel Computing Performance Optimization Process Mapping Co-allocation

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24th international conference on Runtime Verification, RV 2024

24th International Conference on Runtime Verification, RV 20...

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24th international conference on Runtime Verification, RV 2024

ISBN: (纸本)9783031742330

The proceedings contain 18 papers. The special focus in this conference is on Runtime Verification. The topics include: Dynamic, Multi-objective Specification and Falsification of Autonomous CPS;Oblivious Monitoring for Discrete-Time STL via Fully Homomorphic Encryption;sampling-Based and Gradient-Based Efficient Scenario Generation;hyperPart-X: Probabilistic Guarantees for Parameter Mining of Signal Temporal Logic Formulas in Cyber-Physical systems;faRM-LTL: A Domain-Specific Architecture for Flexible and Accelerated Runtime Monitoring of LTL Properties;efficient Offline Monitoring for Dynamic Metric Temporal Logic;timelyMon: A Streaming parallel First-Order Monitor;adding State to Stream Runtime Verification;the Complexity of Data-Free Nfer;RTLolaMo3Vis - A Mobile and Modular Visualization Framework for Online Monitoring;case Study: Runtime Safety Verification of Neural Network Controlled System;gaussian-Based and Outside-the-Box Runtime Monitoring Join Forces;Box-Based Monitor Approach for Out-of-Distribution Detection in YOLO: An Exploratory Study;distributed Monitoring of Timed Properties;towards Efficient Runtime Verified Linearizable Algorithms;approximate distributed Monitoring Under Partial Synchrony: Balancing Speed & Accuracy.

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Improvement of Task Scheduling with Energy Efficiency including Fault Tolerant with Resilient Computing System in parallel and distributed Communication Network 2

Improvement of Task Scheduling with Energy Efficiency includ...

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2nd international conference on Augmented Intelligence and Sustainable systems, ICAISS 2023

作者： Kumar, Vijay Madhuri, P. Yamini, R. Sravani, Palla Muthukumar, K. Graphic Era Hill University Department of Physics Dehradun India Hyderabad501401 India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computing Technologies Tamil Nadu Kattankulathur603203 India Raghu Engineering College Department of Computer Science and Engineering Andhra Pradesh Visakhapatnam531162 India Sri Krishna College of Engineering and Technology Department of Electrical and Electronics Engineering Tamilnadu Coimbatore India

ISBN: (纸本)9798350325799

In parallel and distributed communication networks, task scheduling is essential for attaining the best system performance. Innovative ways that may intelligently distribute computing resources while minimizing energy consumption and maintaining system resilience are required due to the rising need for energy efficiency and fault tolerance. This system, offers a unique approach to job scheduling in parallel and distributed communication networks that integrate genetic algorithms (GA) and machine learning (ML).Using ML approaches, proposed method analyses historical task execution data to discover patterns that may be applied to task execution time prediction in various computing systems. As a result, this can assign tasks to resources effectively, maximize resource usage, and cut down on execution time as a whole. In addition, using predefined objectives like energy efficiency and fault tolerance, GA is used to find the best task-resource assignments. This applies resilient computing approaches to the job scheduling procedure to overcome problems with fault tolerance. This prevents the execution of crucial operations from being hampered by system faults or resource shortages by utilizing fault detection and recovery technologies. This system can adapt dynamically to shifting network conditions through the combination of ML, GA, and resilient computing, which raises the entire system's reliability and availability. © 2023 IEEE.

关键词： Machine learning

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What Appears Suboptimal May Surprise You: A Fixed-Rate Scheduling Policy for Geo-distributed CoFlows 29

What Appears Suboptimal May Surprise You: A Fixed-Rate Sched...

引用

29th IEEE international conference on parallel and distributed systems, ICPADS 2023

作者： Liu, Haoyu Ding, Feiyan Wang, Yao Xiang, Qiao Shu, Jiwu Du, Haizhou Kong, Linghe Liu, Xue Xiamen University School of Informatics Xiamen China University of Electric Power of Shanghai School of Computer Science and Technology Shanghai China Shanghai Jiao Tong University Department of Computer Science and Engineering Shanghai China McGill University School of Computer Science Montreal Canada

ISBN: (纸本)9798350330717

All existing coflow scheduling algorithms compute dynamic-rate schedules that change the rates of flows during transmission. In this paper, we make a crucial finding: although dynamically adjusting the rates of flows could lead to a better coflow completion time (CCT) in theory, it would introduce additional pressures on the congestion control mechanism in the underlying network, which result in poor CCT in practice. This difference between theoretical CCT and practical CCT is further exacerbated in wide-area networks, where the topology does not provide any bisection guarantee as data center networks do. To this end, we designed a fixed-rate coflow scheduling policy called FSCO. Although in theory, the best fixed-rate schedule is usually suboptimal, it keeps the in-flight traffic relatively steady, reducing the risk of triggering congestion control. The core of FSCO is an efficient scheduling algorithm based on the classic network utilization maximization (NUM) framework. We implement a prototype of FSCO and evaluate its performance extensively using real-world topologies and coflow traces. Experimental results show that the total CCT reduces up to 30% compared to baselines while yielding up to 12× speedups compared to the solver. © 2023 IEEE.

关键词： Coflow Scheduling Dual decomposition Network Utility Maximization

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Neuron Pruning-Based Federated Learning for Communication-Efficient distributed Training 1

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23rd international conference on Algorithms and Architectures for parallel Processing (ICA3PP)

作者： Guan, Jianfeng Wang, Pengcheng Yao, Su Zhang, Jing Beijing Univ Posts & Telecommun Sch Comp Sci Natl Pilot Software Engn Sch Beijing 100876 Peoples R China Beijing Univ Posts & Telecommun Key Lab Networking & Switching Technol Beijing 100876 Peoples R China Tsinghua Univ Dept Comp Sci & Technol Beijing 100084 Peoples R China

ISBN: (数字)9789819708598

ISBN: (纸本)9789819708581;9789819708598

Efficient and flexible cloud computing is widely used in distributed systems. However, in the Internet of Things (IoT) environment with heterogeneous capabilities, the performance of cloud computing may decline due to limited communication resources. As located closer to the end, edge computing is used to replace cloud computing to provide timely and stable services. To accomplish distributed system and privacy preserving, Federated Learning (FL) has been combined with edge computing. However, due to the large number of clients, the amount of data transmitted will also grow exponentially. How to reduce the communication overhead in FL is still a big problem. As a major method to reduce the communication overhead, compressing the transmission parameters can effectively reduce the communication overhead. However, the existing methods do not consider the possible internal relationship between neurons. In this paper, we propose Neuron Pruning-Based FL for communication-efficient distributed training, which is a model pruning method to compress model parameters transmitted in FL. In contrast to the previous methods, we use dimensionality reduction method as the importance factor of neurons, and take advantage of the correlation between them to carry out model pruning. Our analysis results show that NPBFL can reduce communication overhead while maintaining classification accuracy.

关键词： Edge Computing Federated Learning Privacy Preserving Neuron Pruning Internet of Things Traffic Identification

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A Frequency-aware Grouping Strategy for Stateful Operators in distributed Stream Processing systems 29

A Frequency-aware Grouping Strategy for Stateful Operators i...

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29th IEEE international conference on parallel and distributed systems, ICPADS 2023

作者： Sun, Dawei Chen, Zhe Lv, Weilong Gao, Shang Rong, Jia China University of Geosciences School of Information Engineering Beijing100083 China Deakin University School of Information Technology Waum PondsVIC3216 Australia Monash University Department of Data Science & AI ClaytonVIC3800 Australia

ISBN: (纸本)9798350330717

Current optimization for stateful flow processing computation tends to focus on load balancing without considering the utilization of downstream instance resources. To address this issue, we propose a data stream grouping method called Fa-Stream, specifically designed for stateful operators and incorporating field values frequency-awareness. Fa-Stream is implemented in three main aspects: (1) A data stream grouping model is built using Count-Min Sketch and Gated Recurrent Unit (GRU) to predict and analyze the frequency of field values. It selectively chooses high-frequency field values, and the communication distance model and instance resource constraint model are designed to adjust the weights of downstream instances for high-frequency field values. (2) A cyclic access routing table is generated, and weights are dynamically adjusted by a rebalancing scheme to avoid load skewness. Consistent hash grouping is implemented for low-frequency field values, and dual mapping is used to prevent large-scale migration caused by scaling. To validate the effectiveness of Fa-Stream, comparative experiments between Partial Key Grouping (PKG) and Fa-Stream are conducted using the Storm platform. Results demonstrate that Fa-Stream improves tuple throughput by 12.3%, reduces system delay by 14.2%, and increases load balancing degree by 42.8%. Furthermore, fa-Stream exhibits efficiency and stability across different data skews and tuple input rates. © 2023 IEEE.

关键词： Big Data Data Stream Grouping Load Balance Stateful Operator Stream computing

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