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parallel Vertex Cover Algorithms on GPUs 36

Parallel Vertex Cover Algorithms on GPUs

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36th ieee international parallel and distributed processing symposium (ieee IPDPS)

作者： Yamout, Peter Barada, Karim Jaljuli, Adnan Mouawad, Amer E. El Hajj, Izzat Amer Univ Beirut Beirut Lebanon

ISBN: (纸本)9781665481069

Finding small vertex covers in a graph has applications in numerous domains such as scheduling, computational biology, telecommunication networks, artificial intelligence, social science, and many more. Two common formulations of the problem include: Minimum Vertex Cover (MVC), which finds the smallest vertex cover in a graph, and Parameterized Vertex Cover (PVC), which finds a vertex cover whose size is less than or equal to some parameter k. Algorithms for both formulations involve traversing a search tree, which grows exponentially with the size of the graph or the value of k. parallelizing the traversal of the vertex cover search tree on GPUs is challenging for multiple reasons. First, the search tree is a narrow binary tree which makes it difficult to extract enough sub-trees to process in parallel to fully utilize the GPU's massively parallel execution resources. Second, the search tree is highly imbalanced which makes load balancing across a massive number of parallel GPU workers especially challenging. Third, keeping around all the intermediate state needed to traverse many sub-trees in parallel puts high pressure on the GPU's memory resources and may act as a limiting factor to parallelism. To address these challenges, we propose an approach to traverse the vertex cover search tree in parallel using GPUs while handling dynamic load balancing. Each thread block traverses a different sub-tree using a local stack, however, we use a global worklist to balance the load to ensure that all blocks remain busy. Blocks contribute branches of their sub-trees to the global worklist on an as-needed basis, while blocks that finish their subtrees pick up new ones from the global worklist. We use degree arrays to represent intermediate graphs so that the representation is compact in memory to avoid limiting parallelism, but selfcontained which is necessary for the load balancing process. Our evaluation shows that compared to approaches used in prior work, our hybrid approa

关键词： Limiting Processor scheduling Instruction sets Heuristic algorithms Social sciences Memory management Graphics processing units

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HETEROGENEOUS ARCHITECTURE FOR SPARSE DATA processing 36

HETEROGENEOUS ARCHITECTURE FOR SPARSE DATA PROCESSING

引用

36th ieee international parallel and distributed processing symposium (ieee IPDPS)

作者： Adavally, Shashank Weaver, Alex Vasireddy, Pranathi Kavi, Krishna Mehta, Gayatri Gulur, Nagendra Univ North Texas Denton TX 76203 USA

ISBN: (纸本)9781665497473

Sparse matrices are very common types of information used in scientific and machine learning applications including deep neural networks. Sparse data representations lead to storage efficiencies by avoiding storing zero values. However, sparse representations incur metadata computational overheads - software first needs to find row/column locations of non-zero values before performing necessary computations. Such metadata accesses involve indirect memory accesses (of the form a[b[i] ] where a[.] and b[.] are large arrays) and they are cache and prefetch-unfriendly, resulting in frequent load stalls. In this paper, we will explore a dedicated hardware for a memory-side accelerator called Hardware Helper Thread (HHT) that performs all the necessary index computations to fetch only the nonzero elements from sparse matrix and sparse vector and supply those values to the primary core, creating heterogeneity within a single CPU core. We show both performance gains and energy savings of HHT for sparse matrix-dense vector multiplication (SpMV) and sparse matrixsparse vector multiplication (SpMSpV). The ASIC HHT shows average performance gains ranging between 1.7 and 3.5 depending on the sparsity levels, vector-widths used by RISCV vector instructions and if the Vector (in Matrix-Vector multiplication) is sparse or dense. We also show energy savings of 19% on average when ASIC HHT is used compared to baseline (for SpMV), and the HHT requires 38.9% of a RISCV core area.

关键词： Sparse matrices DNN Hardware Accelerators RISCV

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In-Memory Indexed Caching for distributed Data processing 36

In-Memory Indexed Caching for Distributed Data Processing

引用

36th ieee international parallel and distributed processing symposium (ieee IPDPS)

作者： Uta, Alexandru Ghit, Bogdan Dave, Ankur Rellermeyer, Jan Boncz, Peter Leiden Univ LIACS Leiden Netherlands Databricks San Francisco CA USA Univ Calif Berkeley Berkeley CA USA Delft Univ Technol Delft Netherlands CWI Amsterdam Netherlands

ISBN: (纸本)9781665481069

Powerful abstractions such as dataframes are only as efficient as their underlying runtime system. The de-facto distributed data processing framework, Apache Spark, is poorly suited for the modern cloud-based data-science workloads due to its outdated assumptions: static datasets analyzed using coarse-grained transformations. In this paper, we introduce the Indexed DataFrame, an in-memory cache that supports a dataframe abstraction which incorporates indexing capabilities to support fast lookup and join operations. Moreover, it supports appends with multi-version concurrency control. We implement the Indexed DataFrame as a lightweight, standalone library which can be integrated with minimum effort in existing Spark programs. We analyze the performance of the Indexed DataFrame in cluster and cloud deployments with real-world datasets and benchmarks using both Apache Spark and Databricks Runtime. In our evaluation, we show that the Indexed DataFrame significantly speeds-up query execution when compared to a nonindexed dataframe, incurring modest memory overhead.

关键词： distributed processing Runtime Data analysis Cluster computing Relational databases Libraries Concurrency control

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Sequre: a high-performance framework for rapid development of secure bioinformatics pipelines 36

Sequre: a high-performance framework for rapid development o...

引用

36th ieee international parallel and distributed processing symposium (ieee IPDPS)

作者： Smajlovic, Haris Shajii, Ariya Berger, Bonnie Cho, Hyunghoon Numanagic, Ibrahim Univ Victoria Victoria BC Canada MIT Cambridge MA 02139 USA Broad Inst MIT & Harvard Cambridge MA 02142 USA

ISBN: (纸本)9781665497473

Genomic data leaks are irreversible. Leaked DNA cannot be changed, stays disclosed indefinitely, and affects the owner's family members as well. The recent large-scale genomic data collections [1], [2] render the traditional privacy protection mechanisms, like the Health Insurance Portability and Accountability Act (HIPAA), inadequate for protection against the novel security attacks [3]. On the other hand, data access restrictions hinder important clinical research that requires large datasets to operate [4]. These concerns can be naturally addressed by the employment of privacy-enhancing technologies, such as a secure multiparty computation (MPC) [5]–[10]. Secure MPC enables computation on data without disclosing the data itself by dividing the data and computation between multiple computing parties in a distributed manner to prevent individual computing parties from accessing raw data. MPC systems are being increasingly adopted in fields that operate on sensitive datasets [11]–[13], such as computational genomics and biomedical research [14]–[22].

关键词： distributed processing Data privacy Pipelines Employment distributed databases Genomics Insurance

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parallel and Batch Multiple Replica Auditing Protocol for Edge Computing 21

Parallel and Batch Multiple Replica Auditing Protocol for Ed...

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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： Li, Yi Zheng, Wenying Pandi, Vijayakumar Bhuiyan, Md Zakirul Alam Thamilarasi, C. Nanjing University of Information Science and Technology School of Computer Science Nanjing China Zhejiang Sci-Tech University School of Computer Science and Technology Hangzhou China University College of Engineering Tindivanam Anna University Chennai Department of Computer Science and Engineering Melpakkam India Fordham University Department of Computer and Information Sciences BronxNY United States P.S.V College of Enfgineering and Technoogy Department of Electronics and Communication Engineering Krishnagiri India

ISBN: (纸本)9798350329223

The edge computing architecture exposes data transmission and storage to unauthorized access, compromising data integrity. Users typically employ multiple data backups to ensure data reliability and availability, enhancing data redundancy and recovery capabilities. Nevertheless, verifying the integrity of multiple backup data under edge computing entails computational and communication overhead. In response, we present a parallel and batch multiple replica auditing protocol tailored for edge computing. Contributions include an efficient multiple replica construction method based on RSA, minimizing computational overhead, and a parallel data integrity verification approach that reduces communication overhead. A prototype implementation is evaluated in a real edge computing environment and compared to existing multiple replica auditing approaches. © 2023 ieee.

关键词： Edge computing

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parallel Bayesian Optimization for Optimal Scheduling of Underground Pumped Hydro-Energy Storage Systems 36

Parallel Bayesian Optimization for Optimal Scheduling of Und...

引用

36th ieee international parallel and distributed processing symposium (ieee IPDPS)

作者： Gobert, Maxime Gmys, Jan Toubeau, Jean-Francois Melab, Nouredine Tuyttens, Daniel Vallee, Francois Univ Mons Math & Operat Res Mons Belgium Univ Lille CNRS CRIStAL INRIA Lille Nord Europe Lille France Univ Mons Power Syst & Markets Res Mons Belgium

ISBN: (纸本)9781665497473

Underground Pumped Hydro-Energy Storage stations are sustainable options to enhance storage capacity and thus the flexibility of energy systems. Efficient management of such units requires high-performance optimization algorithms able to find solutions in a very restricted timing to comply with the responsive energy markets. In this context, parallel computing offers a valuable solution to ensure appropriate decisions that maximize the profit of the station operator, while guaranteeing the safety of the energy network. This study investigates the use of three existing algorithms in parallel Bayesian Optimization, namely q-EGO, BSP-EGO and TuRBO. The three algorithms have different inherent behaviors in terms of parallel potential and, even though TuRBO scales better, q-EGO remains the best choice regarding the final outcomes for all investigated batch sizes and manages to get up to 5 times more profits than other approaches.

关键词： Bayesian Optimization Gaussian Process Batch-based parallelism Optimization Electrical Engineering

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SARXARRAY AND STMTOOLS: OPEN-SOURCE PYTHON LIBRARIES FOR INSAR DATA processing AND ANALYSIS

SARXARRAY AND STMTOOLS: OPEN-SOURCE PYTHON LIBRARIES FOR INS...

引用

ieee international Geoscience and Remote Sensing symposium (IGARSS)

作者： Ku, O. Alidoost, F. Chandramouli, P. van Lankveld, T. Nattino, F. Grootes, M. W. van Leijen, F. J. Hanssen, R. F. Netherlands eSci Ctr Amsterdam Netherlands Delft Univ Technol Dept Geosci & Remote Sensing Delft Netherlands

ISBN: (纸本)9798350360332;9798350360325

We introduce SARXarray and STMtools, two python libraries designed to support the exploitation of modern Interferometric Synthetic Aperture Radar (InSAR) data by enabling handling of larger-than memory datasets and the incorporation and fusion with relevant contextual information. The libraries are developed upon two innovative and well- established open-source Python libraries: Xarray [5] and Dask [ 6]. They are implemented as Xarray extensions. SARXarray is designed to manipulate and operate on larger-than-memory coregistered raster stacks such as Single-look Complex images (SLC) or interferograms, performing scatterer selection and producing STM objects. STMtools leverage the Space- Time Matrix (STM) concept [3,4] and provides functionalities to process STM and perform enrichment/data fusion with other data sources. Both libraries are built on the Xarray library, providing support for a wide range of data formats, and utilize Dask for parallel computation, making them scalable for distributed computation infrastructures. By enabling InSAR data analysis incorporating contextual information, the two libraries enhance the potential to uncover underlying mechanisms driving deformation phenomena.

关键词： InSAR PSI distributed Computation Python Xarray Dask

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Tweeting from Left to Right: Tracing the Dynamics of Emotional Messaging and Public Engagement During the 2018 vs. 2022 US Campaigns 21

Tweeting from Left to Right: Tracing the Dynamics of Emotion...

引用

作者： Wang, Meng-Jie University of Canterbury School of Psychology Speech and Hearing Christchurch New Zealand

ISBN: (纸本)9798350329223

Despite numerous studies delving into social media politicking, there is, thus far, little understanding of how distinct emotions spread online. Much of the prior work has focused on positive vs. negative diffusion or measured emotional rhetoric without considering public engagement on such sites. Since elected officials increasingly turn to social media, even less is known about the impact of distinct emotional messaging on mobilizing users, attracting supporters, or disseminating political speeches. Moving beyond merely a valence-based approach, the study here seeks to address this gap by analyzing over 23,000 tweets posted by Senate candidates during the 2018 and 2022 US midterm elections through natural language processing. Specifically, leveraging the IBM Tone Analyzer, the present work implements a cross-sectional examination to explore: a) whether certain emotions - joy, confidence, anger, fear, and sadness - conveyed in candidates' tweets might be more likely to garner public attention;and b) if so, does this pattern vary across the political spectrum, illuminating the ways in which Democratic vs. Republican candidates adopt to expand their online reach over time? © 2023 ieee.

关键词： Natural language processing systems

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parallel Swarm Propagation for Neural Networks 1

Parallel Swarm Propagation for Neural Networks

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1st international symposium on parallel Computing and distributed Systems, PCDS 2024

作者： Bhattacharya, Arghya Premanand, V. School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu India

ISBN: (纸本)9798350349658

Deep learning has revolutionized various fields but faces challenges such as limitations owing to vanishing gradients and the inability to handle non-differentiable loss functions and layers. Our study proposes parallel Swarm Propagation for Neural Networks, a novel approach that leverages parallel particle swarm optimization (PSO) for optimizing neural networks. PSO does not require gradient calculations and effectively circumvents the vanishing gradient problem. This enables the exploration of non-differentiable loss functions and layers, potentially leading to more robust and flexible deep-learning models. Additionally, the inherent parallelism of PSO allows for significant performance gains by utilizing the ever-increasing processing power of parallel-computing architectures. Our study investigates the efficacy of parallel PSO in optimizing neural networks, analyzing its performance compared to traditional gradient-based methods, and exploring its potential for unlocking new possibilities in deep learning. © 2024 ieee.

关键词： Particle swarm optimization (PSO)

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DEAN: A Lightweight and Resource-efficient Blockchain Protocol for Reliable Edge Computing 36

DEAN: A Lightweight and Resource-efficient Blockchain Protoc...

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36th ieee international parallel and distributed processing symposium (ieee IPDPS)

作者： Al-Mamun, Ahdullah Shen, Haoting Zhao, Dongfang Univ Nevada Reno NV 89557 USA

ISBN: (纸本)9781665481069

Edge computing draws a lot of recent research interests because of the performance improvement by offloading many workloads from the remote data center to nearby edge nodes. Nonetheless, one open challenge of this emerging paradigm lies in the potential security issues on edge nodes. This paper proposes a cooperative protocol, namely DEAN, equipped with a unique resource-efficient quorum building mechanism to adopt blockchain seamlessly in an edge computing infrastructure to prevent data manipulation and allow fair data sharing with quick recovery under resource constraints of limited storage, computing, and network capacity. Specifically, DEAN leverages a parallel mechanism equipped with three independent core components, effectively achieving low resource consumption while allowing secured parallel block processing on edge nodes. We have implemented a system prototype based on DEAN and experimentally verified its effectiveness with a comparison with four popular blockchain implementations: Ethereum, Parity, IOTA, and Hyperledger Fabric. Experimental results show that the system prototype exhibits high resilience to arbitrary failures. Performance-wise, DEAN-based blockchain implementation outperforms the state-of-the-art blockchain systems with up to 88.6x higher throughput and 26x lower latency.

关键词： Edge computing Blockchains distributed computing Consensus protocols

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