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A Streaming System for Large-scale Temporal Graph Mining of Reddit Data 36

A Streaming System for Large-scale Temporal Graph Mining of ...

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

作者： Huber, Andreas Schroeder, Daniel Thilo Pogorelov, Konstantin Griwodz, Carsten Langguth, Johannes Univ Oslo Simula Res Lab Oslo Norway Simula Res Lab Oslo Norway Univ Oslo Oslo Norway BI Norwegian Business Sch Simula Res Lab Oslo Norway

ISBN: (纸本)9781665497473

The study of online social networks has become a major topic of research within the last decade, and many aspects of the networks and the behavior of their users have been investigated. The majority of research efforts has been directed at Twitter, which grants limited data accesses to researchers and provides detailed information. However, recently, important social and economic phenomena such as WallStreetBets or Antiwork have originated on Reddit, which has thus become an important field of investigation in its own right, and, due to its open nature, all Reddit data is available to study. As a consequence, in contrast to Twitter, where it is difficult to obtain large amounts of data, the main challenge of researching Reddit is to handle the vast amounts of data that are freely available. Here, we present the Reddit Dataset Stream Pipeline (RDSP), a simple and efficient parallel system based on Akka Streams that is capable of processing the entire Reddit dataset. We demonstrate how to build massive temporal graphs between subreddits from a parallel streamed dataset. We investigate the generated graphs and present experimental results. Moreover, we publish both the datasets as well as the codebase in order to invite researchers from different fields to contribute and profit from this work.

关键词： Reddit Stream processing parallel processing Computational Social Science

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ScaleDFS: Accelerating Decentralized and Private File Sharing via Scaling Directed Acyclic Graph processing 24

ScaleDFS: Accelerating Decentralized and Private File Sharin...

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33rd international symposium on High-Performance parallel and distributed Computing (HPDC)

作者： Song, Mansub Lan Anh Nguyen Kim, Sunggon Eom, Hyeonsang Son, Yongseok Seoul Natl Univ Seoul South Korea Chung Ang Univ Seoul South Korea Seoul Natl Univ Sci & Technol Seoul South Korea

ISBN: (纸本)9798400704130

This paper introduces a novel file system, ScaleDFS, designed to accelerate decentralized file sharing for a private network, leveraging the potential of scaling file management based on directed acyclic graph (DAG) on modern hardware. Specifically, in Sca1eDFS, we first design a DAG builder that parallelizes the construction of DAG nodes for a file while preserving critical orders to speed up the uploading process. Second, we introduce a DAG reader that retrieves leaf DAG nodes in parallel without graph traversal assisted by a devised DAG cache to accelerate the downloading process. Finally, we present a DAG remover that rapidly identifies obsolete DAG nodes/data and removes them in parallel to mitigate the garbage collection overhead without compromising consistency. We implement Sca1eDFS based on IPFS and demonstrate that Sca1eDFS outperforms IPFS by up to 3.7x, 1.8x, and 12.6x in realistic file, private blockchain, and gateway workloads, respectively.

关键词： Decentralized File System Merkle-DAG Private Network

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distributed Algorithms for the Graph Biconnectivity and Least Common Ancestor Problems 36

Distributed Algorithms for the Graph Biconnectivity and Leas...

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

作者： Bogle, Ian Slota, George M. Rensselaer Polytech Inst Comp Sci Dept Troy NY 12180 USA

ISBN: (纸本)9781665497473

Graph connectivity analysis is one of the primary ways to analyze the topological structure of social networks. Graph biconnectivity decompositions are of particular interest due to how they identify cut vertices and cut edges in a network. We present the first, to our knowledge, implementation of a distributed-memory parallel biconnectivity algorithm. As part of our algorithm, we also require the computation of least common ancestors (LCAs) of non-tree edge endpoints in a BFS tree. As such, we also propose a novel distributed algorithm for the LCA problem. Using our implementations, we observe up to a 14.8 x speedup from 1 to 128 MPI ranks for computing a biconnectivity decomposition.

关键词： distributed processing Social networking (online) Conferences distributed algorithms

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Peachy parallel Assignments (EduPar 2022) 36

Peachy Parallel Assignments (EduPar 2022)

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

作者： Buecker, H. Martin Casanova, Henri da Silva, Rafael Ferreira Lasserre, Alice Luyen, Derrick Namyst, Raymond Schoder, Johannes Wacrenier, Pierre-Andre Bunde, David P. Friedrich Schiller Univ Jena Inst Comp Sci Jena Germany Univ Hawaii Informat & Comp Sci Honolulu HI 96822 USA Oak Ridge Natl Lab Natl Ctr Computat Sci Oak Ridge TN USA Univ Bordeaux Comp Sci Dept Inria Bordeaux Sud Ouest Talence France Knox Coll Comp Sci Dept Galesburg IL USA

ISBN: (纸本)9781665497473

The presentation of Peachy parallel Assignments in several workshops on parallel and distributed computing education aims to promote the reuse of highquality assignments, both saving precious faculty time and improving the quality of course assignments. Presented assignments are selected competitively- they must have been successfully used in a real classroom, be easy for other instructors to adopt, and be "cool and inspirational" to encourage students to spend time on them and talk about them with others. Winning assignments are also archived on the Peachy parallel Assignments website. In this installment of Peachy parallel Assignments, we present three new assignments. The first assignment is to simulate an Abelian Sandpile, with grains of sand moving from tall piles to shorter ones. This is a discrete simulation that creates colorful and intricate images. The second assignment is a Big Data problem in which students use the MapReduce paradigm to recreate "Warming Stripes", a visualization of climate data that highlights climate change. The third assignment introduces climate-oriented optimization by asking students to schedule distributed workflows to minimize their carbon footprint.

关键词： Peachy parallel Assignments parallel computing education High-Performance Computing education parallel programming Curriculum Development Abelian Sandpile parallel Simulation MapReduce Big Data Warming Stripes distributed Workflow Scheduling Carbon Footprint

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Collecting Clustering Coefficient of distributed Graph Data with Shuffled Differential Privacy 22

Collecting Clustering Coefficient of Distributed Graph Data ...

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22nd IEEE international symposium on parallel and distributed processing with Applications, ISPA 2024

作者： Ding, Hongfa Fu, Peiwang Luo, Yingxuan Jiang, Heling Liu, Hai Guizhou University of Fiance and Economics School of Information Guiyang China Co. Ltd Guiyang China Jiangxi Datang International Fuzhou Power Generation Co. Ltd Fuzhou China

ISBN: (纸本)9798331509712

The intricate properties and relevance of graph data make it difficult to collect graph statistics privately via differential privacy (DP). Traditional centralized or local DP on graph data, face challenges like third-party threats and low data utility when collecting the clustering coefficient. In this regard, we introduce GCC-SDP, a scheme for collecting distributed Graph Clustering Coefficient with Shuffled DP (SDP). GCC-SDP gathers the local wedge lists of all edges and adjacency bit vectors through SDP and random response for calculating the noisy local triangle counts. It then collects the local degree values of all users by using Laplace mechanism, followed by estimating the global clustering coefficient of the global graph data by data collector. We provide specific steps of GCC-SDP and demonstrate through theoretical analysis that GCC-SDP conforms to various DPs, with unbiased results. Empirical experiments show that GCC-SDP performs better than existing local DP-based techniques across most accuracy metrics. © 2024 IEEE.

关键词： Data collection

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HM2: Efficient Host Memory Management for RDMA-Enabled distributed Systems 23

HM2: Efficient Host Memory Management for RDMA-Enabled Distr...

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32nd international symposium on High-Performance parallel and distributed Computing (HPDC) part of the ACM Federated Computing Research Conference (FCRC)

作者： Tang, Chen Chu, Zhaole Jin, Peiquan Luo, Yongping Guo, Kuankuan Univ Sci Technol China Hefei Peoples R China ByteDance Inc Beijing Peoples R China

ISBN: (纸本)9798400701559

Remote direct memory access (RDMA) supports zero-copy networking by transferring data from clients directly to host memory, eliminating the need to copy data between clients' memory and the data buffers in the hosting server. However, the hosting server must design efficient memory management schemes to handle incoming clients' data. In this paper, we propose a high-performance host memory management scheme called HM2 for RDMA-enabled distributed systems. We present a new buffer structure for incoming data from clients. In addition, we propose efficient data processing methods to reduce network transfers between clients and servers. We conducted a preliminary experiment to evaluate HM2, and the results showed HM2 achieved higher throughput than existing schemes, including L5 and FaRM.

关键词： RDMA memory management data buffer

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Mnemonic: A parallel Subgraph Matching System for Streaming Graphs 36

Mnemonic: A Parallel Subgraph Matching System for Streaming ...

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

作者： Bhattarai, Bibek Huang, Howie George Washington Univ Washington DC 20052 USA

ISBN: (纸本)9781665481069

Finding patterns in large highly connected datasets is critical for value discovery in business development and scientific research. This work focuses on the problem of subgraph matching on streaming graphs, which provides utility in a myriad of real-world applications ranging from social network analysis to cybersecurity. Each application poses a different set of control parameters, including the restrictions for a match, type of data stream, and search granularity. The problem-driven design of existing subgraph matching systems makes them challenging to apply for different problem domains. This paper presents Mnemonic, a programmable system that provides a high-level API and democratizes the development of a wide variety of subgraph matching solutions. Importantly, Mnemonic also delivers key data management capabilities and optimizations to support real-time processing on long-running, high-velocity multi-relational graph streams. The experiments demonstrate the versatility of Mnemonic, as it outperforms several state-of-the-art systems by up to two orders of magnitude.

关键词： subgraph graph pattern matching isomorphism streaming

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LBCB: One-sided RDMA-based distributed B+ tree Index with Low Bandwidth Consumption 22

LBCB: One-sided RDMA-based Distributed B+ tree Index with Lo...

引用

22nd IEEE international symposium on parallel and distributed processing with Applications, ISPA 2024

作者： Liu, Jibo Xi, Rui Cao, Qinzhen Nie, Xiaowen Hou, Zhuohan University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798331509712

Disaggregated memory architecture segregates computing and memory resources into distinct pools interconnected by a high-speed one-sided RDMA (Remote Direct Memory Access) network, enhancing memory utilization, reducing costs, and facilitating elastic scaling of computing and memory resources. However, optimizing index structures to maximize the benefits of this framework poses significant challenges. Despite progress in B+ tree indexes for disaggregated memory systems, they still suffer from severe write and read amplification issues, which impede latency and throughput *** this paper, we propose LBCB, a B+ tree index for disaggregated memory, which significantly reduces the bandwidth consumption of index operations. First, LBCB introduces an RDMA friendly B+ tree leaf node structure, which improves concurrency while reducing the bandwidth consumption. Second, LBCB designs a logical fusion FAA lock that synchronizes more information with a single RDMA communication, significantly reducing the number of RDMA network round trips. Finally, LBCB optimistically compresses the critical path of index operations, further optimizing latency and throughput. Evaluation results show that compared with the state-of-the-art distributed B+ tree index, the write performance of LBCB is improved by 1.42 times. © 2024 IEEE.

关键词： Memory architecture

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parallel Global Edge Switching for the Uniform Sampling of Simple Graphs with Prescribed Degrees 36

Parallel Global Edge Switching for the Uniform Sampling of S...

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

作者： Allendorf, Daniel Meyer, Ulrich Penschuck, Manuel Tran, Hung Goethe Univ Frankfurt Germany

ISBN: (纸本)9781665481069

The uniform sampling of simple graphs matching a prescribed degree sequence is an important tool in network science, e.g., to construct graph generators or null-models. Here, the Edge Switching Markov Chain (ES-MC) is a common choice. Given an arbitrary simple graph with the required degree sequence, ES-MC carries out a large number of small changes involving at most four edges to eventually obtain a uniform sample. In practice, reasonably short runs efficiently yield approximate uniform samples. We first engineer a simple sequential ES-MC implementation representing the graph in a hash-set. Despite its simplicity and to the best of our knowledge, our implementation significantly outperforms all openly available solutions. Secondly, we propose the Global Edge Switching Markov Chain (G-ES-MC) and show that it, too, converges to a uniform distribution. We provide empirical evidence that G-ES-MC requires not more switches than ES-MC (and often fewer). Thirdly, we engineer shared-memory parallel algorithms for ES-MC and G-ES-MC;we find that they benefit from the easier dependency structure of the G-ES-MC. In an empirical evaluation, we demonstrate the scalability of our implementations.

关键词： random graphs degrees edge switching markov chain parallelism

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Why Globally Re-shuffle? Revisiting Data Shuffling in Large Scale Deep Learning 36

Why Globally Re-shuffle? Revisiting Data Shuffling in Large ...

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

作者： Nguyen, Truong Thao Trahay, Francois Domke, Jens Drozd, Aleksandr Vatai, Emil Liao, Jianwei Wahib, Mohamed Gerofi, Balazs Natl Inst Adv Ind Sci & Technol Tokyo Japan Inst Polytech Paris Telecom SudParis Paris France RIKEN Ctr Computat Sci Tokyo Japan Southwest Univ China Coll Comp & Informat Sci Chongqing Peoples R China Tokyo Inst Technol Tokyo Japan Amigawa GK Tokyo Japan

ISBN: (纸本)9781665481069

Stochastic gradient descent (SGD) is the most prevalent algorithm for training Deep Neural Networks (DNN). SGD iterates the input data set in each training epoch processing data samples in a random access fashion. Because this puts enormous pressure on the I/O subsystem, the most common approach to distributed SGD in HPC environments is to replicate the entire dataset to node local SSDs. However, due to rapidly growing data set sizes this approach has become increasingly infeasible. Surprisingly, the questions of why and to what extent random access is required have not received a lot of attention in the literature from an empirical standpoint. In this paper, we revisit data shuffling in DL workloads to investigate the viability of partitioning the dataset among workers and performing only a partial distributed exchange of samples in each training epoch. Through extensive experiments on up to 2,048 GPUs of ABCI and 4,096 compute nodes of Fugaku, we demonstrate that in practice validation accuracy of global shuffling can be maintained when carefully tuning the partial distributed exchange. We provide a solution implemented in PyTorch that enables users to control the proposed data exchange scheme.

关键词： Training Deep learning distributed processing Costs Neural networks distributed databases Stochastic processes

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