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SW-LCM: A Scalable and Weakly-supervised Land Cover Mapping Method on a New Sunway Supercomputer 37

SW-LCM: A Scalable and Weakly-supervised Land Cover Mapping ...

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37th IEEE international parallel and distributed processing symposium (IPDPS)

作者： Zhao, Yi Zheng, Juepeng Fu, Haohuan Wu, Wenzhao Gao, Jie Chen, Mengxuan Zhang, Jinxiao Zhang, Lixian Dong, Rumnin Du, Zhenrong Liu, Sha Liu, Xin Zhang, Shaoqing Yu, Le Tsinghua Univ Beijing Peoples R China Tsinghua Univ Xian Inst Surveying & Mapping Join Beijing Peoples R China Natl Supercomp Ctr Wuxi Wuxi Peoples R China Natl Res Ctr Parallel Comp Engn & Technol Beijing Peoples R China Qingdao Pilot Natl Lab Marine Sci & Technol Qingdao Peoples R China

ISBN: (纸本)9798350337662

High-resolution land cover mapping (LCM) is an important application for studying and understanding the change of the earth surface. While deep learning (DL) methods demonstrate great potential in analyzing satellite images, they largely depend on massive high-quality labels. this paper proposes SWLCM, a Scalable and Weakly-supervised two-stage Land Cover Mapping method on a new Sunway Supercomputer. Our method consists of a k-means clustering module as a first stage, and an iterative deep learning module as a second stage. With the kmeans module providing a good enough starting point (taking inaccurate results as noisy labels), the deep learning module improves the classification results in an iterative way, without any labelling efforts required for processing large scenarios. To achieve efficiency for country-level land cover mapping, we design a customized data partition scheme and an on-the-fly assembly for k-means. through careful parallelization and optimization, our k-means module scales to 98,304 computing nodes (over 38 million cores), and provides a sustained performance of 437.56 PFLOPS, in a real LCM task of the entire region of China;the iterative updating part scales to 24,576 nodes, with a performance of 11 PFLOPS. We produce a 10-m resolution land cover map of China, with an accuracy of 83.5% (10-class) or 73.2% (25-class), 7% to 8% higher than best existing products, paving ways for finer land surveys to support sustainability-related applications.

关键词： New Sunway Supercomputer land cover mapping SW-LCM k-means noise correction learning

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parallel Tensor Train Rounding using Gram SVD 36

Parallel Tensor Train Rounding using Gram SVD

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

作者： Al Daas, Hussam Ballard, Grey Manning, Lawton Rutherford Appleton Lab Computat Math Grp Didcot Oxon England Wake Forest Univ Dept Comp Sci Winston Salem NC 27101 USA

ISBN: (纸本)9781665481069

Tensor Train (TT) is a low-rank tensor representation consisting of a series of three-way cores whose dimensions specify the TT ranks. Formal tensor train arithmetic often causes an artificial increase in the TT ranks. thus, a key operation for applications that use the TT format is rounding, which truncates the TT ranks subject to an approximation error guarantee. Truncation is performed via SVD of a highly structured matrix, and current rounding methods require careful orthogonalization to compute an accurate SVD. We propose a new algorithm for TT-Rounding based on the Gram SVD algorithm that avoids the expensive orthogonalization phase. Our algorithm performs less computation and can be parallelized more easily than existing approaches, at the expense of a slight loss of accuracy. We demonstrate that our implementation of the rounding algorithm is efficient, scales well, and consistently outperforms the existing state-of-the-art parallel implementation in our experiments.

关键词： distributed processing Tensors Scalability Approximation algorithms Approximation error Matrix decomposition Iterative methods

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A Research-Based Course Module to Study Non-determinism in High Performance applications 36

A Research-Based Course Module to Study Non-determinism in H...

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

作者： Bell, Patrick Suarez, Kae Fossum, Barbara Chapp, Dylan Bhowmick, Sanjukta Taufer, Michela Univ Tennessee Knoxville TN 37996 USA Univ North Texas Denton TX 76203 USA

ISBN: (纸本)9781665497473

We present a research-based course module to teach computer science students, software developers, and scientists the effects of non-determinism on high performance applications. the course module uses the ANACIN-X software package, a suite of software modules developed by the authors;ANACIN-X provides test cases, analytic tools to run different scenarios (e.g., using different numbers of processes and different communication patterns), and visualization tools for beginner, intermediate, and advanced level understandings in non-determinism. through our course module, students in computer science, software developers, and scientists gain an understanding of non-determinism, how to measure its occurrence in an execution, and how to identify its root causes within an application's code.

关键词： High Performance Computing parallelism Message Passing Workforce Development Debugging

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ASimulated Logistic Algorithm for distributed Flexible Assembly Flowshop Scheduling with Post-processing 15

ASimulated Logistic Algorithm for Distributed Flexible Assem...

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15th international symposium on Industrial Electronics and applications, INDEL 2024

作者： Hao, Haiqiang Zhu, Haiping School of Mechanical Science and Engineering Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9798350352320

An optimized mathematical model has been developed to streamline gas turbine production by targeting the minimization of the overall completion time, known as makespan. the production process is broken down into a series of stages: jobs are processed across multiple parallel factories, assembled into semi-finished goods with flexibility in their assembly order, and then transformed into final products through various stages of additional processing. To initiate solutions, a three-tier composite heuristic is employed, supplemented by six specialized neighborhood structures devised according to the different stages of the problem. Drawing inspiration from natural population growth mechanisms, we introduce a Simulated Logistic Algorithm (SLA) designed for robust solution enhancement. Comparative experiments on both small and extensive scales demonstrate the SLA's superior performance over established optimization techniques such as Particle Swarm Optimization, Tabu Search, and Genetic Algorithms, especially notable in large-scale applications. thorough mathematical and statistical evaluations reinforce the efficacy of the SLA and validate the proposed approach. © 2024 IEEE.

关键词： Genetic algorithms

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Multi-Phase Task-Based HPC applications: Quickly Learning how to Run Fast 36

Multi-Phase Task-Based HPC Applications: Quickly Learning ho...

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

作者： Nesi, Lucas Leandro Schnorr, Lucas Mello Legrand, Arnaud PPGC UFRGS Inst Informat Porto Alegre RS Brazil Univ Grenoble Alpes Grenoble France Univ Grenoble Alpes LIG Grenoble INP INRIACNRS F-38000 Grenoble France

ISBN: (纸本)9781665481069

parallel applications performance strongly depends on the number of resources. Although adding new nodes usually reduces execution time, excessive amounts are often detrimental as they incur substantial communication overhead, which is difficult to anticipate. Characteristics like network contention, data distribution methods, synchronizations, and how communications and computations overlap generally impact the performance. Finding the correct number of resources can thus be particularly tricky for multi-phase applications as each phase may have very different needs, and the popularization of hybrid (CPU+GPU) machines and heterogeneous partitions makes it even more difficult. In this paper, we study and propose, in the context of a task-based GeoStatistic application, strategies for the application to actively learn and adapt to the best set of heterogeneous nodes it has access to. We propose strategies that use the Gaussian Process method with trends, bound mechanisms for reducing the search space, and heterogeneous behavior modeling. We compare these methods with traditional exploration strategies in 16 different machines scenarios. In the end, the proposed strategies are able to gain up to similar to 51% compared to the standard case of using all the nodes while having low overhead.

关键词： HPC Heterogeneous Task-Based Distribution Load Balancing

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Efficient Differentially Private Tensor Factorization in the parallel and distributed Computing Paradigm 21

Efficient Differentially Private Tensor Factorization in the...

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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： Zhang, Feng Wang, Hao Guo, Ruixin Xue, Erkang Qu, Guangzhi China University of Geosciences School of Computer Science Wuhan China Oakland University Department of Computer Science and Engineering Rochester United States

ISBN: (纸本)9798350329223

Tensor factorization plays a fundamental role in multiple areas of AI research. Nevertheless, it encounters significant challenges related to privacy breaches and operational efficiency. In this study, we propose a novel approach that addresses both of these issues simultaneously by integrating differential privacy with parallel and distributed computing. To accommodate diverse scenarios, we introduce two models: DPTF-SVRG and ADMM-DPTF, each leveraging specific techniques. DPTF-SVRG is designed for single-GPU environments and utilizes a unique strategy to reduce gradient variance, enabling faster convergence compared to SGD. Moreover, it achieves parallelism on the GPU through a lock-free asynchronous approach. On the other hand, ADMM-DPTF utilizes distributed ADMM to parallelize DPTF-SVRG, enabling multi-GPU parallelism. Experimental results demonstrate that our algorithms outperform existing benchmarks while maintaining differential privacy. © 2023 IEEE.

关键词： Tensors

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QoS-awareness of Microservices with Excessive Loads via Inter-Datacenter Scheduling 36

QoS-awareness of Microservices with Excessive Loads via Inte...

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

作者： Shi, Jiuchen Wang, Jiawen Fu, Kaihua Chen, Quan Zeng, Deze Guo, Minyi Shanghai Jiao Tong Univ Shanghai Peoples R China China Univ Geosci Wuhan Peoples R China

ISBN: (纸本)9781665481069

User-facing applications often experience excessive loads and are shifting towards microservice software architecture. While the local datacenter may not have enough resources to host the excessive loads, a reasonable solution is moving some microservices of the applications to remote datacenters. However, it is nontrivial to identify the appropriate migration decision, as the microservices show different characteristics, and the local datacenter also shows different resource contention situations. We therefore propose ELIS, an inter-datacenter scheduling system that ensures the required Quality-of-Service (QoS) of the microservice application with excessive loads, while minimizing the resource usage of the remote datacenter. ELIS comprises a resource manager and a reward-based microservice migrator. the resource manager finds the near-optimal resource configurations for different microservices to minimize resource usage while ensuring QoS. the microservice migrator migrates some microservices to remote datacenters when local resources cannot afford the excessive loads. Our experimental results show that ELIS ensures the required QoS of user-facing applications at excessive loads. Meanwhile, it reduces overall/remote resource usage by 13.1% and 58.1% on average, respectively.

关键词： distributed processing Costs Software architecture Microservice architectures Quality of service Performance gain throughput

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Dynamic Batch parallel Algorithms for Updating PageRank 36

Dynamic Batch Parallel Algorithms for Updating PageRank

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

作者： Sahu, Subhajit Kothapalli, Kishore Banerjee, Dip Sankar Int Inst Informat Technol Hyderabad Hyderabad India Indian Inst Technol Jodhpur Jodhpur Rajasthan India

ISBN: (纸本)9781665497473

the design and implementation of parallel algorithms for dynamic graph problems is attracting significant research attention in the recent years, driven by numerous applications to social network analysis, neuroscience, and protein interaction networks. One such problem is the computation of PageRank values of vertices in a directed graph. this paper presents two new parallel algorithms for recomputing the PageRank values of vertices in a dynamic graph. Our techniques require the recomputation of the PageRank of only the vertices affected by the insertion/deletion of a batch of edges. We conduct detailed experimental studies of our algorithm on a set of 11 real-world graphs. Our results on Intel Xeon Silver 4116 CPU and NVIDIA Tesla V100 PCIe 16GB GPU indicate that our algorithms outperform static and dynamic update algorithms by 6.1x and 8.6x on the CPU, and by 9.8x and 9.3x on the GPU respectively. We also compare the performance of the algorithms in batched mode to cumulative single-edge updates.

关键词： Proteins Silver distributed processing Neuroscience Social networking (online) Heuristic algorithms Graphics processing units

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Research on communication mechanism optimization based on distributed graph computing environment 4

Research on communication mechanism optimization based on di...

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4th international Conference on Big Data Economy and Information Management, BDEIM 2023

作者： Lin, Jianchao Cao, Chunna Ren, Chunhui State Grid Laboratory of Grid Advanced Computing and Applications State Grid Smart Grid Research Institute Co. Ltd Beijing China

ISBN: (纸本)9798400716669

In recent years, with the rapid development of the Internet of things, the Internet, and social networks, the storage of data in the network is growing at an explosive rate and is becoming more and more closely related to the real natural world. Driven by large-scale data mining and machine learning applications, distributed graph computing models that use graph data structures to describe data and relationships between data have been increasingly widely used. therefore, this paper studies the optimization of communication mechanisms based on a distributed graph computing environment. Firstly, a BSP model based on a pure message-passing communication mechanism of a distributed graph computing system is established. Secondly, the optimization model is evaluated from two aspects: data communication and convergence condition judgment. Finally, large-scale data sets are used to test and evaluate performance optimization. the results show that this method can greatly improve the efficiency of graph parallel computing. © 2023 ACM.

关键词： BSP model distributed Graph Computation distributed system Graph processing parallel computing

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Modeling Memory Contention between Communications and Computations in distributed HPC Systems 36

Modeling Memory Contention between Communications and Comput...

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

作者： Denis, Alexandre Jeannot, Emmanuel Swartvagher, Philippe Inria Bordeaux Sud Ouest Bordeaux France

ISBN: (纸本)9781665497473

To amortize the cost of MPI communications, distributed parallel HPC applications can overlap network communications with computations in the hope that it improves global application performance. When using this technique, both computations and communications are running at the same time. But computation usually also performs some data movements. Since data for computations and for communications use the same memory system, memory contention may occur when computations are memory-bound and large messages are transmitted through the network at the same time. In this paper we propose a model to predict memory band-width for computations and for communications when they are executed side by side, according to data locality and taking contention into account. Elaboration of the model allowed to better understand locations of bottleneck in the memory system and what are the strategies of the memory system in case of contention. the model was evaluated on many platforms with different characteristics, and showed a prediction error in average lower than 4 %.

关键词： HPC MPI Memory Contention NUMA Band-width Predictive Models Multicore processing

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