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检索条件"主题词=Partitioning Algorithms"

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TLGPA: Two-Level Graph partitioning Algorithm under Multi-Machine Multi-Card System

TLGPA: Two-Level Graph Partitioning Algorithm under Multi-Ma...

引用

International Conference on Computer Supported Cooperative Work in Design

作者： Huanqing Cui Silin Lv College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Recently, the distributed graph processing systems are widely deployed on the cluster of computers with multiple accelerating cards, but as the basis of these systems, the traditional graph partitioning algorithms only partition graphs to computers without considering the distribution over multiple accelerating card. To tackle the graph partitioning requirements on a multi-computer multi-card system, this paper proposes a two-level graph partitioning algorithm (TLGPA). The first level aims to divide the graph to computers based on the piece-wise adaptive mutation differential evolution algorithm (PAMDE), whose purpose is to minimize the communication cost while keeping load balance. The second level aims to divide the sub-graph of a computer to different accelerating cards based on label propagation algorithm (LPA) whose purpose is to keep load balance. Experimental results with real-world graphs demonstrate that the proposed algorithm outperforms the rival algorithms in most cases.

关键词： Computers Costs Federated learning Heuristic algorithms Clustering algorithms partitioning algorithms

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MDL: Maximum Density Label-Cut Graph partitioning

MDL: Maximum Density Label-Cut Graph Partitioning

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International Conference on Big Data and Information Analytics (BigDIA)

作者： Zhanzhe Li Ningchao Ge Haiwen Chen Kaiming Xiao Peng Peng Hongbin Huang Laboratory for Big Data and Decision National University of Defense Technology Changsha China College of Computer Science and Electronic Engineering Hunan University Changsha China

ISBN: (数字)9798350354621

ISBN: (纸本)9798350354638

As the scale of graph data increases, the performance bottleneck problem of centralized graph data management is becoming increasingly prominent. Therefore, it is common to divide a massive graph dataset into several data partitions. To achieve distributed workload and improve efficiency through distributed graph management. The typical graph partitioning method is to minimize the cutting of edges and points, or to maximize the tightness of the structure within the partition. However, these methods are difficult to avoid cross partition same label query connections (i.e. inter partition connections) in the context of multi-label graph data queries. Toward this end, we propose a Maximum Density Label partitioning (MDL) based on labels. Our method allows for independent query evaluation of more identical labels without inter partition connections. A heuristic greedy algorithm is proposed to address the challenge that maximum density label partitioning is an NP-hard problem. A large number of experiments conducted on various synthetic and real graph datasets have shown that the proposed method can significantly avoid inter partition connections and produce good performance.

关键词： Greedy algorithms NP-hard problem System performance Query processing Clustering algorithms Graphics processing units Load management partitioning algorithms Sparse matrices Python

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Universal partitioning of a Large Array for Communications in Environments with Limited Spatial Coherence

Universal Partitioning of a Large Array for Communications i...

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Asilomar Conference on Signals, Systems & Computers

作者： Yongjie Zhuang John R. Buck Andrew C. Singer Dept. Electrical and Computer Engineering Stony Brook University Stony Brook NY USA Dept. Electrical and Computer Engineering University of Massachusetts Dartmouth Dartmouth MA USA

ISBN: (数字)9798350354058

ISBN: (纸本)9798350354065

Large aperture arrays improve communication performance with higher gain, but are susceptible to limited spatial coherence of signals, especially in shallow water acoustic environments. An acoustic communication receiver's ability to combine the signal wavefront coherently degrades when there are losses across the array in either phase coherence or the ability to track phase variation. To mitigate such coherence issues, the array can be separated into smaller segments of sensors known as subapertures. The phase variations can be tracked and mitigated on each subaperture. Subsequently, each subaperture can be coherently combined to realize the full array gain, through this two-stage process. However, subaperture size selection remains an open problem, especially in dynamic, uncertain environments like those experienced in underwater acoustic communications. In this paper, a universal algorithm is proposed for realizing the performance of the best possible partitioning among a collection of partitions of the receiver into subapertures, such that the overall receiver performance is as good as if the best partitioning were known a priori. This work builds on previous work in universal adaptive filtering and beamforming.

关键词： Phased arrays Array signal processing Simulation Spatial coherence Coherence Receivers Acoustic arrays partitioning algorithms Underwater acoustics Sensor arrays

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Accelerated Multilevel Graph partitioning on GPUs

Accelerated Multilevel Graph Partitioning on GPUs

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IEEE International Conference on High Performance Computing Workshops (HiPCW)

作者： Amitesh Singh Bhakti Dhorajiya Vishwesh Jatala Department of CSE IIT Bhilai India

ISBN: (数字)9798331509118

ISBN: (纸本)9798331509125

Graph partitioning is a long-standing research problem and has applications in various domains, such as data mining, scientific computing, VLSI design. For many years researchers have been focusing on improving the performance of graph partitioning using multi-core CPUs and accelerators like GPUs. However, efficient implementation of graph partitioning on GPU is a challenge. This is because graphs exhibit irregular behavior whereas GPUs follow SIMD execution patterns and GPUs have various resources and efficient utilization is the key to improving the *** paper aims to provide an efficient GPU based implementation of multi-level graph partitioning scheme. We provide GPU implementations of coarsening, partitioning, and refinement phases of the algorithm, to improve the overall performance of the graph partitioning scheme. We evaluated our implementation on various real-world datasets, and we observed a significant improvement in the performance when compared to the baseline METIS.

关键词： Single instruction multiple data Scientific computing High performance computing Conferences Graphics processing units Focusing Very large scale integration partitioning algorithms Data mining

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An Enhanced Random Forest Algorithm Based on Spatial partitioning and Weighted Voting Mechanism

An Enhanced Random Forest Algorithm Based on Spatial Partiti...

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Frontiers Technology of Information and Computer (ICFTIC), IEEE International Conference on

作者： Mei Wang Qingdao Institute of Software College of Computer Science and Technology China University of Petroleum (East China) Qingdao Shandong China

ISBN: (数字)9798331541750

ISBN: (纸本)9798331541767

Random Forest is an efficient machine learning classification algorithm known for its high accuracy and interpretability. However, the traditional voting mechanism in the standard Random Forest algorithm assigns equal voting power to all decision trees, regardless of their training accuracy, which can reduce overall model performance. To address this issue without overemphasizing individual trees through direct weighting, this paper proposes an enhanced Random Forest algorithm based on spatial partitioning and a weighted voting mechanism. The proposed method partitions the entire feature space, and within each subspace, a relative majority voting mechanism is applied. Additionally, a validation set is used to assign weights to each subspace, taking into account the differences between strong and weak classifiers. Experimental results on publicly available datasets demonstrate that the proposed algorithm outperforms the traditional Random Forest, SVM, and KNN classifiers in terms of classification accuracy and recall.

关键词： Support vector machines Training Accuracy Machine learning algorithms Nearest neighbor methods Classification algorithms partitioning algorithms Decision trees Random forests Standards

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An Optimized partitioning Around Medoids Algorithm for Analyzing Agricultural Data Utilizing Deep Neural Network for Feature Selection

An Optimized Partitioning Around Medoids Algorithm for Analy...

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International Symposium on Advanced Electrical and Communication Technologies (ISAECT)

作者： S. Gayathri Devi Samir El-Nakla A.P. Roger Rozario Hiren Mewada M. Arthi K. Rajasri School of Computer Sci. & Engg Vellore Institute of Technology Chennai India Electrical Engineering Department Prince Mohammad Bin Fahd University Al Khobar Kingdom of Saudi Arabia Electrical & Electronics Engg. Department Sri Ramakrishna Institute of Technology Coimbatore India Saveetha School of Engineering Saveetha Inst. of Medical & Tech. Sci Chennai India Electrical & Electronics Engg. Department MLR Institute of Technology Hyderabad Telangana India

ISBN: (数字)9798331529987

ISBN: (纸本)9798331529994

In India’s Agriculture, countless decisions are taken by personnel every day. The major challenge is an accurate estimation of crop yield. In the proposed work, the Partition Around Medoids (PAM) algorithm is employed. The algorithm challenges include 1) predicting the k value and 2) comparing cluster quality is time-consuming. This work evaluates agricultural data by 1) selecting optimal parameters and 2) applying optimized PAM (OPAM) to boost crop production. Initially, feature selection is carried out using a deep neural network (DNN). Additionally, massive amounts of crop, soil, and climatic data are mined for processing. Then, analyzing new, non-experimental data is performed which helps in optimizing the production and increases the resilience of agriculture to changes in climatic conditions. Later, the PAM algorithm is used on the dataset. In this case, the user enters a number of clusters (k). The Anarchic Society Optimization (ASO) algorithm selects k automatically. The model is then assessed using performance metrics.

关键词： Clustering algorithms Artificial neural networks Production Voltage Soil Prediction algorithms Feature extraction Agriculture partitioning algorithms Solar panels

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Streaming Balanced Graph partitioning algorithms for Random Graphs 14

Streaming Balanced Graph Partitioning Algorithms for Random ...

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Annual ACM-SIAM Symposium on Discrete algorithms

作者： Isabelle Stanton Google Inc.

ISBN: (纸本)9781611973389

With recent advances in storage technology, it is now possible to store the vast amounts of data generated by cloud computing applications. The sheer size of `big data' motivates the need for streaming algorithms that can compute approximate solutions without full random access to all of the data. In this paper, we consider the problem of loading a graph onto a distributed cluster with the goal of optimizing later computation. We model this as computing an approximately balanced k-partitioning of a graph in a streaming fashion with only one pass over the data. We give lower bounds on this problem, showing that no algorithm can obtain an o(n) approximation with a random or adversarial stream ordering. We analyze two variants of a randomized greedy algorithm (looking at the distribution of edges from the vertex to be assigned, one prefers the partition that is the arg max and one that assigns the vertex proportional to the edge distribution) on random graphs with embedded balanced k-cuts and are able to theoretically bound the performance of each algorithms - the arg max algorithm is able to recover the embedded k-cut while the proportional variant can not. This matches the experimental results in [30].

关键词： Streaming ASSAY SCALE Storage technologies partitioning algorithms big data Line graph Random algorithms Arginine arg Lower bound

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Process variation-aware workload partitioning algorithms for GPUs supporting spatial-multitasking 14

Process variation-aware workload partitioning algorithms for...

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Design, Automation and Test in Europe Conference and Exhibition

作者： Paula Aguilera Jungseob Lee Amin Farmahini-Farahani Katherine Morrow Michael Schulte Nam Sung Kim University of Wisconsin Madison Advanced Micro Devices

ISBN: (纸本)9783981537024

High-level programming languages have transformed graphics processing units (GPUs) from domain-restricted devices into powerful compute platforms. Yet many “generalpurpose GPU” (GPGPU) applications fail to fully utilize the GPU resources. Executing multiple applications simultaneously on different regions of the GPU (spatial multitasking) thus improves system performance. However, within-die process variations lead to significantly different maximum operating frequencies (F max ) of the streaming multiprocessors (SMs) within a GPU. As the chip size and number of SMs per chip increase, the frequency variation is also expected to increase, exacerbating the problem. The increased number of SMs also provides a unique opportunity: we can allocate resources to concurrently-executing applications based on how those applications are affected by the different available F max values. In this paper, we study the effects of per-SM clocking on spatial multitasking-capable GPUs. We demonstrate two factors that affect the performance of simultaneously-running applications: (i) the SM partitioning algorithm that decides how many resources to assign to each application, and (ii) the assignment of SMs to applications based on the operating frequencies of those SMs and the applications characteristics. Our experimental results show that spatial multitasking that partitions SMs based on application characteristics, when combined with per-SM clocking, can greatly improve application performance by up to 46% on average compared to cooperative multitasking with global clocking.

关键词： Graphics processing units Multitasking Clocks Business process re-engineering partitioning algorithms Kernel Frequency control

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Coupling-aware graph partitioning algorithms: Preliminary study

Coupling-aware graph partitioning algorithms: Preliminary st...

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International Conference on High Performance Computing

作者： Maria Predari Aurélien Esnard Univ. Bordeaux LaBRI Talence France CNRS LaBRI Talence France

In the field of scientific computing, load balancing is a major issue that determines the performance of parallel applications. Nowadays, simulations of real-life problems are becoming more and more complex, involving numerous coupled codes, representing different models. In this context, reaching high performance can be a great challenge. In this paper, we present graph partitioning techniques, called co-partitioning, that address the problem of load balancing for two coupled codes: the key idea is to perform a “coupling-aware” partitioning, instead of partitioning these codes independently, as it is usually done. Finally, we present a preliminary experimental study which compares our methods against the usual approach.

关键词： Couplings Program processors Computational modeling partitioning algorithms Load modeling Load management Atmospheric modeling

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Optimal Online Balanced Graph partitioning 40

Optimal Online Balanced Graph Partitioning

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40th IEEE Conference on Computer Communications (IEEE INFOCOM)

作者： Pacut, Maciej Parham, Mahmoud Schmid, Stefan Univ Vienna Fac Comp Sci Vienna Austria

ISBN: (纸本)9780738112817

Distributed applications generate a significant amount of network traffic. By collocating frequently communicating nodes (e.g., virtual machines) on the same clusters (e.g., server or rack), we can reduce the network load and improve application performance. However, the communication pattern of different applications is often unknown a priori and may change over time, hence it needs to be learned in an online manner. This paper revisits the online balanced partitioning problem that asks for an algorithm that strikes an optimal tradeoff between the benefits of collocation (i.e., lower network load) and its costs (i.e., migrations). Our first contribution is a significantly improved deterministic lower bound of Omega(k . l) on the competitive ratio, where l is the number of clusters and k is the cluster size, even for a scenario in which the communication pattern is static and can be perfectly partitioned;we also provide an asymptotically tight upper bound of O (k . l) for this scenario. For k = 3, we contribute an asymptotically tight upper bound of Theta(l) for the general model in which the communication pattern can change arbitrarily over time. We improve the result for k = 2 by providing a strictly 6-competitive upper bound for the general model.

关键词： Upper bound Computational modeling Conferences Clustering algorithms Telecommunication traffic Virtual machining partitioning algorithms

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