Exploiting multi–core and many–core parallelism for subspace clustering

Abstract Finding clusters in high dimensional data is a challenging research problem. Subspace clustering algorithms aim to find clusters in all possible subspaces of the dataset, where a subspace is a subset of dimensions of the data. But the exponential increase in the number of subspaces with the dimensionality of data renders most of the algorithms inefficient as well as ineffective. Moreover, these algorithms have ingrained data dependency in the clustering process, which means that parallelization becomes difficult and inefficient. SUBSCALE is a recent subspace clustering algorithm which is scalable with the dimensions and contains independent processing steps which can be exploited through parallelism. In this paper, we aim to leverage the computational power of widely available multi-core processors to improve the runtime performance of the SUBSCALE algorithm. The experimental evaluation shows linear speedup. Moreover, we develop an approach using graphics processing units (GPUs) for fine-grained data parallelism to accelerate the computation further. First tests of the GPU implementation show very promising results.

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Efficient Vector Partitioning Algorithms for Graph Clustering

journal of Data Intelligence ◽

10.26421/jdi1.2-1 ◽

2020 ◽

Vol 1 (2) ◽

pp. 101-123

Author(s):

Hiroaki Shiokawa ◽

Yasunori Futamura

Keyword(s):

Social Networks ◽

Large Scale ◽

Clustering Algorithm ◽

Ground Truth ◽

Graph Clustering ◽

Mining Communities ◽

Fine Grained ◽

Efficient Vector ◽

Public Datasets ◽

Many Core

This paper addressed the problem of finding clusters included in graph-structured data such as Web graphs, social networks, and others. Graph clustering is one of the fundamental techniques for understanding structures present in the complex graphs such as Web pages, social networks, and others. In the Web and data mining communities, the modularity-based graph clustering algorithm is successfully used in many applications. However, it is difficult for the modularity-based methods to find fine-grained clusters hidden in large-scale graphs; the methods fail to reproduce the ground truth. In this paper, we present a novel modularity-based algorithm, \textit{CAV}, that shows better clustering results than the traditional algorithm. The proposed algorithm employs a cohesiveness-aware vector partitioning into the graph spectral analysis to improve the clustering accuracy. Additionally, this paper also presents a novel efficient algorithm \textit{P-CAV} for further improving the clustering speed of CAV; P-CAV is an extension of CAV that utilizes the thread-based parallelization on a many-core CPU. Our extensive experiments on synthetic and public datasets demonstrate the performance superiority of our approaches over the state-of-the-art approaches.

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A Representation of Membrane Computing with a Clustering Algorithm on the Graphical Processing Unit

Processes ◽

10.3390/pr8091199 ◽

2020 ◽

Vol 8 (9) ◽

pp. 1199

Author(s):

Ravie Chandren Muniyandi ◽

Ali Maroosi

Keyword(s):

Graphics Processing Units ◽

Clustering Algorithm ◽

Hamiltonian Path ◽

Fold Increase ◽

General Purpose ◽

Processing Unit ◽

Thread Block ◽

Hard Problems ◽

Graphical Processing ◽

Graphics Processing

Long-timescale simulations of biological processes such as photosynthesis or attempts to solve NP-hard problems such as traveling salesman, knapsack, Hamiltonian path, and satisfiability using membrane systems without appropriate parallelization can take hours or days. Graphics processing units (GPU) deliver an immensely parallel mechanism to compute general-purpose computations. Previous studies mapped one membrane to one thread block on GPU. This is disadvantageous given that when the quantity of objects for each membrane is small, the quantity of active thread will also be small, thereby decreasing performance. While each membrane is designated to one thread block, the communication between thread blocks is needed for executing the communication between membranes. Communication between thread blocks is a time-consuming process. Previous approaches have also not addressed the issue of GPU occupancy. This study presents a classification algorithm to manage dependent objects and membranes based on the communication rate associated with the defined weighted network and assign them to sub-matrices. Thus, dependent objects and membranes are allocated to the same threads and thread blocks, thereby decreasing communication between threads and thread blocks and allowing GPUs to maintain the highest occupancy possible. The experimental results indicate that for 48 objects per membrane, the algorithm facilitates a 93-fold increase in processing speed compared to a 1.6-fold increase with previous algorithms.

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XVA PRINCIPLES, NESTED MONTE CARLO STRATEGIES, AND GPU OPTIMIZATIONS

International Journal of Theoretical and Applied Finance ◽

10.1142/s0219024918500309 ◽

2018 ◽

Vol 21 (06) ◽

pp. 1850030 ◽

Cited By ~ 3

Author(s):

LOKMAN A. ABBAS-TURKI ◽

STÉPHANE CRÉPEY ◽

BABACAR DIALLO

Keyword(s):

Monte Carlo ◽

Interest Rate ◽

Outer Layer ◽

Graphics Processing Units ◽

Lower Layer ◽

Credit Derivatives ◽

Square Root ◽

Root Number ◽

Graphics Processing ◽

Gpu Implementation

We present a nested Monte Carlo (NMC) approach implemented on graphics processing units (GPUs) to X-valuation adjustments (XVAs), where X ranges over C for credit, F for funding, M for margin, and K for capital. The overall XVA suite involves five compound layers of dependence. Higher layers are launched first, and trigger nested simulations on-the-fly whenever required in order to compute an item from a lower layer. If the user is only interested in some of the XVA components, then only the sub-tree corresponding to the most outer XVA needs be processed computationally. Inner layers only need a square root number of simulation with respect to the most outer layer. Some of the layers exhibit a smaller variance. As a result, with GPUs at least, error-controlled NMC XVA computations are doable. But, although NMC is naively suited to parallelization, a GPU implementation of NMC XVA computations requires various optimizations. This is illustrated on XVA computations involving equities, interest rate, and credit derivatives, for both bilateral and central clearing XVA metrics.

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Rough assessment of GPU capabilities for parallel PCC-based biclustering method applied to microarray data sets

Bio-Algorithms and Med-Systems ◽

10.1515/bams-2015-0033 ◽

2015 ◽

Vol 11 (4) ◽

Cited By ~ 3

Author(s):

Patryk Orzechowski ◽

Krzysztof Boryczko

Keyword(s):

Microarray Data ◽

Graphics Processing Units ◽

Pearson Correlation ◽

Clustering Algorithms ◽

Computation Time ◽

Data Sets ◽

Heterogeneous Environments ◽

Heterogeneous Architecture ◽

Input Matrix ◽

Graphics Processing

AbstractParallel computing architectures are proven to significantly shorten computation time for different clustering algorithms. Nonetheless, some characteristics of the architecture limit the application of graphics processing units (GPUs) for biclustering task, whose function is to find focal similarities within the data. This might be one of the reasons why there have not been many biclustering algorithms proposed so far. In this article, we verify if there is any potential for application of complex biclustering calculations (CPU+GPU). We introduce minimax with Pearson correlation – a complex biclustering method. The algorithm utilizes Pearson’s correlation to determine similarity between rows of input matrix. We present two implementations of the algorithm, sequential and parallel, which are dedicated for heterogeneous environments. We verify the weak scaling efficiency to assess if a heterogeneous architecture may successfully shorten heavy biclustering computation time.

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State-of-the-art in Heterogeneous Computing

Scientific Programming ◽

10.1155/2010/540159 ◽

2010 ◽

Vol 18 (1) ◽

pp. 1-33 ◽

Cited By ~ 96

Author(s):

Andre R. Brodtkorb ◽

Christopher Dyken ◽

Trond R. Hagen ◽

Jon M. Hjelmervik ◽

Olaf O. Storaasli

Keyword(s):

Graphics Processing Units ◽

High Performance ◽

Heterogeneous Computing ◽

State Of The Art ◽

Peak Performance ◽

Fine Grained ◽

Field Programmable ◽

Programmable Gate Arrays ◽

Cost Efficient ◽

Graphics Processing

Node level heterogeneous architectures have become attractive during the last decade for several reasons: compared to traditional symmetric CPUs, they offer high peak performance and are energy and/or cost efficient. With the increase of fine-grained parallelism in high-performance computing, as well as the introduction of parallelism in workstations, there is an acute need for a good overview and understanding of these architectures. We give an overview of the state-of-the-art in heterogeneous computing, focusing on three commonly found architectures: the Cell Broadband Engine Architecture, graphics processing units (GPUs), and field programmable gate arrays (FPGAs). We present a review of hardware, available software tools, and an overview of state-of-the-art techniques and algorithms. Furthermore, we present a qualitative and quantitative comparison of the architectures, and give our view on the future of heterogeneous computing.

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Improved Object Recognition with Decision Trees Using Subspace Clustering

Journal of Advanced Computational Intelligence and Intelligent Informatics ◽

10.20965/jaciii.2016.p0041 ◽

2016 ◽

Vol 20 (1) ◽

pp. 41-48 ◽

Cited By ~ 1

Author(s):

Billy Peralta ◽

◽

Luis Alberto Caro

Keyword(s):

Object Recognition ◽

Decision Trees ◽

Clustering Algorithm ◽

Comprehensive Evaluation ◽

Recognition Performance ◽

Clustering Algorithms ◽

Subspace Clustering ◽

Visual Words ◽

Ensemble Techniques ◽

Standard Object

Generic object recognition algorithms usually require complex classificationmodels because of intrinsic difficulties arising from problems such as changes in pose, lighting conditions, or partial occlusions. Decision trees present an inexpensive alternative for classification tasks and offer the advantage of being simple to understand. On the other hand, a common scheme for object recognition is given by the appearances of visual words, also known as the bag-of-words method. Although multiple co-occurrences of visual words are more informative regarding visual classes, a comprehensive evaluation of such combinations is unfeasible because it would result in a combinatorial explosion. In this paper, we propose to obtain the multiple co-occurrences of visual words using a variant of the CLIQUE subspace-clustering algorithm for improving the object recognition performance of simple decision trees. Experiments on standard object datasets show that our method improves the accuracy of the classification of generic objects in comparison to traditional decision tree techniques that are similar, in terms of accuracy, to ensemble techniques. In future we plan to evaluate other variants of decision trees, and apply other subspace-clustering algorithms.

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Graphics processing unit implementation of the F-statistic for continuous gravitational wave searches

Classical and Quantum Gravity ◽

10.1088/1361-6382/ac4616 ◽

2021 ◽

Author(s):

Liam Dunn ◽

Patrick Clearwater ◽

Andrew Melatos ◽

Karl Wette

Keyword(s):

Gravitational Wave ◽

Graphics Processing Units ◽

Graphics Processing Unit ◽

Computational Cost ◽

Processing Unit ◽

Central Processing ◽

Long Baseline ◽

Using Data ◽

Graphics Processing ◽

Gpu Implementation

Abstract The F-statistic is a detection statistic used widely in searches for continuous gravitational waves with terrestrial, long-baseline interferometers. A new implementation of the F-statistic is presented which accelerates the existing "resampling" algorithm using graphics processing units (GPUs). The new implementation runs between 10 and 100 times faster than the existing implementation on central processing units without sacrificing numerical accuracy. The utility of the GPU implementation is demonstrated on a pilot narrowband search for four newly discovered millisecond pulsars in the globular cluster Omega Centauri using data from the second Laser Interferometer Gravitational-Wave Observatory observing run. The computational cost is 17:2 GPU-hours using the new implementation, compared to 1092 core-hours with the existing implementation.

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Parallelization and sustainability of distributed genetic algorithms on many-core processors

International Journal of Intelligent Computing and Cybernetics ◽

10.1108/ijicc-06-2013-0033 ◽

2014 ◽

Vol 7 (1) ◽

pp. 2-23 ◽

Cited By ~ 1

Author(s):

Yuji Sato ◽

Mikiko Sato

Keyword(s):

Graphics Processing Units ◽

Fault Tolerant ◽

Optimal Solution ◽

New Approach ◽

Content Type ◽

Distributed Genetic Algorithms ◽

Distributed Genetic Algorithm ◽

Many Core ◽

Graphics Processing ◽

Application Programs

Purpose – The purpose of this paper is to propose a fault-tolerant technology for increasing the durability of application programs when evolutionary computation is performed by fast parallel processing on many-core processors such as graphics processing units (GPUs) and multi-core processors (MCPs). Design/methodology/approach – For distributed genetic algorithm (GA) models, the paper proposes a method where an island's ID number is added to the header of data transferred by this island for use in fault detection. Findings – The paper has shown that the processing time of the proposed idea is practically negligible in applications and also shown that an optimal solution can be obtained even with a single stuck-at fault or a transient fault, and that increasing the number of parallel threads makes the system less susceptible to faults. Originality/value – The study described in this paper is a new approach to increase the sustainability of application program using distributed GA on GPUs and MCPs.

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