G-DBSCAN: A GPU Accelerated Algorithm for Density-based Clustering

Procedia Computer Science, 2016

Due the recent increase of the volume of data that has been generated, organizing this data has become one of the biggest problems in Computer Science. Among the different strategies propose to deal efficiently and effectively for this purpose, we highlight those related to clustering, more specifically, density-based clustering strategies, which stands out for its ability to define clusters of arbitrary shape and the robustness to deal with the presence of data noise, such as DBSCAN and OPTICS. However, these algorithms are still a computational challenge since they are distance-based proposals. In this work we present a new approach to make OPTICS feasible based on data indexing strategy. Although the simplicity with which the data are indexed, using graphs, it allows explore various parallelization opportunities, which were explored using graphic processing unit (GPU). Based on this structure, the complexity of OPTICS is reduced to O(E * logV) in the worst case, becoming itself very fast. In our evaluation we show that our proposal can be over 200x faster than its sequential version using CPU.

IJSRD, 2013

In today's digital world, Data sets are increasing exponentially. Statistical analysis using clustering in various scientific and engineering applications become very challenging issue for such large data set. Clustering on huge data set and its performance are two major factors demand for optimization. Parallelization is well-known approach to optimize performance. It has been observed from recent research work that GPU based parallelization help to achieve high degree of performance. Hence, this thesis focuses on optimizing hierarchical clustering algorithms using parallelization. It covers implementation of optimized algorithm on various parallel environments using Open MP on multi-core architecture and using CUDA on may-core architecture.

Proceedings of the combined workshops on UnConventional high performance computing workshop plus memory access workshop - UCHPC-MAW '09, 2009

In this paper, we report our research on using GPUs to accelerate clustering of very large data sets, which are common in today's real world applications. While many published works have shown that GPUs can be used to accelerate various general purpose applications with respectable performance gains, few attempts have been made to tackle very large problems. Our goal here is to investigate if GPUs can be useful accelerators even with very large data sets that cannot fit into GPU's onboard memory.

… Conference on Parallel …, 2008

Graphics Processing Units (GPU) have recently been the subject of attention in research as an efficient coprocessor for implementing many classes of highly parallel applications. The GPUs design is engineered for graphics applications, where many independent SIMD workloads are simultaneously dispatched to processing elements. While parallelism has been explored in the context of traditional CPU threads and SIMD processing elements, the principles involved in dividing the steps of a parallel algorithm for execution on GPU architectures remains a significant challenge. In this paper, we introduce a first step towards building an efficient GPU-based parallel implementation of a commonly used clustering algorithm called K-Means on an NVIDIA G80 PCI express graphics board using the CUDA processing extensions. Clustering algorithms are important for search, data mining, spam and intrusion detection applications. Modern desktop machines commonly include desktop search software that can be greatly enhanced by these advances, while low-power machines such as laptops can reduce power consumption by utilizing the video chip for these clustering and indexing operations. Our preliminary results show over a 13x performance improvement compared to a baseline 3 GHz Intel Pentium(R) based PC running the same algorithm with an average spec G80 graphics card, the NVIDIA 8600GT. The low cost of these video cards (less than $100 market price as of 2008), and the high performance gains suggest that our approach is both practical and economical for common applications.

DBSCAN, a density-based clustering method for multi-dimensional points, was proposed in 1996.

IEEE Transactions on Parallel and Distributed Systems, 2015

This paper presents a massively parallel implementation of a prominent network clustering algorithm, the structural clustering algorithm for networks (SCAN), on a graphical processing unit (GPU). SCAN is a fast and efficient clustering technique for finding hidden communities and isolating hubs/outliers within a network. However, for very large networks, it still takes considerable amount of time. With the introduction of massively parallel Compute Unified Device Architecture (CUDA) by Nvidia, applications properly employing GPUs are demonstrating high speed up. In current study, GPUSCAN, a CUDA based parallel implementation of SCAN, is presented. SCAN's computation steps have been carefully redesigned to run very efficiently on the GPU by transforming SCAN into a series of highly regular and independent concurrent operations. All intermediate data structures are created in the GPU to efficiently benefit from GPU's memory hierarchy. How these structures reformed and represented in the GPU memory hierarchy is illustrated. Now, through GPUSCAN, a large network or a batch of disjoint networks can be offloaded to the GPU for very fast and equivalent structural clustering. The performance of the GPU accelerated structural clustering has been shown to be much faster than the sequential CPU implementation. Both GPUSCAN and SCAN are tested on different size artificial and real-world networks. Results indicate that network becomes larger GPUSCAN significantly over performs SCAN. In tested datasets, speed-up of over 500-fold is achieved. For instance, calculating structural similarity and clustering of 5.5 million edges of the California road network in GPUSCAN is 513-fold faster than the serial version of SCAN.

Concurrency and Computation: Practice and Experience, 2019

Classification and clustering techniques are used in different applications. Large-scale big data applications such as social networks analysis applications need to process large data chunks in a short time. Classification and clustering tasks in such applications consume a lot of processing time. Improving the performance of classification and clustering algorithms enhances the performance of applications that use such type of algorithms. This paper introduces an approach for exploiting the graphics processing unit (GPU) platform to improve the performance of classification and clustering algorithms. The proposed approach uses two GPUs implementations, which are the pure GPU or GPU-only implementation and the GPU-CPU hybrid implementation. The results show that the hybrid implementation, which optimizes the subtask scheduling for both the CPU and the GPU processing elements, outperforms the approach that uses only the GPU.

Iterative clustering algorithms based on Lloyds algorithm (often referred to as the k-means algorithm) have been used in a wide variety of areas, including graphics, computer vision, signal processing, compression, and computational geometry. We describe a method for accelerating many variants of iterative clustering by using programmable graphics hardware to perform the most computationally expensive portion of the work. In particular, we demonstrate significant speedups for k-means clustering (essential in vector quantization) and clustered principal component analysis. An additional contribution is a new hierarchical algorithm for k-means which performs less work than the brute-force algorithm, but which offers significantly more SIMD parallelism than the straightforward hierarchical approach.

2017 New York Scientific Data Summit (NYSDS), 2017

Clustering has become an unavoidable step in big data analysis. It may be used to arrange data into a compact format, making operations on big data manageable. However, clustering of big data requires not only the capability of handling data with large volume and high dimensionality, but also the ability to process streaming data, all of which are less developed in most current algorithms. Furthermore, big data processing is seldom interactive, which stands at conflict with users who seek answers immediately. The best one can do is to process incrementally, such that partial and, hopefully, accurate results can be available relatively quickly and are then progressively refined over time. We propose a clustering framework which uses Multi-Dimensional Scaling for layout and GPU acceleration to accomplish these goals. Our domain application is the clustering of mass spectral data of individual aerosol particles with 8 million data points of 450 dimensions each.

Density-based clustering forms the clusters of densely gathered objects separated by sparse regions. In this paper, we survey the previous and recent density-based clustering algorithms. DBSCAN [6], OPTICS [1], and DENCLUE [5, 6] are previous representative density-based clustering algorithms. Several recent algorithms such as PDBSCAN [8], CUDA-DClust [3], and GSCAN [7] have been proposed to improve the performance of DBSCAN. They make the most of multi-core CPUs and GPUs.