k-Means Clustering on Two-Level Memory Systems

Bender, Michael A.; Berry, Jonathan W.; Hammond, Simon David; Moore, Branden J.; Moseley, Benjamin; Phillips, Cynthia A.

doi:10.1145/2818950.2818977

Cited by 15 publications

(22 citation statements)

References 28 publications

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“…• Hardware Acceleration. Parallelization [73], GPU [72], and cache [23] can accelerate it at physical level. [20,22].…”

Section: Accelerationmentioning

confidence: 99%

On the Efficiency of K-Means Clustering: Evaluation, Optimization, and Algorithm Selection

Wang,

Sun,

Bao

2020

Preprint

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This paper presents a thorough evaluation of the existing methods that accelerate Lloyd's algorithm for fast 𝑘-means clustering. To do so, we analyze the pruning mechanisms of existing methods, and summarize their common pipeline into a unified evaluation framework UniK. UniK embraces a class of well-known methods and enables a fine-grained performance breakdown. Within UniK, we thoroughly evaluate the pros and cons of existing methods using multiple performance metrics on a number of datasets. Furthermore, we derive an optimized algorithm over UniK, which effectively hybridizes multiple existing methods for more aggressive pruning. To take this further, we investigate whether the most efficient method for a given clustering task can be automatically selected by machine learning, to benefit practitioners and researchers.

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“…• Hardware Acceleration. Parallelization [73], GPU [72], and cache [23] can accelerate it at physical level. [20,22].…”

Section: Accelerationmentioning

confidence: 99%

On the Efficiency of K-Means Clustering: Evaluation, Optimization, and Algorithm Selection

Wang,

Sun,

Bao

2020

Preprint

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“…More recent implementations of Lloyd's algorithm were developed for architectures used in modern supercomputers. In [33] a version for systems using a high bandwidth scratchpad DRAM, which is physically bonded to a die containing compute cores, was proposed. An example of such architecture was discontinued Intel Knights Landing many-core processor.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid MPI/OpenMP Parallelization of $K$ -Means Algorithms Accelerated Using the Triangle Inequality

Kwedlo

Czochanski

2019

IEEE Access

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The standard formulation of the K -means clustering (Lloyd's method) performs many unnecessary distance calculations. In this paper, we focus on four approaches that use the triangle inequality to avoid unnecessary distance calculations. These approaches are Drake's, Elkan's, Annulus, and Yinyang algorithms. We propose a hybrid MPI/OpenMP parallelization of these algorithms in which the dataset and the corresponding data structures storing bounds on distances are evenly divided among MPI processes. Then, in the assignment step of a K -means iteration, each MPI process computes the assignment of its portion of data using OpenMP threads. In the update step of the iteration, the cluster centroids are computed using a hierarchical all-reduce operation. In the computational experiments, we compared the strong scalability of these four algorithms with the scalability of Lloyd's algorithm, parallelized using the same approach. The results indicate that all four algorithms maintain an advantage in computing time over Lloyd's algorithm. A comparison with two software packages, whose sources are publicly available, in the same computing environment, shows that our implementations are more efficient.

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“…These atomic operations are overlapped with main computation to increase the execution efficiency. K-means, a popular machine learning algorithm, is shown to benefit from higher bandwidth achieved by physically bonding the memory to the package containing processing elements [11]. Another proposal [13] is to use content addressable memories with hamming distance units in the logic layer to minimize the impact of significant data movement in k-nearest neighbours.…”

Section: Applications Of Pimmentioning

confidence: 99%

“…The umbrella of NDP covers 2D-integrated Processing-In-Memory, 3D-stacked Processing-In-Memory (PIM) and In-Storage Processing (ISP). Existing studies show efficacy of processingin-memory (PIM) approach for simple map-reduce applications [16,28], graph analytics [6,25], machine learning applications [11,20] and SQL queries [24,34]. Researchers also show the potential of processing in non-volatile memories for I/O bound big data applications [12,30,33].…”

Section: Introductionmentioning

confidence: 99%

Identifying the potential of near data processing for apache spark

Awan

Ohara

Ayguadé

et al. 2017

Proceedings of the International Symposium on Memory Systems

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While cluster computing frameworks are continuously evolving to provide real-time data analysis capabilities, Apache Spark has managed to be at the forefront of big data analytics for being a unified framework for both, batch and stream data processing. There is also a renewed interest in Near Data Processing (NDP) due to technological advancement in the last decade. However, it is not known if NDP architectures can improve the performance of big data processing frameworks such as Apache Spark. In this paper, we build the case of NDP architecture comprising programmable logic based hybrid 2D integrated processing-in-memory and instorage processing for Apache Spark, by extensive profiling of Apache Spark based workloads on Ivy Bridge Server.

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k-Means Clustering on Two-Level Memory Systems

Cited by 15 publications

References 28 publications

On the Efficiency of K-Means Clustering: Evaluation, Optimization, and Algorithm Selection

On the Efficiency of K-Means Clustering: Evaluation, Optimization, and Algorithm Selection

A Hybrid MPI/OpenMP Parallelization of $K$ -Means Algorithms Accelerated Using the Triangle Inequality

Identifying the potential of near data processing for apache spark

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