Large-Scale Automatic K-Means Clustering for Heterogeneous Many-Core Supercomputer

Funding: UK EPSRC grants ”Discovery” EP/P020631/1, ”ABC: Adaptive Brokerage for the Cloud” EP/R010528/1.This article presents an automatic k-means clustering solution targeting the Sunway TaihuLight supercomputer. We first introduce a multilevel parallel partition approach that not only partitions by dataflow and centroid, but also by dimension, which unlocks the potential of the hierarchical parallelism in the heterogeneous many-core processor and the system architecture of the supercomputer. The parallel design is able to process large-scale clustering problems with up to 196,608 dimensions and over 160,000 targeting centroids, while maintaining high performance and high scalability. Furthermore, we propose an automatic hyper-parameter determination process for k-means clustering, by automatically generating and executing the clustering tasks with a set of candidate hyper-parameter, and then determining the optimal hyper-parameter using a proposed evaluation method. The proposed auto-clustering solution can not only achieve high performance and scalability for problems with massive high-dimensional data, but also support clustering without sufficient prior knowledge for the number of targeted clusters, which can potentially increase the scope of k-means algorithm to new application areas.PostprintPeer reviewe

An efficient K-means clustering algorithm for tall data

The analysis of continously larger datasets is a task of major importance in a wide variety of scientific fields. Therefore, the development of efficient and parallel algorithms to perform such an analysis is a a crucial topic in unsupervised learning. Cluster analysis algorithms are a key element of exploratory data analysis and, among them, the K-means algorithm stands out as the most popular approach due to its easiness in the implementation, straightforward parallelizability and relatively low computational cost. Unfortunately, the K-means algorithm also has some drawbacks that have been extensively studied, such as its high dependency on the initial conditions, as well as to the fact that it might not scale well on massive datasets. In this article, we propose a recursive and parallel approximation to the K-means algorithm that scales well on the number of instances of the problem, without affecting the quality of the approximation. In order to achieve this, instead of analyzing the entire dataset, we work on small weighted sets of representative points that are distributed in such a way that more importance is given to those regions where it is harder to determine the correct cluster assignment of the original instances. In addition to different theoretical properties, which explain the reasoning behind the algorithm, experimental results indicate that our method outperforms the state-of-the-art in terms of the trade-off between number of distance computations and the quality of the solution obtained

Large-scale hierarchical k-means for heterogeneous many-core supercomputers

Funding: J.Thomson and T.Yu are supported by the EPSRC grants ”Discovery” EP/P020631/1, ”ABC: Adaptive Brokerage for the Cloud” EP/R010528/1, and EU Horizon 2020 grant Team-Play: ”Time, Energy and security Analysis for Multi/Many-core heterogenous PLAtforms” (ICT-779882, https://teamplay- h2020.eu)This paper presents a novel design and implementation of k-means clustering algorithm targeting the Sunway TaihuLight supercomputer. We introduce a multi-level parallel partition approach that not only partitions by dataflow and centroid, but also by dimension. Our multi-level (nkd) approach unlocks the potential of the hierarchical parallelism in the SW26010 heterogeneous many-core processor and the system architecture of the supercomputer. Our design is able to process large-scale clustering problems with up to 196,608 dimensions and over 160,000 targeting centroids, while maintaining high performance and high scalability, significantly improving the capability of k-means over previous approaches. The evaluation shows our implementation achieves performance of less than 18 seconds per iteration for a large-scale clustering case with 196,608 data dimensions and 2,000 centroids by applying 4,096 nodes (1,064,496 cores) in parallel, making k-means a more feasible solution for complex scenarios.Postprin

Heterogeneity-aware scheduling and data partitioning for system performance acceleration

Over the past decade, heterogeneous processors and accelerators have become increasingly prevalent in modern computing systems. Compared with previous homogeneous parallel machines, the hardware heterogeneity in modern systems provides new opportunities and challenges for performance acceleration. Classic operating systems optimisation problems such as task scheduling, and application-specific optimisation techniques such as the adaptive data partitioning of parallel algorithms, are both required to work together to address hardware heterogeneity. Significant effort has been invested in this problem, but either focuses on a specific type of heterogeneous systems or algorithm, or a high-level framework without insight into the difference in heterogeneity between different types of system. A general software framework is required, which can not only be adapted to multiple types of systems and workloads, but is also equipped with the techniques to address a variety of hardware heterogeneity. This thesis presents approaches to design general heterogeneity-aware software frameworks for system performance acceleration. It covers a wide variety of systems, including an OS scheduler targeting on-chip asymmetric multi-core processors (AMPs) on mobile devices, a hierarchical many-core supercomputer and multi-FPGA systems for high performance computing (HPC) centers. Considering heterogeneity from on-chip AMPs, such as thread criticality, core sensitivity, and relative fairness, it suggests a collaborative based approach to co-design the task selector and core allocator on OS scheduler. Considering the typical sources of heterogeneity in HPC systems, such as the memory hierarchy, bandwidth limitations and asymmetric physical connection, it proposes an application-specific automatic data partitioning method for a modern supercomputer, and a topological-ranking heuristic based schedule for a multi-FPGA based reconfigurable cluster. Experiments on both a full system simulator (GEM5) and real systems (Sunway Taihulight Supercomputer and Xilinx Multi-FPGA based clusters) demonstrate the significant advantages of the suggested approaches compared against the state-of-the-art on variety of workloads."This work is supported by St Leonards 7th Century Scholarship and Computer Science PhD funding from University of St Andrews; by UK EPSRC grant Discovery: Pattern Discovery and Program Shaping for Manycore Systems (EP/P020631/1)." -- Acknowledgement

Compressed K - Means for large-scale clustering

Copyright © 2017, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. Large-scale clustering has been widely used in many applications, and has received much attention. Most existing clustering methods suffer from both expensive computation and memory costs when applied to large-scale datasets. In this paper, we propose a novel clustering method, dubbed compressed k-means (CKM), for fast large-scale clustering. Specifically, high-dimensional data are compressed into short binary codes, which are well suited for fast clustering. CKM enjoys two key benefits: 1) storage can be significantly reduced by representing data points as binary codes; 2) distance computation is very efficient using Hamming metric between binary codes. We propose to jointly learn binary codes and clusters within one framework. Extensive experimental results on four large-scale datasets, including two million-scale datasets demonstrate that CKM outperforms the state-of-theart large-scale clustering methods in terms of both computation and memory cost, while achieving comparable clustering accuracy