knor: A NUMA-Optimized In-Memory, Distributed and Semi-External-Memory k-means Library
Disa Mhembere, Da Zheng, Carey E. Priebe, Joshua T. Vogelstein and, Randal Burns

TL;DR
The paper introduces knor, a NUMA-optimized, scalable k-means library with in-memory, distributed, and semi-external memory modules, significantly improving performance and scalability for large datasets on modern hardware.
Contribution
It presents a novel NUMA-aware parallelization scheme and a versatile library that outperforms existing solutions for large-scale k-means clustering.
Findings
knor in-memory module boosts performance by over 10x on single machines
semi-external memory module scales to billions of points using SSDs
outperforms commercial distributed solutions by more than an order of magnitude
Abstract
k-means is one of the most influential and utilized machine learning algorithms. Its computation limits the performance and scalability of many statistical analysis and machine learning tasks. We rethink and optimize k-means in terms of modern NUMA architectures to develop a novel parallelization scheme that delays and minimizes synchronization barriers. The \textit{k-means NUMA Optimized Routine} (\textsf{knor}) library has (i) in-memory (\textsf{knori}), (ii) distributed memory (\textsf{knord}), and (iii) semi-external memory (\textsf{knors}) modules that radically improve the performance of k-means for varying memory and hardware budgets. \textsf{knori} boosts performance for single machine datasets by an order of magnitude or more. \textsf{knors} improves the scalability of k-means on a memory budget using SSDs. \textsf{knors} scales to billions of points on a single machine, using…
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Taxonomy
TopicsCaching and Content Delivery · Stochastic Gradient Optimization Techniques · Advanced Data Storage Technologies
