Clubmark: 一种用于在NUMA架构上对聚类算法进行基准测试和分析的并行隔离框架
Clubmark: a Parallel Isolation Framework for Benchmarking and Profiling Clustering Algorithms on NUMA Architectures
AI总结：
本文提出Clubmark框架，用于在NUMA架构上对多种聚类算法进行并行基准测试和分析，提供细粒度的执行变量控制和对多种聚类算法的评估。
摘要：
存在大量的聚类和社区检测算法，这些算法是许多数据分析和探索系统的关键组成部分。然而，截至目前为止，尚不存在任何公开可用的统一基准测试框架，能够对多种聚类算法在广泛的人工合成和现实世界数据集上的并行基准测试进行评估。在本文中，我们介绍了Clubmark，一种新的可扩展框架，旨在通过为聚类算法及其在NUMA服务器上的评估提供一个并行隔离的基准测试平台来填补这一空白。Clubmark允许对各种执行变量（超时、内存消耗、CPU亲和性和缓存策略）进行细粒度控制，并支持在加权和非加权输入网络上评估多种聚类算法，包括多层次、层次化和重叠聚类技术，并内置了几种外在和内在度量的评估。我们的框架是开源的，并提供了一种一致且系统的方法来执行、评估和分析聚类技术，考虑了在最先进框架和基准测试系统中常缺失的多个方面。
英文摘要：
There is a great diversity of clustering and community detection algorithms, which are key components of many data analysis and exploration systems. To the best of our knowledge, however, there does not exist yet any uniform benchmarking framework, which is publicly available and suitable for the parallel benchmarking of diverse clustering algorithms on a wide range of synthetic and real-world datasets. In this paper, we introduce Clubmark, a new extensible framework that aims to fill this gap by providing a parallel isolation benchmarking platform for clustering algorithms and their evaluation on NUMA servers. Clubmark allows for fine-grained control over various execution variables (timeouts, memory consumption, CPU affinity and cache policy) and supports the evaluation of a wide range of clustering algorithms including multi-level, hierarchical and overlapping clustering techniques on both weighted and unweighted input networks with built-in evaluation of several extrinsic and intrinsic measures. Our framework is open-source and provides a consistent and systematic way to execute, evaluate and profile clustering techniques considering a number of aspects that are often missing in state-of-the-art frameworks and benchmarking systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
