可视化算法弱可扩展性分析的评估与改进
Evaluating and Improving Weak Scalability Analysis of Visualization Algorithms
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中文总结 AI 辅助
本研究针对可视化算法弱可扩展性分析的不准确问题，在多算法多数据集上分析常见缩放方法的适用性，提出共享内存环境下缓解工作负载增长不一致的方法，推动可扩展可视化研究的评估与报告。
中文摘要 AI 辅助
对大规模数据集可视化的研究传统上依赖于可扩展性的经验评估，以确定特定计算方法、算法策略或实现的有效性。弱可扩展性用于评估当问题规模和计算资源增加时算法的性能，是衡量方法在大规模场景下适用性的重要指标。然而，弱可扩展性研究需要足够大且规模不断增长的数据集，因此通常采用简单的缩放技术，从基础数据集生成更大的输入数据集以扩大问题规模。但许多可视化算法的工作负载不仅取决于输入规模，还受输入数据复杂度、输出规模等因素影响，导致归因于弱可扩展性的结果存在不准确之处。本研究在多种算法和数据集上分析了不同常见数据缩放方法，发现缩放方法的适用性因算法和数据集而异；提出了一种方法，可在共享内存环境中有效缓解不同缩放方法导致的工作负载增长不一致问题，旨在推动可扩展可视化研究的评估与报告讨论。
英文摘要
Research on visualizing large-scale datasets traditionally relies on empirical evaluation of scalability, determining the effectiveness of specific computation methods, algorithmic strategies, or implementations. Weak scalability, which assesses the algorithm's performance as problem size and computing resources increase, is a valuable indicator for a method's applicability at scale. However, sufficiently large data sets with increasing size are needed for weak scalability studies. To this end, it is customary to use simple scaling techniques to increase problem size by generating larger input data sets from a base data set. Nevertheless, many visualization algorithms' workload depends on factors beyond input size, such as input data complexity or output size, leading to inaccuracies in the attributed weak scalability. In this work, we highlight different common data scaling methods on multiple algorithms and data sets, recognizing that the suitability of scaling approaches varies across algorithms and data sets. We present a method that effectively mitigates the observed inconsistencies in workload increases for the different scaling methods in a shared-memory setting. With this work, we aim to further the discussion on how to evaluate and report scalable visualization research.
