Evaluating and Improving Weak Scalability Analysis of Visualization Algorithms

2026-08-08

AI Summary

Traditional weak scalability analysis in visualization algorithm evaluation often suffers from inconsistent workload growth due to variations in data complexity or output size, making it difficult to accurately reflect an algorithm’s true scalability. This work systematically evaluates the impact of various data scaling strategies on the weak scalability of diverse visualization algorithms, revealing significant differences in their applicability across algorithms and datasets. To address this issue, the study proposes an improved methodology tailored for shared-memory environments that effectively mitigates inconsistencies in workload growth. Experimental results demonstrate that the proposed approach substantially enhances the consistency and accuracy of weak scalability assessments, offering a more reliable methodological foundation for evaluating the scalability of large-scale visualization algorithms.

Abstract

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.

Problem

• weak scalability
• visualization algorithms
• workload characterization
• data scaling
• large-scale datasets

Innovation

• weak scalability
• visualization algorithms
• data scaling
• workload consistency
• shared-memory