tsdownsample: High-performance time series downsampling for scalable visualization
Interactive line chart visualizations greatly enhance the effective exploration of large time series. Although downsampling has emerged as a well-established approach to enable efficient interactive visualization of large datasets, it is not an inherent feature in most visualization tools. Furthermo...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-02-01
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| Series: | SoftwareX |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352711025000123 |
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| Summary: | Interactive line chart visualizations greatly enhance the effective exploration of large time series. Although downsampling has emerged as a well-established approach to enable efficient interactive visualization of large datasets, it is not an inherent feature in most visualization tools. Furthermore, there is no library offering a convenient interface for high-performance implementations of prominent downsampling algorithms. To address these shortcomings, we present tsdownsample, an open-source Python package specifically designed for CPU-based, in-memory time series downsampling. Our library focuses on performance and convenient integration, offering optimized implementations of leading downsampling algorithms. We achieve this optimization by leveraging low-level Single Instruction, Multiple Data (SIMD) instructions and multithreading capabilities in Rust. In particular, SIMD instructions were employed to optimize the argmin and argmax operations. This SIMD optimization, along with some algorithmic tricks, proved crucial in enhancing the performance of various downsampling algorithms. We evaluate the performance of tsdownsample and demonstrate its interoperability with an established visualization framework. Our performance benchmarks indicate that the algorithmic runtime of tsdownsample approximates the CPU’s memory bandwidth. This work marks a significant advancement in bringing high-performance time series downsampling to the Python ecosystem, enabling scalable visualization. |
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| ISSN: | 2352-7110 |