Adaptive Parallel Downloader for Large Genomic Datasets
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866911096076500992 |
|---|---|
| author | Swargo, Rasman Mubtasim Arslan, Engin Arifuzzaman, Md |
| author_facet | Swargo, Rasman Mubtasim Arslan, Engin Arifuzzaman, Md |
| contents | Modern next-generation sequencing (NGS) projects routinely generate terabytes of data, which researchers commonly download from public repositories such as SRA or ENA. Existing download tools often employ static concurrency settings, leading to inefficient bandwidth utilization and prolonged download times due to their inability to adapt to dynamic network conditions. We introduce FastBioDL, a parallel file downloader designed for large biological datasets, featuring an adaptive concurrency controller. FastBioDL frames the download process as an online optimization problem, utilizing a utility function and gradient descent to adjust the number of concurrent socket streams in real-time dynamically. This approach maximizes download throughput while minimizing resource overhead. Comprehensive evaluations on public genomic datasets demonstrate that FastBioDL achieves up to $4x$ speedup over state-of-the-art tools. Moreover, in high-speed network experiments, its adaptive design was up to $2.1x$ faster than existing tools. By intelligently optimizing standard HTTP or FTP downloads on the client side, FastBioDL provides a robust and efficient solution for large-scale genomic data acquisition, democratizing high-performance data retrieval for researchers without requiring specialized commercial software or protocols. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_05511 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Adaptive Parallel Downloader for Large Genomic Datasets Swargo, Rasman Mubtasim Arslan, Engin Arifuzzaman, Md Distributed, Parallel, and Cluster Computing Modern next-generation sequencing (NGS) projects routinely generate terabytes of data, which researchers commonly download from public repositories such as SRA or ENA. Existing download tools often employ static concurrency settings, leading to inefficient bandwidth utilization and prolonged download times due to their inability to adapt to dynamic network conditions. We introduce FastBioDL, a parallel file downloader designed for large biological datasets, featuring an adaptive concurrency controller. FastBioDL frames the download process as an online optimization problem, utilizing a utility function and gradient descent to adjust the number of concurrent socket streams in real-time dynamically. This approach maximizes download throughput while minimizing resource overhead. Comprehensive evaluations on public genomic datasets demonstrate that FastBioDL achieves up to $4x$ speedup over state-of-the-art tools. Moreover, in high-speed network experiments, its adaptive design was up to $2.1x$ faster than existing tools. By intelligently optimizing standard HTTP or FTP downloads on the client side, FastBioDL provides a robust and efficient solution for large-scale genomic data acquisition, democratizing high-performance data retrieval for researchers without requiring specialized commercial software or protocols. |
| title | Adaptive Parallel Downloader for Large Genomic Datasets |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2508.05511 |