HEGrid: A High Efficient Multi-Channel Radio Astronomical Data Gridding Framework in Heterogeneous Computing Environments
Hao Wang, Ce Yu, Jian Xiao, Shanjiang Tang, Min Long, and Ming Zhu

TL;DR
HEGrid is a novel multi-channel radio astronomical data gridding framework designed for heterogeneous computing environments, significantly improving processing efficiency and supporting high concurrency for large-scale telescope data.
Contribution
The paper introduces HEGrid, a high-performance, multi-pipeline, heterogeneous computing framework for radio astronomy data gridding, overcoming limitations of existing CPU-only solutions.
Findings
HEGrid outperforms existing frameworks by up to 5.5x in speed.
Supports multi-channel processing on AMD and NVIDIA GPUs.
Demonstrates robustness and portability across hardware platforms.
Abstract
The challenge to fully exploit the potential of existing and upcoming scientific instruments like large single-dish radio telescopes is to process the collected massive data effectively and efficiently. As a "quasi 2D stencil computation" with the "Moore neighborhood pattern," gridding is the most computationally intensive step in data reduction pipeline for radio astronomy studies, enabling astronomers to create correct sky images for further analysis. However, the existing gridding frameworks can either only run on multi-core CPU architecture or do not support high-concurrency, multi-channel data gridding. Their performance is then limited, and there are emerging needs for innovative gridding frameworks to process data from large single-dish radio telescopes like the Five-hundred-meter Aperture Spherical Telescope (FAST). To address those challenges, we developed a High Efficient…
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Taxonomy
TopicsDistributed and Parallel Computing Systems
