HEGrid: A High Efficient Multi-Channel Radio Astronomical Data Gridding Framework in Heterogeneous Computing Environments

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 Gridding framework, HEGrid, by overcoming the above limitations. Specifically, we propose and construct the gridding pipeline in heterogeneous computing environments and achieve multi-pipeline concurrency for high performance multi-channel processing. Furthermore, we propose pipeline-based co-optimization to alleviate the potential negative performance impact of possible intra- and inter-pipeline low computation and I/O utilization, including component share-based redundancy elimination, thread-level data reuse and overlapping I/O and computation. Our experiments are based on both simulated datasets and actual FAST observational datasets. The results show that HEGrid outperforms other state-of-the-art gridding frameworks by up to 5.5x and has robust hardware portability, including AMD Radeon Instinct GPU and NVIDIA GPU.