利用RICK加速射电天文成像
Accelerating radio astronomy imaging with RICK
AI总结：
本文提出RICK代码，在现代HPC基础设施上利用多GPU加速实现w-stacking射电天文成像算法，通过完全在GPU内存运行最小化数据传输，显著缩短了运行时间。
摘要：
本文介绍了在现代高性能计算（HPC）基础设施上实现射电天文成像算法的方法，利用分布式内存并行机制并在多个GPU间进行全面加速。我们的代码名为RICK（Radio Imaging Code Kernels），能够以节点间和节点内的方式执行Offringa等人（2014）提出的w-stacking算法的主要步骤，特别是能够完全在GPU内存中运行，从而最小化CPU与GPU之间的数据传输次数。考虑到射电数据集规模巨大，这一特性（尤其是在多个GPU之间）至关重要。在详细描述了相对于Gheller等人（2023）提出的第一版代码的新实现之后，我们分析了代码在执行过程中每个步骤的性能。我们还讨论了针对该问题的加速方法的优缺点及其对代码整体行为的影响。只要计算资源量不超过问题规模所需的资源量，这种方法在运行时间上相比CPU版本代码就有显著改善：实际上，代码现在受限于通信开销，而计算量则因加速器的能力而大幅降低。
英文摘要：
This paper presents an implementation of radio astronomy imaging algorithms on modern High Performance Computing (HPC) infrastructures, exploiting distributed memory parallelism and acceleration throughout multiple GPUs. Our code, called RICK (Radio Imaging Code Kernels), is capable of performing the major steps of the w-stacking algorithm presented in Offringa et al. (2014) both inter- and intra-node, and in particular has the possibility to run entirely on the GPU memory, minimising the number of data transfers between CPU and GPU. This feature, especially among multiple GPUs, is critical given the huge sizes of radio datasets involved. After a detailed description of the new implementations of the code with respect to the first version presented in Gheller et al. (2023), we analyse the performances of the code for each step involved in its execution. We also discuss the pros and cons related to an accelerated approach to this problem and its impact on the overall behaviour of the code. Such approach to the problem results in a significant improvement in terms of runtime with respect to the CPU version of the code, as long as the amount of computational resources does not exceed the one requested by the size of the problem: the code, in fact, is now limited by the communication costs, with the computation that gets heavily reduced by the capabilities of the accelerators.
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