标题： 通过异构计算进行宽带扩展引力波爆发的深度搜索 显示英文标题

标题： Deep searches for broadband extended gravitational-wave emission bursts by heterogeneous computing

摘要： 我们提出了一种异构搜索算法，用于宽带扩展引力波辐射（BEGE），这预期来自伽马射线暴和高能核心坍缩超新星。 它通过在 $(f,\dot{f})$-平面上搜索长时间持续的爆发，通过在 {\em 图形处理器单元} (GPU) 上对数百万个一秒钟持续的啁啾模板库进行匹配滤波，以缓慢耗尽内部引擎的能量储备。 利用LIGO S6数据在350-2000 Hz范围内的近高斯噪声，使用帕塞瓦尔定理来预测滤波器输出的标准差 $\sigma$。 超过 $\sigma$倍的尾部被传回到 {\em 中央处理器} (CPU)。 该算法总体效率约为65%，归一化到快速傅里叶变换（FFT）。 在16秒持续的数据段 $(N=2^{16}$ 样本）上，每秒进行约一百万次相关运算，实现了比实时分析更好的性能，在大约十几个GPU的集群上运行。 我们展示了其在捕捉高频硬件LIGO注入中的应用。 该算法作为当前观测运行中档案数据和实时分析的深度全天空搜索的起点。 显示英文摘要

摘要： We present a heterogeneous search algorithm for broadband extended gravitational-wave emission (BEGE), expected from gamma-ray bursts and energetic core-collapse supernovae. It searches the $(f,\dot{f})$-plane for long duration bursts by inner engines slowly exhausting their energy reservoir by matched filtering on a {\em Graphics Processor Unit} (GPU) over a template bank of millions of one-second duration chirps. Parseval's Theorem is used to predict the standard deviation $\sigma$ of filter output, taking advantage of near-Gaussian noise in LIGO S6 data over 350-2000 Hz. Tails exceeding a mulitple of $\sigma$ are communicated back to a {\em Central Processing Unit} (CPU). This algorithm attains about 65\% efficiency overall, normalized to the Fast Fourier Transform (FFT). At about one million correlations per second over data segments of 16 s duration $(N=2^{16}$ samples), better than real-time analysis is achieved on a cluster of about a dozen GPUs. We demonstrate its application to the capture of high frequency hardware LIGO injections. This algorithm serves as a starting point for deep all-sky searches in both archive data and real-time analysis in current observational runs.