Lossy data compression reduces communication time in hybrid time-parallel integrators
Lossy data compression reduces communication time in hybrid time-parallel integrators
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DOI：
10.1007/s00791-018-0293-2
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发表时间：
2018-05
影响因子：
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通讯作者：
L. Fischer;Sebastian Götschel;M. Weiser
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文献类型：
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作者：
L. Fischer;Sebastian Götschel;M. Weiser
求解初值问题的时间内并行方法是提高数值模拟并行性的一种手段。混合并行格式是求解一般非线性问题的最有效的方法之一。尽管通信时间隐藏在计算之后，但在某些情况下，通信对总运行时间有重大影响。在这里，我们提出了严格的，但不是尖锐的，混合parareal方法与不精确的通信，由于有损数据压缩的误差界，并推导出理论估计的压缩算法的并行效率的影响。这些和一些计算实验表明，压缩是一种可行的方法，使混合parareal计划的鲁棒性相对于低带宽设置。
Parallel-in-time methods for solving initial value problems are a means to increase the parallelism of numerical simulations. Hybrid parareal schemes interleaving the parallel-in-time iteration with an iterative solution of the individual time steps are among the most efficient methods for general nonlinear problems. Despite the hiding of communication time behind computation, communication has in certain situations a significant impact on the total runtime. Here we present strict, yet not sharp, error bounds for hybrid parareal methods with inexact communication due to lossy data compression, and derive theoretical estimates of the impact of compression on parallel efficiency of the algorithms. These and some computational experiments suggest that compression is a viable method to make hybrid parareal schemes robust with respect to low bandwidth setups.