@misc{15591,
  author = {Chad Jarvis and Glenn Lines and Johannes Langguth and Kengo Nakajima and Xing Cai},
  title = {Combining algorithmic rethinking and AVX-512 intrinsics for efficient simulation of subcellular calcium signaling},
  abstract = {Calcium signaling is vital for the contraction of the heart. Physiologically realistic simulation of this subcellular process requires nanometer resolutions and a complicated mathematical model of differential equations. Since the subcellular space is composed of several irregularly-shaped and intricately-connected physiological domains with distinct properties, one particular challenge is to correctly compute the diffusion-induced calcium fluxes between the physiological domains. The common approach is to pre-calculate the effective diffusion coefficients between all pairs of neighboring computational voxels, and store them in large arrays. Such a strategy avoids complicated if-tests when looping through the computational mesh, but suffers from substantial memory overhead. In this paper, we adopt a memory-efficient strategy that uses a small lookup table of diffusion coefficients. The memory footprint and traffic are both drastically reduced, while also avoiding the if-tests. However, the new strategy induces more instructions on the processor level. To offset this potential performance pitfall, we use AVX-512 intrinsics to effectively vectorize the code. Performance measurements on a Knights Landing processor and a quad-socket Skylake server show a clear performance advantage of the manually vectorized implementation that uses lookup tables, over the counterpart using coefficient arrays.},
  year = {2019},
  journal = {International Conference on Computational Science (ICCS 2019)},
  pages = {681-687},
  publisher = {Springer},
  doi = {10.1007/978-3-030-22750-0_66},
}