AuthorsC. Jarvis, G. T. Lines, J. Langguth, K. Nakajima and X. Cai
TitleCombining algorithmic rethinking and AVX-512 intrinsics for efficient simulation of subcellular calcium signaling
AfilliationScientific Computing
Project(s)Meeting Exascale Computing with Source-to-Source Compilers, Department of High Performance Computing
Publication TypeProceedings, refereed
Year of Publication2019
Conference NameInternational Conference on Computational Science (ICCS 2019)
Publisher Springer

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.

Citation Key26985

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