Combining Algorithmic Rethinking and AVX-512 Intrinsics for Efficient Simulation of Subcellular Calcium Signaling
| Autor: | Kengo Nakajima, Chad Jarvis, Johannes Langguth, Glenn T. Lines, Xing Cai |
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| Rok vydání: | 2019 |
| Předmět: | |
| Zdroj: | Lecture Notes in Computer Science ISBN: 9783030227494 ICCS (5) |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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