Enhancing Large Scale Brain Simulation with Optimized Parallel Algorithms on Fugaku Supercomputer

Tianxiang Lyu, Mitsuhisa Sato, Shigeki Aoki, Ryutaro Himeno, Zhe Sun

Abstract: The quest to understand the brain has progressed from experimental and theoretical phases to the burgeoning field of simulation neuroscience [1]. Driven by big data generated at multiple levels of brain organization, simulation neuroscience seems to be the only methodology for systematically investigation on multi-scale brain and the interactions within and across all these levels. However, simulating the whole human brain is one of the most ambitious scientific challenges in the 21st century [2], impeded by issues of scale and complexity. Current spiking neural networks based brain simulator, like the NEST simulator [3], face several operational challenges on high-performance computing systems, including low computing intensity, high memory consumption and so on. Addressing these challenges, we introduce an innovative framework optimized for the Fugaku computing system, demonstrating enhanced performance compared to the NEST simulator