A Fast Scalable Implicit Solver for Nonlinear Time-Evolution Earthquake City Problem on Low-Ordered Unstructured Finite Elements with Artificial Intelligence and Transprecision Computing
A Fast Scalable Implicit Solver for Nonlinear Time-Evolution Earthquake City Problem on Low-Ordered Unstructured Finite Elements with Artificial Intelligence and Transprecision Computing
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DOI：
10.1109/sc.2018.00052
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发表时间：
2018-11
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通讯作者：
T. Ichimura;K. Fujita;Takuma Yamaguchi;Akira Naruse;J. Wells;T. Schulthess;T. Straatsma;Christopher Z
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作者：
T. Ichimura;K. Fujita;Takuma Yamaguchi;Akira Naruse;J. Wells;T. Schulthess;T. Straatsma;Christopher Z
为了解决城市地区因地震而出现的问题，我们提出了一种利用人工智能（AI）和精确计算来加速非线性动态低阶非结构化有限元求解器的方法。利用人工智能提高迭代求解器的收敛性，使算法计数比标准求解器减少5.56倍；利用FP16-FP21-FP32-FP64计算加速稀疏矩阵向量积核，在Summit上实现了71.4%的峰值FP64性能。这比标准求解器快25.3倍，比最先进的SC14戈登贝尔决赛求解器快3.99倍。此外，所提出的求解器具有很高的可扩展性（在K计算机上为88.8%，在Piz paint上为89.5%），在Summit的4096个节点上实现了14.7%的峰值FP64性能。利用人工智能和FP16算法提出的方法对加速用于地震城市模拟以及各个领域的其他隐式求解器具有重要意义。
To address problems that occur due to earthquake in urban areas, we propose a method that utilizes artificial intelligence (AI) and transprecision computing to accelerate a nonlinear dynamic low-order unstructured finite-element solver. The AI is used to improve the convergence of iterative solver leading to 5.56-fold reduction in arithmetic count from a standard solver, and FP16-FP21-FP32-FP64 computing is used to accelerate the sparse matrix-vector product kernel, which demonstrated 71.4% peak FP64 performance on Summit. This is 25.3 times faster than a standard solver and 3.99 times faster than the state-of-the-art SC14 Gordon Bell Finalist solver. Furthermore, the proposed solver demonstrated high scalability (88.8% on the K computer and 89.5% on Piz Daint), leading to 14.7% peak FP64 performance on 4096 nodes of Summit. The proposed approach utilizing AI and FP16 arithmetic has implications for accelerating other implicit solvers used for earthquake city simulations as well as various fields.