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Using State-of-the-Art Sparse Matrix Optimizations for Accelerating the Performance of Multiphysics Simulations

机译:使用最先进的稀疏矩阵优化,用于加速多麦体验模拟的性能

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Multiphysics simulations are at the core of modern Computer Aided Engineering (CAE) allowing the analysis of multiple, simultaneously acting physical phenomena. These simulations often rely on Finite Element Methods (FEM) and the solution of large linear systems which, in turn, end up in multiple calls of the costly Sparse Matrix-Vector Multiplication (SpM×V) kernel. The major-and mostly inherent-performance problem of the this kernel is its very low flop:byte ratio, meaning that the algorithm must retrieve a significant amount of data from the memory hierarchy in order to perform a useful operation. In modern hardware, where the processor speed has far overwhelmed that of the memory subsystem, this characteristic becomes an overkill.
机译:多职业模拟是现代计算机辅助工程(CAE)的核心,允许分析多个同时行动物理现象。这些模拟通常依赖于有限元方法(FEM)和大型线性系统的解决方案,其又最终以昂贵的稀疏矩阵 - 矢量乘法(SPM×V)内核的多个呼叫。此内核的主要和大多是固有的性能问题是它的非常低的浮点:字节比,这意味着算法必须从内存层次结构中检索大量数据以执行有用的操作。在现代硬件中,处理器速度远远忽视了内存子系统的速度,这种特性成为矫枉过正。

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