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In-Core LU-Decomposition of Symmetrical Dense MoM Matrix in WIPL-D Multi-GPU Solver

机译:WIPL-D多GPU求解器对称密度母矩阵的核心LU分解

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Acceleration of in-core LU decomposition of symmetrical dense MoM matrices by using multiple GPUs in parallel is presented in this paper. Memory limitations of GPUs are overcome by using block LU decomposition, where the entire system matrix is stored in CPU RAM, while only processed blocks are stored in GPU VRAM. The presented algorithm for LU decomposition enables highly efficient utilization of an arbitrary number of GPUs. Comparison of performance of up to 4 GTX 680 GPUs and up to 4 GTX 1080 Ti GPUs is shown. Presented results show that a symmetrical dense MoM matrix with 100 000 complex unknowns in single precision can be LU decomposed in about 3.5 minutes, on a personal computer equipped with 4 GTX 1080 Ti GPUs.
机译:本文提出了通过使用多个GPU的对称致密MOM矩阵的核心LU核心LU分解的加速。通过使用块LU分解来克服GPU的内存限制,其中整个系统矩阵存储在CPU RAM中,而仅处理的块存储在GPU VRAM中。 LU分解的算法使得能够高效利用任意数量的GPU。显示最多4GTX 680 GPU的性能和高达4GTX 1080 TI GPU的比较。提出的结果表明,在单一精度下具有100 000个复杂未知数的对称密集的MOM矩阵可以在大约3.5分钟内分解,位于配备4个GTX 1080 Ti GPU的个人计算机上。

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