Energy Consumption of Algorithms for Solving the Compressible Navier-Stokes Equations on CPU’s, GPU’s and KNL’s
Energy Consumption of Algorithms for Solving the Compressible Navier-Stokes Equations on CPU’s, GPU’s and KNL’s
复制标题
CPU、GPU 和 KNL 上求解可压缩纳维-斯托克斯方程的算法的能耗
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
N. Sandham
中科院分区：
文献类型：
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作者：
S. P. Jammy;C. Jacobs;D. J. Lusher;N. Sandham
除了高性能计算系统中常见的硬件墙时间限制外，未来的系统很可能还将受到能源预算的限制。本文在Intel Ivy Bridge CPU节点、Intel Xeon Phi Knight Landing处理器和NVIDIA Tesla K40c图形处理器上对不同计算和存储强度的有限差分算法的能效和运行时间进行了评估。将离散化的导数存储到全局数组以用于解决方案改进的传统方法在能量消耗和运行时间方面被发现是低效的。相比之下，一类算法，其中离散化的导数动态求值或存储为线程/进程局部变量(产生高计算强度)，在能耗和运行时方面都是最优的。在所考虑的三种硬件体系结构上，与内存密集型算法相比，高计算密集型算法的∼2加速和∼2节能。与功耗无关，能耗与运行时间成正比，并且与∼节点上的相同算法相比，该图形处理器具有5倍的节能。
In addition to the hardware wall-time restrictions commonly seen in highperformance computing systems, it is likely that future systems will also be constrained by energy budgets. In the present work, finite difference algorithms of varying computational and memory intensity are evaluated with respect to both energy efficiency and runtime on an Intel Ivy Bridge CPU node, an Intel Xeon Phi Knights Landing processor, and an NVIDIA Tesla K40c GPU. The conventional way of storing the discretised derivatives to global arrays for solution advancement is found to be inefficient in terms of energy consumption and runtime. In contrast, a class of algorithms in which the discretised derivatives are evaluated on-the-fly or stored as thread-/process-local variables (yielding high compute intensity) is optimal both with respect to energy consumption and runtime. On all three hardware architectures considered, a speed-up of ∼ 2 and an energy saving of ∼ 2 are observed for the high compute intensive algorithms compared to the memory intensive algorithm. The energy consumption is found to be proportional to runtime, irrespective of the power consumed and the GPU has an energy saving of ∼ 5 compared to the same algorithm on a CPU node.
具有不同计算量和内存强度的显式有限差分算法的性能评估
影响因子：
3.3
作者：
Jammy S
通讯作者：
Jammy S