A thermal simulation methodology derived from the proper orthogonal decomposition (POD) and the Galerkin projection (GP), hereafter referred to as PODTherm-GP, is evaluated in terms of its efficiency and accuracy in a multi-core CPU. The GP projects the heat transfer equation onto a mathematical space whose basis functions are generated from thermal data enabled by the POD learning algorithm. The thermal solution data are collected from FEniCS using the finite element method (FEM) accounting for appropriate parametric variations. The GP incorporates physical principles of heat transfer in the methodology to reach high accuracy and efficiency. The dynamic power map for the CPU in FEM thermal simulation is generated from gem5 and McPACT, together with the SPLASH-2 benchmarks as the simulation workload. It is shown that PODTherm-GP offers an accurate thermal prediction of the CPU with a resolution as fine as the FEM. It is also demonstrated that PODTherm-GP is capable of predicting the dynamic thermal profile of the chip with a good accuracy beyond the training conditions. Additionally, the approach offers a reduction in degrees of freedom by more than 5 orders of magnitude and a speedup of 4 orders, compared to the FEM.

翻译：基于本征正交分解（POD）与伽辽金投影（GP）的热模拟方法（以下简称PODTherm-GP）在多核CPU中的效率和准确性被评估。GP将热传导方程投影到一个数学空间，其基函数由POD学习算法生成的热数据中提取。热解数据通过有限元法（FEM）从FEniCS收集，并考虑适当的参数变化。GP将热传导的物理原理融入该方法，以实现高精度和高效率。FEM热模拟中的CPU动态功耗图由gem5和McPACT生成，并采用SPLASH-2基准测试作为模拟负载。研究表明，PODTherm-GP能够以与FEM相同的分辨率对CPU进行精确的热预测。同时证明，PODTherm-GP在训练条件之外，仍能以较高精度预测芯片的动态热分布。此外，与FEM相比，该方法可将自由度降低超过5个数量级，并实现4个数量级的加速。