  The classical proper orthogonal decomposition (POD) with the Galerkin
projection (GP) has been revised for chip-level thermal simulation of
microprocessors with a large number of cores. An ensemble POD-GP methodology
(EnPOD-GP) is introduced to significantly improve the training effectiveness
and prediction accuracy by dividing a large number of heat sources into heat
source blocks (HSBs) each of which may contains one or a very small number of
heat sources. Although very accurate, efficient and robust to any power map,
EnPOD-GP suffers from intensive training for microprocessors with an enormous
number of cores. A local-domain EnPOD-GP model (LEnPOD-GP) is thus proposed to
further minimize the training burden. LEnPOD-GP utilizes the concepts of local
domain truncation and generic building blocks to reduce the massive training
data. LEnPOD-GP has been demonstrated on thermal simulation of NVIDIA Tesla
Volta GV100, a GPU with more than 13,000 cores including FP32, FP64, INT32, and
Tensor Cores. Due to the domain truncation for LEnPOD-GP, the least square
error (LSE) is degraded but is still as small as 1.6% over the entire space and
below 1.4% in the device layer when using 4 modes per HSB. When only the
maximum temperature of the entire GPU is of interest, LEnPOD-GP offers a
computing speed 1.1 million times faster than the FEM with a maximum error near
1.2 degrees over the entire simulation time.
