# GPU Acceleration

NVIDIA and several Korean industry partners are pushing physical AI into production, spanning semiconductor engineering, autonomous driving and robot learning. In chip-scale thermal and mechanical analysis, Samsung Electronics combined AI-predicted effective material properties, coarser grids and GPU acceleration to cut one homogenization case from about 18 hours on a 32-core CPU fine-grid solve to 41 seconds on a GPU coarse-grid solve, roughly 1,600x faster; selected quantum chemical calculations on its sMOL platform ran up to 5x faster end to end.

NVIDIA and several Korean industry partners are pushing physical AI into production, spanning semiconductor engineering, autonomous driving and robot learning. In chip-scale thermal and mechanical analysis, Samsung Electronics combined AI-predicted effective material properties, coarser grids and GPU acceleration to cut one homogenization case from about 18 hours on a 32-core CPU fine-grid solve to 41 seconds on a GPU coarse-grid solve, roughly 1,600x faster; selected quantum chemical calculations on its sMOL platform ran up to 5x faster end to end.

GPUMAP splits UMAP's dimensionality-reduction pipeline into three interleavable operations, insert, update and embed, and runs them on a commodity GPU so that intermediate projections appear while computation continues, letting million-point datasets be inspected before the full run ends. On the 5-million-point HIGGS dataset, sequential RAPIDS UMAP produced its first projection only at 50.4 seconds, whereas GPUMAP produced an inspectable intermediate projection at 8.42 seconds and kept refining it; the cost is a total runtime about 1.6 times that of sequential execution.

GPUMAP splits UMAP's dimensionality-reduction pipeline into three interleavable operations, insert, update and embed, and runs them on a commodity GPU so that intermediate projections appear while computation continues, letting million-point datasets be inspected before the full run ends. On the 5-million-point HIGGS dataset, sequential RAPIDS UMAP produced its first projection only at 50.4 seconds, whereas GPUMAP produced an inspectable intermediate projection at 8.42 seconds and kept refining it; the cost is a total runtime about 1.6 times that of sequential execution.