# Progressive Dimensionality Reduction

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.