A dataset summarising wall-clock performance for the main PCA entry points
in bigPCAcpp across matrices of increasing size. The benchmarks compare
the classical block-based decomposition, the streaming variant that writes
rotations as it progresses, the scalable stochastic PCA implementation, and
the base R stats::prcomp() routine for reference.
Format
A data frame with 360 rows and 14 columns:
- dataset
Human-readable size label ("small", "medium", "large", "xlarge").
- rows
Number of rows in the simulated matrix.
- cols
Number of columns in the simulated matrix.
- ncomp
Number of components requested.
- method
Computation strategy ("classical", "streaming", "scalable", or "prcomp").
- replicate
Replication index for repeated runs.
- user_time
User CPU time in seconds returned by
base::system.time().- system_time
System CPU time in seconds.
- elapsed
Elapsed (wall-clock) time in seconds.
- success
Logical flag indicating whether the run completed without errors.
- backend
Name of the backend reported by the result object when the computation succeeded.
- iterations
Recorded iteration count for iterative methods when available (otherwise
NA).- converged
Logical convergence flag for iterative methods when available.
- error
Error message captured for failed runs (otherwise
NA).
Source
Generated by scripts/run_benchmark.R using randomly simulated
in-memory matrices (no file-backed storage).
Examples
data(benchmark_results)