⚡ Performance Enhancement: data.table, future, furrr, Rcpp

[pjt222]

Summary

Enhance rendering performance using modern R performance packages.

Current State

• Rendering works but could be faster
• Using base R for computation
• Single-threaded execution

Proposed Enhancements

1. data.table

• Replace data frame operations with data.table for faster manipulation
• Memory-efficient operations on spiral point data

2. future + furrr

• Parallel computation for Voronoi tessellation
• Non-blocking async operations
• Better utilization of multi-core CPUs

3. Rcpp

• C++ implementation of hot paths:
  • Fermat spiral point generation
  • Coordinate transformations
  • Bounded cell filtering
• Potential 10-100x speedup for computation-heavy operations

Implementation Plan

• Benchmark current performance baseline
• Add data.table for data manipulation
• Implement future/furrr for parallel Voronoi computation
• Profile and identify Rcpp candidates
• Create Rcpp implementations for bottlenecks
• Benchmark improvements

Dependencies to Add

renv::install(c("data.table", "future", "furrr", "Rcpp"))

Files to Modify

• R/utils/spiral_math.R - Core computation
• R/modules/ui_plot.R - Rendering pipeline
• New: src/ folder for Rcpp code

Labels

enhancement, performance