Doing the Prospero-Challenge in RPython
a year ago
- #optimization
- #performance
- #rendering
- The Prospero Challenge involves rendering a 1024x1024 image from a mathematical formula with 7866 operations per pixel.
- The formula is in SSA-form, a linear sequence where each variable is assigned once, making it ideal for optimization.
- A baseline interpreter was implemented to parse and execute the program, but it was slow due to high interpretation overhead.
- Quadtrees were used to optimize rendering by subdividing the image and simplifying the formula via range analysis.
- A simple optimizer was written to perform interval analysis and peephole optimizations, though most added rules had minimal impact.
- A 'demanded information' optimization was introduced, focusing only on the sign of the result, which removed 25% of operations.
- Dead code elimination was implemented by marking used operations in a backward pass, improving efficiency.
- Random testing with Hypothesis ensured optimizer correctness by generating and minimizing test cases.
- Visualizations of the program and octree recursion were created to better understand the optimization process.
- A C implementation was developed to explore performance improvements using musttail, SIMD, and efficient struct packing.
- Performance tests showed the C version with demanded info optimizations was faster than the RPython version and competitive with Fidget.