Benchmarks
Binaries
cargo run --profile=profiling --bin=<BINARY> -- <ARGS>
builder
Generate a grid using GridDescriptor w.r.t. the specified arguments.
Arguments are the following: <N_CELL_X> <N_CELL_Y> <SPLIT>, with:
<N_CELL_X>: number of cell of the grid along the X axis<N_CELL_Y>: number of cell of the grid along the Y axis<SPLIT>: if non-empty, the program will build a grid of tris instead of quads
grisubal
Run the grisubal algorithm w.r.t. the specified arguments. Arguments are the following:
<INPUT_FILE> <LEN_CELL_X> <LEN_CELL_Y> <CLIP>, with:
<INPUT_FILE>: input geometry, passed as a VTK file input<LEN_CELL_X>: length of the cells of the overlay grid along the X axis<LEN_CELL_X>: length of the cells of the overlay grid along the Y axis<CLIP>:leftto clip the left side of the boundary,rightthe right side, anything else is a no-op
Benchmarks
cargo bench --bench <BENCHMARK>
The following benchmarks are available:
| Name | Type | Source file |
|---|---|---|
builder | Criterion | benches/builder/time.rs |
builder-grid-size | Criterion | benches/builder∕grid_size.rs |
fetch-icells | Criterion | benches/core/cmap2/fetch_icells.rs |
grisubal | Criterion | benches/grisubal/time.rs |
grisubal-grid-size | Criterion | benches/grisubal/grid_size.rs |
prof-cmap2-basic | Iai-callgrind | benches/core/cmap2/basic_ops.rs |
prof-cmap2-build | Iai-callgrind | benches/core/cmap2/constructors.rs |
prof-cmap2-sewing-unsewing | Iai-callgrind | benches/core/cmap2/link_and_sew.rs |
triangulate-quads | Criterion | benches/triangulate/quads.rs |
A detailed explanation about the purpose of each benchmark is provided at the beginning of their respective source files.
Scripts
Benchmarking
Both scripts provide an interactive menu with four options:
(0) all
(1) fixed-size profiling
(2) grid size scaling
(3) thread number scaling (not yet implmented)
builder.py
The script aggregates metrics about the grid building routines used by CMapBuilder. Data is collected from Criterion
(runtime benchmarks), perf (CPU profiling), flamegraph (visualization), heaptrack (memory analysis), and across
different grid sizes (from 128x128 to 8192x8192).
grisubal.py
The script aggregates metrics about the grisubal kernel. Data is collected from Criterion (runtime benchmarks),
perf (CPU profiling), flamegraph (visualization), and heaptrack (memory analysis). Additionally, internal timers
are implemented to measure time-per-sections of the algorithm. Measurements are done across different grid
granularities (from 0.1 to 1.0 in 0.1 increments).
Plotting
grisubal_plot.py
This script generates a pie chart from per-section timings sampled by the grisubal.py script.