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Merged
merged 2 commits into from
Aug 9, 2025
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Update benchmarking.jl definition of big #697

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a365e38
Update benchmarking.jl definition of big
ChrisRackauckas Aug 9, 2025
c1afe7a
Update autotune.md
ChrisRackauckas Aug 9, 2025
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4 changes: 2 additions & 2 deletions docs/src/tutorials/autotune.md
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Original file line number Diff line number Diff line change
Expand Up @@ -73,7 +73,7 @@ Control which matrix size ranges to test:
# :small - 20×20 to 100×100 (small problems)
# :medium - 100×100 to 300×300 (typical problems)
# :large - 300×300 to 1000×1000 (larger problems)
# :big - 10000×10000 to 100000×100000 (GPU/HPC scale)
# :big - 10000×1000 to 20000x20000 (GPU/HPC scale)

# Default: test tiny through large
results = autotune_setup() # uses [:tiny, :small, :medium, :large]
Expand Down Expand Up @@ -449,4 +449,4 @@ LinearSolveAutotune provides a comprehensive system for benchmarking and optimiz
- Performance visualization
- Preference setting for automatic algorithm selection (in development)

By running autotune and optionally sharing your results, you help improve LinearSolve.jl's performance for everyone in the Julia community.
By running autotune and optionally sharing your results, you help improve LinearSolve.jl's performance for everyone in the Julia community.
6 changes: 3 additions & 3 deletions lib/LinearSolveAutotune/src/benchmarking.jl
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[JuliaFormatter] reported by reviewdog 🐶

LinearSolve.jl/lib/LinearSolveAutotune/src/benchmarking.jl

Lines 186 to 190 in a365e38

- `:tiny` - 5:5:20 (for very small problems)
- `:small` - 20:20:100 (for small problems)
- `:medium` - 100:50:300 (for typical problems)
- `:large` - 300:100:1000 (for larger problems)
- `:big` - vcat(1000:2000:10000, 10000:5000:20000) (for very large/GPU problems)

Original file line number Diff line number Diff line change
Expand Up @@ -187,7 +187,7 @@ Size categories:
- `:small` - 20:20:100 (for small problems)
- `:medium` - 100:50:300 (for typical problems)
- `:large` - 300:100:1000 (for larger problems)
- `:big` - 10000:1000:100000 (for very large/GPU problems)
- `:big` - vcat(1000:2000:10000, 10000:5000:20000) (for very large/GPU problems)
"""
function get_benchmark_sizes(size_categories::Vector{Symbol})
sizes = Int[]
Expand All @@ -202,7 +202,7 @@ function get_benchmark_sizes(size_categories::Vector{Symbol})
elseif category == :large
append!(sizes, 300:100:1000)
elseif category == :big
append!(sizes, 10000:1000:100000)
append!(sizes, vcat(1000:2000:10000, 10000:5000:20000))
else
@warn "Unknown size category: $category. Skipping."
end
Expand Down Expand Up @@ -236,7 +236,7 @@ function categorize_results(df::DataFrame)
("small (20-100)", 21:100),
("medium (100-300)", 101:300),
("large (300-1000)", 301:1000),
("big (10000+)", 10000:typemax(Int))
("big (1000+)", 1000:typemax(Int))
]

# Get unique element types
Expand Down
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