Improve LinearSolveAutotune UI/UX

lib/LinearSolveAutotune/Project.toml

@@ -7,6 +7,7 @@ version = "1.0.0"
 LinearSolve = "7ed4a6bd-45f5-4d41-b270-4a48e9bafcae"
 BenchmarkTools = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf"
 Base64 = "2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"
+CPUSummary = "2a0fbf3d-bb9c-48f3-b0a9-814d99fd7ab9"
 DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
 GitHub = "bc5e4493-9b4d-5f90-b8aa-2b2bcaad7a26"
 Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
@@ -18,6 +19,7 @@ LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 Printf = "de0858da-6303-5e67-8744-51eddeeeb8d7"
 Dates = "ade2ca70-3891-5945-98fb-dc099432e06a"
 Test = "8dfed614-e22c-5e08-85e1-65c5234f0b40"
+ProgressMeter = "92933f4c-e287-5a05-a399-4b506db050ca"
 RecursiveFactorization = "f2c3362d-daeb-58d1-803e-2bc74f2840b4"
 blis_jll = "6136c539-28a5-5bf0-87cc-b183200dce32"
 LAPACK_jll = "51474c39-65e3-53ba-86ba-03b1b862ec14"
@@ -28,6 +30,7 @@ Metal = "dde4c033-4e86-420c-a63e-0dd931031962"
 LinearSolve = "3"
 BenchmarkTools = "1"
 Base64 = "1"
+CPUSummary = "0.2"
 DataFrames = "1"
 GitHub = "5"
 Plots = "1"
@@ -39,6 +42,7 @@ LinearAlgebra = "1"
 Printf = "1"
 Dates = "1"
 Test = "1"
+ProgressMeter = "1"
 RecursiveFactorization = "0.2"
 blis_jll = "0.9.0"
 LAPACK_jll = "3"

lib/LinearSolveAutotune/README.md

@@ -0,0 +1,173 @@
+# LinearSolveAutotune.jl
+
+Automatic benchmarking and tuning for LinearSolve.jl algorithms.
+
+## Quick Start
+
+```julia
+using LinearSolve, LinearSolveAutotune
+
+# Run benchmarks with default settings (small, medium, and large sizes)
+results = autotune_setup()
+
+# View a summary of results
+display(results)
+
+# Plot all benchmark results
+plot(results)
+
+# Share your results with the community (optional)
+share_results(results)
+```
+
+## Features
+
+- **Automatic Algorithm Benchmarking**: Tests all available LU factorization methods
+- **Multi-size Testing**: Flexible size categories from small to very large matrices
+- **Element Type Support**: Tests with Float32, Float64, ComplexF32, ComplexF64
+- **GPU Support**: Automatically detects and benchmarks GPU algorithms if available
+- **Performance Visualization**: Generate plots on demand with `plot(results)`
+- **Community Sharing**: Optional telemetry to help improve algorithm selection
+
+## Size Categories
+
+The package now uses flexible size categories:
+
+- `:tiny` - Matrices from 5×5 to 20×20 (very small problems)
+- `:small` - Matrices from 20×20 to 100×100 (small problems)
+- `:medium` - Matrices from 100×100 to 300×300 (typical problems)
+- `:large` - Matrices from 300×300 to 1000×1000 (larger problems)
+- `:big` - Matrices from 10000×10000 to 100000×100000 (GPU/HPC)
+
+## Usage Examples
+
+### Basic Benchmarking
+
+```julia
+# Default: small, medium, and large sizes
+results = autotune_setup()
+
+# Test all size ranges
+results = autotune_setup(sizes = [:small, :medium, :large, :big])
+
+# Large matrices only (for GPU systems)
+results = autotune_setup(sizes = [:large, :big])
+
+# Custom configuration
+results = autotune_setup(
+    sizes = [:medium, :large],
+    samples = 10,
+    seconds = 1.0,
+    eltypes = (Float64, ComplexF64)
+)
+
+# View results and plot
+display(results)
+plot(results)
+```
+
+### Sharing Results
+
+After running benchmarks, you can optionally share your results with the LinearSolve.jl community to help improve automatic algorithm selection:
+
+```julia
+# Share your benchmark results
+share_results(results)
+```
+
+## Setting Up GitHub Authentication
+
+To share results, you need GitHub authentication. We recommend using the GitHub CLI:
+
+### Method 1: GitHub CLI (Recommended)
+
+1. **Install GitHub CLI**
+   - macOS: `brew install gh`
+   - Windows: `winget install --id GitHub.cli`
+   - Linux: See [cli.github.com](https://cli.github.com/manual/installation)
+
+2. **Authenticate**
+   ```bash
+   gh auth login
+   ```
+   Follow the prompts to authenticate with your GitHub account.
+
+3. **Verify authentication**
+   ```bash
+   gh auth status
+   ```
+
+### Method 2: GitHub Personal Access Token
+
+If you prefer using a token:
+
+1. Go to [GitHub Settings > Tokens](https://github.com/settings/tokens/new)
+2. Add description: "LinearSolve.jl Telemetry"
+3. Select scope: `public_repo`
+4. Click "Generate token" and copy it
+5. In Julia:
+   ```julia
+   ENV["GITHUB_TOKEN"] = "your_token_here"
+   share_results(results, sysinfo, plots)
+   ```
+
+## How It Works
+
+1. **Benchmarking**: The `autotune_setup()` function runs comprehensive benchmarks of all available LinearSolve.jl algorithms across different matrix sizes and element types.
+
+2. **Analysis**: Results are analyzed to find the best-performing algorithm for each size range and element type combination.
+
+3. **Preferences**: Optionally sets Julia preferences to automatically use the best algorithms for your system.
+
+4. **Sharing**: The `share_results()` function allows you to contribute your benchmark data to the community collection at [LinearSolve.jl Issue #669](https://github.com/SciML/LinearSolve.jl/issues/669).
+
+## Privacy and Telemetry
+
+- Sharing results is **completely optional**
+- Only benchmark performance data and system specifications are shared
+- No personal information is collected
+- All shared data is publicly visible on GitHub
+- You can review the exact data before sharing
+
+## API Reference
+
+### `autotune_setup`
+
+```julia
+autotune_setup(;
+    sizes = [:small, :medium, :large],
+    set_preferences = true,
+    samples = 5,
+    seconds = 0.5,
+    eltypes = (Float32, Float64, ComplexF32, ComplexF64),
+    skip_missing_algs = false
+)
+```
+
+**Parameters:**
+- `sizes`: Vector of size categories to test
+- `set_preferences`: Update LinearSolve preferences
+- `samples`: Number of benchmark samples per test
+- `seconds`: Maximum time per benchmark
+- `eltypes`: Element types to benchmark
+- `skip_missing_algs`: Continue if algorithms are missing
+
+**Returns:**
+- `results`: AutotuneResults object containing benchmark data and system info
+
+### `share_results`
+
+```julia
+share_results(results)
+```
+
+**Parameters:**
+- `results`: AutotuneResults object from `autotune_setup`
+
+## Contributing
+
+Your benchmark contributions help improve LinearSolve.jl for everyone! By sharing results from diverse hardware configurations, we can build better automatic algorithm selection heuristics.
+
+## License
+
+Part of the SciML ecosystem. See LinearSolve.jl for license information.