mirror of
https://github.com/RGBCube/uutils-coreutils
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108 lines
3 KiB
Markdown
108 lines
3 KiB
Markdown
<!-- spell-checker:ignore taskset -->
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# Performance Profiling Tutorial
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## Effective Benchmarking with Hyperfine
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[Hyperfine](https://github.com/sharkdp/hyperfine) is a powerful command-line benchmarking tool that allows you to measure and compare execution times of commands with statistical rigor.
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### Benchmarking Best Practices
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When evaluating performance improvements, always set up your benchmarks to compare:
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1. The GNU implementation as reference
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2. The implementation without the change
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3. The implementation with your change
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This three-way comparison provides clear insights into:
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- How your implementation compares to the standard (GNU)
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- The actual performance impact of your specific change
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### Example Benchmark
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First, you will need to build the binary in release mode. Debug builds are significantly slower:
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```bash
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cargo build --features unix --profile profiling
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```
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```bash
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# Three-way comparison benchmark
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hyperfine \
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--warmup 3 \
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"/usr/bin/ls -R ." \
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"./target/profiling/coreutils.prev ls -R ." \
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"./target/profiling/coreutils ls -R ."
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# can be simplified with:
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hyperfine \
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--warmup 3 \
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-L ls /usr/bin/ls,"./target/profiling/coreutils.prev ls","./target/profiling/coreutils ls" \
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"{ls} -R ."
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```
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```
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# to improve the reproducibility of the results:
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taskset -c 0
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```
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### Interpreting Results
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Hyperfine provides summary statistics including:
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- Mean execution time
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- Standard deviation
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- Min/max times
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- Relative performance comparison
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Look for consistent patterns rather than focusing on individual runs, and be aware of system noise that might affect results.
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## Using Samply for Profiling
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[Samply](https://github.com/mstange/samply) is a sampling profiler that helps you identify performance bottlenecks in your code.
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### Basic Profiling
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```bash
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# Generate a flame graph for your application
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samply record ./target/debug/coreutils ls -R
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# Profile with higher sampling frequency
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samply record --rate 1000 ./target/debug/coreutils seq 1 1000
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```
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The output using the `debug` profile might be easier to understand, but the performance characteristics may be somewhat different from `release` profile that we _actually_ care about.
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Consider using the `profiling` profile, that compiles in `release` mode but with debug symbols. For example:
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```bash
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cargo build --profile profiling -p uu_ls
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samply record -r 10000 target/profiling/ls -lR /var .git .git .git > /dev/null
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```
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## Workflow: Measuring Performance Improvements
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1. **Establish baselines**:
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```bash
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hyperfine --warmup 3 \
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"/usr/bin/sort large_file.txt" \
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"our-sort-v1 large_file.txt"
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```
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2. **Identify bottlenecks**:
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```bash
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samply record ./our-sort-v1 large_file.txt
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```
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3. **Make targeted improvements** based on profiling data
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4. **Verify improvements**:
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```bash
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hyperfine --warmup 3 \
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"/usr/bin/sort large_file.txt" \
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"our-sort-v1 large_file.txt" \
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"our-sort-v2 large_file.txt"
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```
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5. **Document performance changes** with concrete numbers
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```bash
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hyperfine --export-markdown file.md [...]
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```
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