pokemon-battle-engine/benchmarks

Benchmarks Directory (benchmarks/)

This directory contains performance benchmarks and profiling tools for the Pokemon battle simulator using Google Benchmark.

Quick Start

To run the benchmarks:

# Build with benchmarks enabled
cmake -DBUILD_BENCHMARKS=ON ..
make -j$(nproc)

# Run benchmarks
./benchmarks/benchmarks

# Run specific benchmark
./benchmarks/benchmarks --benchmark_filter=BM_BattleSimulation

# Save results to JSON
./benchmarks/benchmarks --benchmark_out=results.json --benchmark_out_format=json

Benchmark Examples

Basic Benchmark

static void BM_SimpleFunction(benchmark::State& state) {
    for (auto _ : state) {
        // Code to benchmark
        function_to_test();
    }
}
BENCHMARK(BM_SimpleFunction);

Parameterized Benchmark

static void BM_Parameterized(benchmark::State& state) {
    int size = state.range(0);
    for (auto _ : state) {
        // Code using size parameter
    }
}
BENCHMARK(BM_Parameterized)->Arg(100)->Arg(1000)->Arg(10000);

Fixture-based Benchmark

class MyFixture : public benchmark::Fixture {
public:
    void SetUp(const benchmark::State& state) override {
        // Setup code
    }
    void TearDown(const benchmark::State& state) override {
        // Cleanup code
    }
};

BENCHMARK_F(MyFixture, BM_TestWithFixture)(benchmark::State& state) {
    for (auto _ : state) {
        // Test code
    }
}

Current Benchmarks

Core Benchmarks (core/)

• Battle Simulation: Measures battle simulation performance
• Pokemon Creation: Tests Pokemon instantiation speed
• Bulk Operations: Performance with multiple Pokemon
• Health Operations: Getter/setter performance

Benchmark Output

The benchmarks provide detailed timing information including:

• Mean: Average execution time
• Median: Middle value of execution times
• StdDev: Standard deviation of results
• Iterations: Number of times the benchmark was run

Performance Optimization Tips

1. Use benchmark::DoNotOptimize() to prevent compiler optimization
2. Use benchmark::ClobberMemory() for memory benchmarks
3. Set appropriate iteration counts for consistent measurements
4. Use fixtures for complex setup/teardown scenarios
5. Run multiple repetitions for statistical significance

Key Performance Targets

Battle Simulation

• Target: 1M+ battles per second on modern hardware
• Memory: < 1KB per Pokemon instance
• Latency: < 1µs per move calculation

Data Loading

• Target: < 100ms to load all Gen 1 data
• Memory: Efficient data structures with minimal overhead
• Caching: Fast lookup tables for frequently accessed data

AI Performance

• Random AI: < 1µs per decision
• Minimax AI: Configurable depth with time limits
• Memory: Bounded memory usage for tree search

Profiling Integration

• CPU Profiling: Integration with perf, VTune
• Memory Profiling: Valgrind, AddressSanitizer integration
• Cache Analysis: Cache miss analysis and optimization
• Flame Graphs: Visual performance analysis

Continuous Performance Monitoring

Benchmarks run automatically to detect performance regressions and track improvements over time.