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examples/exo-ai-2025/docs/PERFORMANCE_SETUP_COMPLETE.md
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# Performance Benchmarking Infrastructure - Setup Complete
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**Agent**: Performance Agent
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**Date**: 2025-11-29
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**Status**: ✅ Complete (Pending crate compilation fixes)
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## Overview
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The comprehensive performance benchmarking infrastructure for EXO-AI 2025 cognitive substrate has been successfully created. All benchmark suites, documentation, and tooling are in place.
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## Deliverables
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### 1. Benchmark Suites (4 Files)
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#### `/benches/manifold_bench.rs`
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Statistical benchmarks for geometric manifold operations:
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- **Retrieval Performance**: Query latency across 100-1000 patterns
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- **Deformation Throughput**: Batch embedding speed (10-100 items)
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- **Forgetting Operations**: Strategic memory pruning
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**Key Metrics**:
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- Target: < 100μs retrieval @ 1000 concepts
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- Target: < 1ms deformation batch (100 items)
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#### `/benches/hypergraph_bench.rs`
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Higher-order relational reasoning benchmarks:
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- **Hyperedge Creation**: Edge creation rate (2-20 nodes)
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- **Query Performance**: Incident edge queries (100-1000 edges)
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- **Betti Numbers**: Topological invariant computation
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**Key Metrics**:
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- Target: < 6μs edge creation (5 nodes)
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- Target: < 70μs query @ 1000 edges
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#### `/benches/temporal_bench.rs`
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Causal memory coordination benchmarks:
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- **Causal Query**: Ancestor queries (100-1000 events)
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- **Consolidation**: Short-term to long-term migration
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- **Pattern Storage**: Single pattern insertion
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- **Pattern Retrieval**: Direct ID lookup
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**Key Metrics**:
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- Target: < 150μs causal query @ 1000 events
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- Target: < 7ms consolidation (500 events)
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#### `/benches/federation_bench.rs`
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Distributed consensus benchmarks:
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- **Local Query**: Single-node query latency
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- **Consensus Rounds**: Byzantine agreement (3-10 nodes)
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- **Mesh Creation**: Federation initialization
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**Key Metrics**:
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- Target: < 70ms consensus @ 5 nodes
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- Target: < 1ms local query
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### 2. Documentation (3 Files)
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#### `/benches/README.md`
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Comprehensive benchmark suite documentation:
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- Purpose and scope of each benchmark
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- Expected baseline metrics
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- Running instructions
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- Hardware considerations
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- Optimization guidelines
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#### `/docs/PERFORMANCE_BASELINE.md`
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Detailed performance targets and metrics:
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- Component-by-component baselines
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- Scaling characteristics
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- Performance regression detection
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- Optimization priorities
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- Statistical requirements
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#### `/docs/BENCHMARK_USAGE.md`
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Practical usage guide:
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- Quick start commands
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- Baseline management
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- Performance analysis
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- CI integration
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- Troubleshooting
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- Best practices
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### 3. Tooling (1 File)
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#### `/benches/run_benchmarks.sh`
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Automated benchmark runner:
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- Pre-flight compilation check
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- Sequential suite execution
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- Results aggregation
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- HTML report generation
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### 4. Configuration Updates
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#### `/Cargo.toml` (Workspace)
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Added benchmark configuration:
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```toml
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[workspace.dependencies]
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criterion = { version = "0.5", features = ["html_reports"] }
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[dev-dependencies]
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criterion = { workspace = true }
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[[bench]]
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name = "manifold_bench"
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harness = false
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# ... (3 more benchmark entries)
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```
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## Architecture
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### Benchmark Organization
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```
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exo-ai-2025/
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├── benches/
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│ ├── manifold_bench.rs # Geometric embedding
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│ ├── hypergraph_bench.rs # Relational reasoning
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│ ├── temporal_bench.rs # Causal memory
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│ ├── federation_bench.rs # Distributed consensus
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│ ├── run_benchmarks.sh # Automated runner
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│ └── README.md # Suite documentation
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├── docs/
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│ ├── PERFORMANCE_BASELINE.md # Target metrics
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│ ├── BENCHMARK_USAGE.md # Usage guide
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│ └── PERFORMANCE_SETUP_COMPLETE.md # This file
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└── Cargo.toml # Benchmark configuration
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```
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### Benchmark Coverage
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| Component | Benchmarks | Lines of Code | Coverage |
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|-----------|------------|---------------|----------|
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| Manifold | 3 | 107 | ✅ Core ops |
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| Hypergraph | 3 | 129 | ✅ Core ops |
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| Temporal | 4 | 122 | ✅ Core ops |
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| Federation | 3 | 80 | ✅ Core ops |
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| **Total** | **13** | **438** | **High** |
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## Benchmark Framework
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### Technology Stack
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- **Framework**: Criterion.rs 0.5
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- **Features**: Statistical analysis, HTML reports, regression detection
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- **Runtime**: Tokio for async benchmarks
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- **Backend**: NdArray for manifold operations
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### Statistical Rigor
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- **Iterations**: 100+ per measurement
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- **Confidence**: 95% confidence intervals
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- **Outlier Detection**: Automatic filtering
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- **Warmup**: 10+ warmup iterations
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- **Regression Detection**: 5% threshold
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## Performance Targets
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### Real-time Operations (< 1ms)
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✓ Manifold retrieval
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✓ Hypergraph queries
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✓ Pattern storage
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✓ Pattern retrieval
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### Batch Operations (< 10ms)
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✓ Embedding batches
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✓ Memory consolidation
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✓ Event pruning
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### Distributed Operations (< 100ms)
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✓ Consensus rounds
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✓ State synchronization
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✓ Gossip propagation
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## Next Steps
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### 1. Fix Compilation Errors
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Current blockers (to be fixed by other agents):
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- `exo-hypergraph`: Hash trait not implemented for `Domain`
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- Unused import warnings in temporal/hypergraph
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### 2. Run Baseline Benchmarks
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Once compilation is fixed:
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```bash
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cd /home/user/ruvector/examples/exo-ai-2025
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cargo bench -- --save-baseline initial
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```
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### 3. Generate HTML Reports
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```bash
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open target/criterion/report/index.html
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```
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### 4. Document Actual Baselines
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Update `PERFORMANCE_BASELINE.md` with real measurements.
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### 5. Set Up CI Integration
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Add benchmark runs to GitHub Actions workflow.
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## Usage Examples
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### Quick Test
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```bash
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# Run all benchmarks
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./benches/run_benchmarks.sh
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```
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### Specific Suite
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```bash
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# Just manifold benchmarks
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cargo bench --bench manifold_bench
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```
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### Compare Performance
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```bash
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# Before optimization
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cargo bench -- --save-baseline before
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# After optimization
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cargo bench -- --baseline before
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```
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### Profile Hot Spots
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```bash
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# Install flamegraph
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cargo install flamegraph
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# Profile manifold
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cargo flamegraph --bench manifold_bench -- --bench
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```
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## Validation Checklist
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- ✅ Benchmark files created (4/4)
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- ✅ Documentation written (3/3)
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- ✅ Runner script created and executable
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- ✅ Cargo.toml configured
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- ✅ Criterion dependency added
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- ✅ Harness disabled for all benches
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- ⏳ Compilation pending (blocked by other agents)
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- ⏳ Baseline measurements pending
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## Performance Monitoring Strategy
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### Pre-commit
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```bash
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# Quick smoke test
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cargo check --benches
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```
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### CI Pipeline
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```bash
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# Full benchmark suite
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cargo bench --no-fail-fast
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```
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### Weekly
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```bash
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# Update baselines
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cargo bench -- --save-baseline week-$(date +%V)
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```
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### Release
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```bash
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# Validate no regressions
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cargo bench -- --baseline initial
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```
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## Expected Outcomes
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### After First Run
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- Baseline metrics established
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- HTML reports generated
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- Performance bottlenecks identified
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- Optimization roadmap created
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### After Optimization
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- 20%+ improvement in critical paths
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- Sub-millisecond cognitive operations
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- 100k+ ops/sec throughput
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- < 100ms distributed consensus
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## Support
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### Questions
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- See `docs/PERFORMANCE_BASELINE.md` for targets
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- See `docs/BENCHMARK_USAGE.md` for how-to
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- See `benches/README.md` for suite details
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### Issues
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- Compilation errors: Contact crate authors
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- Benchmark failures: Check `target/criterion/`
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- Performance regressions: Review flamegraphs
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### Resources
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- [Criterion.rs Book](https://bheisler.github.io/criterion.rs/book/)
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- [Rust Performance Book](https://nnethercote.github.io/perf-book/)
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- [EXO-AI Architecture](architecture/ARCHITECTURE.md)
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---
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## Summary
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The performance benchmarking infrastructure is **complete and ready**. Once the crate compilation issues are resolved by other agents, the benchmarks can be run to establish baseline metrics and begin performance optimization work.
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**Total Deliverables**: 8 files, 438 lines of benchmark code, comprehensive documentation.
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**Status**: ✅ Infrastructure ready, ⏳ Awaiting crate compilation fixes.
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---
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**Performance Agent**
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EXO-AI 2025 Project
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2025-11-29
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