6ed69a3d48
Major changes: - Organized Python v1 implementation into v1/ subdirectory - Created Rust workspace with 9 modular crates: - wifi-densepose-core: Core types, traits, errors - wifi-densepose-signal: CSI processing, phase sanitization, FFT - wifi-densepose-nn: Neural network inference (ONNX/Candle/tch) - wifi-densepose-api: Axum-based REST/WebSocket API - wifi-densepose-db: SQLx database layer - wifi-densepose-config: Configuration management - wifi-densepose-hardware: Hardware abstraction - wifi-densepose-wasm: WebAssembly bindings - wifi-densepose-cli: Command-line interface Documentation: - ADR-001: Workspace structure - ADR-002: Signal processing library selection - ADR-003: Neural network inference strategy - DDD domain model with bounded contexts Testing: - 69 tests passing across all crates - Signal processing: 45 tests - Neural networks: 21 tests - Core: 3 doc tests Performance targets: - 10x faster CSI processing (~0.5ms vs ~5ms) - 5x lower memory usage (~100MB vs ~500MB) - WASM support for browser deployment
59 lines
1.3 KiB
Markdown
59 lines
1.3 KiB
Markdown
# Performance Bottleneck Analysis
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## Purpose
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Identify and resolve performance bottlenecks in your development workflow.
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## Automated Analysis
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### 1. Real-time Detection
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The post-task hook automatically analyzes:
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- Execution time vs. complexity
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- Agent utilization rates
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- Resource constraints
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- Operation patterns
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### 2. Common Bottlenecks
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**Time Bottlenecks:**
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- Tasks taking > 5 minutes
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- Sequential operations that could parallelize
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- Redundant file operations
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**Coordination Bottlenecks:**
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- Single agent for complex tasks
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- Unbalanced agent workloads
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- Poor topology selection
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**Resource Bottlenecks:**
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- High operation count (> 100)
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- Memory constraints
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- I/O limitations
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### 3. Improvement Suggestions
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```
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Tool: mcp__claude-flow__task_results
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Parameters: {"taskId": "task-123", "format": "detailed"}
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Result includes:
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{
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"bottlenecks": [
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{
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"type": "coordination",
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"severity": "high",
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"description": "Single agent used for complex task",
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"recommendation": "Spawn specialized agents for parallel work"
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}
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],
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"improvements": [
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{
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"area": "execution_time",
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"suggestion": "Use parallel task execution",
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"expectedImprovement": "30-50% time reduction"
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}
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]
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}
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```
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## Continuous Optimization
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The system learns from each task to prevent future bottlenecks! |