home / skills / proffesor-for-testing / agentic-qe / v3-performance-optimization
/v2/docs/internal/archived-v3-development-skills/v3-performance-optimization
This skill analyzes and tunes claude-flow v3 for Flash Attention, HNSW indexing, and benchmarking to maximize speed, memory efficiency, and throughput.
npx playbooks add skill proffesor-for-testing/agentic-qe --skill v3-performance-optimizationReview the files below or copy the command above to add this skill to your agents.
---
name: "V3 Performance Optimization"
description: "Achieve aggressive v3 performance targets: 2.49x-7.47x Flash Attention speedup, 150x-12,500x search improvements, 50-75% memory reduction. Comprehensive benchmarking and optimization suite."
---
# V3 Performance Optimization
## What This Skill Does
Validates and optimizes claude-flow v3 to achieve industry-leading performance through Flash Attention, AgentDB HNSW indexing, and comprehensive system optimization with continuous benchmarking.
## Quick Start
```bash
# Initialize performance optimization
Task("Performance baseline", "Establish v2 performance benchmarks", "v3-performance-engineer")
# Target validation (parallel)
Task("Flash Attention", "Validate 2.49x-7.47x speedup target", "v3-performance-engineer")
Task("Search optimization", "Validate 150x-12,500x search improvement", "v3-performance-engineer")
Task("Memory optimization", "Achieve 50-75% memory reduction", "v3-performance-engineer")
```
## Performance Target Matrix
### Flash Attention Revolution
```
┌─────────────────────────────────────────┐
│ FLASH ATTENTION │
├─────────────────────────────────────────┤
│ Baseline: Standard attention │
│ Target: 2.49x - 7.47x speedup │
│ Memory: 50-75% reduction │
│ Latency: Sub-millisecond processing │
└─────────────────────────────────────────┘
```
### Search Performance Revolution
```
┌─────────────────────────────────────────┐
│ SEARCH OPTIMIZATION │
├─────────────────────────────────────────┤
│ Current: O(n) linear search │
│ Target: 150x - 12,500x improvement │
│ Method: HNSW indexing │
│ Latency: <100ms for 1M+ entries │
└─────────────────────────────────────────┘
```
## Comprehensive Benchmark Suite
### Startup Performance
```typescript
class StartupBenchmarks {
async benchmarkColdStart(): Promise<BenchmarkResult> {
const startTime = performance.now();
await this.initializeCLI();
await this.initializeMCPServer();
await this.spawnTestAgent();
const totalTime = performance.now() - startTime;
return {
total: totalTime,
target: 500, // ms
achieved: totalTime < 500
};
}
}
```
### Memory Operation Benchmarks
```typescript
class MemoryBenchmarks {
async benchmarkVectorSearch(): Promise<SearchBenchmark> {
const queries = this.generateTestQueries(10000);
// Baseline: Current linear search
const baselineTime = await this.timeOperation(() =>
this.currentMemory.searchAll(queries)
);
// Target: HNSW search
const hnswTime = await this.timeOperation(() =>
this.agentDBMemory.hnswSearchAll(queries)
);
const improvement = baselineTime / hnswTime;
return {
baseline: baselineTime,
hnsw: hnswTime,
improvement,
targetRange: [150, 12500],
achieved: improvement >= 150
};
}
async benchmarkMemoryUsage(): Promise<MemoryBenchmark> {
const baseline = process.memoryUsage().heapUsed;
await this.loadTestDataset();
const withData = process.memoryUsage().heapUsed;
await this.enableOptimization();
const optimized = process.memoryUsage().heapUsed;
const reduction = (withData - optimized) / withData;
return {
baseline,
withData,
optimized,
reductionPercent: reduction * 100,
targetReduction: [50, 75],
achieved: reduction >= 0.5
};
}
}
```
### Swarm Coordination Benchmarks
```typescript
class SwarmBenchmarks {
async benchmark15AgentCoordination(): Promise<SwarmBenchmark> {
const agents = await this.spawn15Agents();
// Coordination latency
const coordinationTime = await this.timeOperation(() =>
this.coordinateSwarmTask(agents)
);
// Task decomposition
const decompositionTime = await this.timeOperation(() =>
this.decomposeComplexTask()
);
// Consensus achievement
const consensusTime = await this.timeOperation(() =>
this.achieveSwarmConsensus(agents)
);
return {
coordination: coordinationTime,
decomposition: decompositionTime,
consensus: consensusTime,
agentCount: 15,
efficiency: this.calculateEfficiency(agents)
};
}
}
```
### Flash Attention Benchmarks
```typescript
class AttentionBenchmarks {
async benchmarkFlashAttention(): Promise<AttentionBenchmark> {
const sequences = this.generateSequences([512, 1024, 2048, 4096]);
const results = [];
for (const sequence of sequences) {
// Baseline attention
const baselineResult = await this.benchmarkStandardAttention(sequence);
// Flash attention
const flashResult = await this.benchmarkFlashAttention(sequence);
results.push({
sequenceLength: sequence.length,
speedup: baselineResult.time / flashResult.time,
memoryReduction: (baselineResult.memory - flashResult.memory) / baselineResult.memory,
targetSpeedup: [2.49, 7.47],
achieved: this.checkTarget(flashResult, [2.49, 7.47])
});
}
return {
results,
averageSpeedup: this.calculateAverage(results, 'speedup'),
averageMemoryReduction: this.calculateAverage(results, 'memoryReduction')
};
}
}
```
### SONA Learning Benchmarks
```typescript
class SONABenchmarks {
async benchmarkAdaptationTime(): Promise<SONABenchmark> {
const scenarios = [
'pattern_recognition',
'task_optimization',
'error_correction',
'performance_tuning'
];
const results = [];
for (const scenario of scenarios) {
const startTime = performance.hrtime.bigint();
await this.sona.adapt(scenario);
const endTime = performance.hrtime.bigint();
const adaptationTimeMs = Number(endTime - startTime) / 1000000;
results.push({
scenario,
adaptationTime: adaptationTimeMs,
target: 0.05, // ms
achieved: adaptationTimeMs <= 0.05
});
}
return {
scenarios: results,
averageTime: results.reduce((sum, r) => sum + r.adaptationTime, 0) / results.length,
successRate: results.filter(r => r.achieved).length / results.length
};
}
}
```
## Performance Monitoring Dashboard
### Real-time Metrics
```typescript
class PerformanceMonitor {
async collectMetrics(): Promise<PerformanceSnapshot> {
return {
timestamp: Date.now(),
flashAttention: await this.measureFlashAttention(),
searchPerformance: await this.measureSearchSpeed(),
memoryUsage: await this.measureMemoryEfficiency(),
startupTime: await this.measureStartupLatency(),
sonaAdaptation: await this.measureSONASpeed(),
swarmCoordination: await this.measureSwarmEfficiency()
};
}
async generateReport(): Promise<PerformanceReport> {
const snapshot = await this.collectMetrics();
return {
summary: this.generateSummary(snapshot),
achievements: this.checkTargetAchievements(snapshot),
trends: this.analyzeTrends(),
recommendations: this.generateOptimizations(),
regressions: await this.detectRegressions()
};
}
}
```
### Continuous Regression Detection
```typescript
class PerformanceRegression {
async detectRegressions(): Promise<RegressionReport> {
const current = await this.runFullBenchmark();
const baseline = await this.getBaseline();
const regressions = [];
for (const [metric, currentValue] of Object.entries(current)) {
const baselineValue = baseline[metric];
const change = (currentValue - baselineValue) / baselineValue;
if (change < -0.05) { // 5% regression threshold
regressions.push({
metric,
baseline: baselineValue,
current: currentValue,
regressionPercent: change * 100,
severity: this.classifyRegression(change)
});
}
}
return {
hasRegressions: regressions.length > 0,
regressions,
recommendations: this.generateRegressionFixes(regressions)
};
}
}
```
## Optimization Strategies
### Memory Optimization
```typescript
class MemoryOptimization {
async optimizeMemoryUsage(): Promise<OptimizationResult> {
// Implement memory pooling
await this.setupMemoryPools();
// Enable garbage collection tuning
await this.optimizeGarbageCollection();
// Implement object reuse patterns
await this.setupObjectPools();
// Enable memory compression
await this.enableMemoryCompression();
return this.validateMemoryReduction();
}
}
```
### CPU Optimization
```typescript
class CPUOptimization {
async optimizeCPUUsage(): Promise<OptimizationResult> {
// Implement worker thread pools
await this.setupWorkerThreads();
// Enable CPU-specific optimizations
await this.enableSIMDInstructions();
// Implement task batching
await this.optimizeTaskBatching();
return this.validateCPUImprovement();
}
}
```
## Target Validation Framework
### Performance Gates
```typescript
class PerformanceGates {
async validateAllTargets(): Promise<ValidationReport> {
const results = await Promise.all([
this.validateFlashAttention(), // 2.49x-7.47x
this.validateSearchPerformance(), // 150x-12,500x
this.validateMemoryReduction(), // 50-75%
this.validateStartupTime(), // <500ms
this.validateSONAAdaptation() // <0.05ms
]);
return {
allTargetsAchieved: results.every(r => r.achieved),
results,
overallScore: this.calculateOverallScore(results),
recommendations: this.generateRecommendations(results)
};
}
}
```
## Success Metrics
### Primary Targets
- [ ] **Flash Attention**: 2.49x-7.47x speedup validated
- [ ] **Search Performance**: 150x-12,500x improvement confirmed
- [ ] **Memory Reduction**: 50-75% usage optimization achieved
- [ ] **Startup Time**: <500ms cold start consistently
- [ ] **SONA Adaptation**: <0.05ms learning response time
- [ ] **15-Agent Coordination**: Efficient parallel execution
### Continuous Monitoring
- [ ] **Performance Dashboard**: Real-time metrics collection
- [ ] **Regression Testing**: Automated performance validation
- [ ] **Trend Analysis**: Performance evolution tracking
- [ ] **Alert System**: Immediate regression notification
## Related V3 Skills
- `v3-integration-deep` - Performance integration with agentic-flow
- `v3-memory-unification` - Memory performance optimization
- `v3-swarm-coordination` - Swarm performance coordination
- `v3-security-overhaul` - Secure performance patterns
## Usage Examples
### Complete Performance Validation
```bash
# Full performance suite
npm run benchmark:v3
# Specific target validation
npm run benchmark:flash-attention
npm run benchmark:agentdb-search
npm run benchmark:memory-optimization
# Continuous monitoring
npm run monitor:performance
```This skill validates and aggressively optimizes claude-flow v3 to meet high-impact performance targets for attention, search, and memory. It provides a benchmark-driven workflow, automated validation gates, and continuous monitoring to guarantee reproducible speedups and resource reductions. The suite is implementation-focused and designed for TypeScript-based agent fleets.
The skill runs targeted micro- and system-level benchmarks: Flash Attention vs. standard attention, AgentDB HNSW search vs. linear search, memory usage before/after optimizations, startup latency, swarm coordination, and SONA adaptation. It automates optimization steps (memory pooling, worker threads, SIMD enablement, HNSW indexing) and validates results against configurable target ranges. Continuous monitoring and regression detection keep performance stable over time.
What hardware is required to validate Flash Attention targets?
Validate on representative GPUs or CPUs that support Flash Attention primitives; results depend on hardware and kernel implementations, so include target devices in benchmark definitions.
How do I interpret the search improvement range (150x–12,500x)?
The range reflects different dataset sizes and baseline implementations; aim for minimum 150x on realistic datasets (1M+ vectors) and document configuration that achieves higher multipliers.