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v3-performance-optimization

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.

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ruvnet/agentic-flow
Dernière activité de la source
13 février 2026 à 16:43
Langue détectée de SKILL.md
anglais
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SKILL.md
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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 ```
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