V3 Performance Optimization

ruvnet/ruflo/v3/@claude-flow/cli/.claude/skills/v3-performance-optimization

作者 ruvnet58e0ae7e14e68aab45a4127d6f42f567bbcfb328无许可证74K 个星标收录于 2026年10月9日更新于 2026年10月9日仓库昨天更新

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.

AI 生成的概览

通过基准测试与回归监控,验证并优化 claude-flow v3 的性能目标。

功能
该技能指导智能体验证并优化 claude-flow v3 的性能,涵盖 Flash Attention 加速、AgentDB HNSW 搜索索引、内存缩减、启动延迟、SONA 自适应以及集群协调。它描述了基准测试套件、实时性能监控面板、带阈值的回归检测,以及内存和 CPU 优化策略。其产出是基准测试结果、验证报告和优化建议,而非可执行代码。
适用场景
适用于建立性能基线、验证 v3 的加速与内存目标,或为 claude-flow v3 搭建持续性能监控与回归检测时。
运行要求
仅为说明性内容,不附带脚本。文中引用 TypeScript 基准测试类和 npm 基准/监控命令,因此预期具备 claude-flow v3 环境、Node.js 及相应 npm 脚本。

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 optimizationTask("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 suitenpm run benchmark:v3
# Specific target validationnpm run benchmark:flash-attentionnpm run benchmark:agentdb-searchnpm run benchmark:memory-optimization
# Continuous monitoringnpm run monitor:performance

来源与署名

来源:ruvnet/ruflo位于v3/@claude-flow/cli/.claude/skills/v3-performance-optimization提交58e0ae7

许可证: 无许可证

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