DNYoussef/when-analyzing-performance-use-performance-analysis

Performance Analysis SOP

Overview

Comprehensive performance analysis for Claude Flow swarms including bottleneck detection, profiling, benchmarking, and actionable optimization recommendations.

Agents & Responsibilities

performance-analyzer

Role: Analyze system performance and identify issues Responsibilities:

• Collect performance metrics
• Analyze resource utilization
• Identify bottlenecks
• Generate analysis reports

performance-benchmarker

Role: Run performance benchmarks and comparisons Responsibilities:

• Execute benchmark suites
• Compare performance across configurations
• Establish performance baselines
• Validate improvements

perf-analyzer

Role: Deep performance profiling and optimization Responsibilities:

• Profile code execution
• Analyze memory usage
• Optimize critical paths
• Recommend improvements

Phase 1: Establish Baseline

Objective

Measure current performance and establish baseline metrics.

Scripts

# Collect baseline metrics
npx claude-flow@alpha performance baseline \
  --duration 300 \
  --interval 5 \
  --output baseline-metrics.json

# Run benchmark suite
npx claude-flow@alpha benchmark run \
  --type swarm \
  --iterations 10 \
  --output benchmark-results.json

# Profile system resources
npx claude-flow@alpha performance profile \
  --include-cpu \
  --include-memory \
  --include-network \
  --output resource-profile.json

# Collect agent metrics
npx claude-flow@alpha agent metrics --all --format json > agent-metrics.json

# Store baseline
npx claude-flow@alpha memory store \
  --key "performance/baseline" \
  --file baseline-metrics.json

# Generate baseline report
npx claude-flow@alpha performance report \
  --type baseline \
  --metrics baseline-metrics.json \
  --output baseline-report.md

Key Baseline Metrics

Swarm-Level:

• Total throughput (tasks/min)
• Average latency (ms)
• Resource utilization (%)
• Error rate (%)
• Coordination overhead (ms)

Agent-Level:

• Task completion rate
• Response time (ms)
• CPU usage (%)
• Memory usage (MB)
• Idle time (%)

System-Level:

• Total CPU usage (%)
• Total memory usage (MB)
• Network bandwidth (MB/s)
• Disk I/O (MB/s)

Memory Patterns

# Store performance baseline
npx claude-flow@alpha memory store \
  --key "performance/baseline/timestamp" \
  --value "$(date -Iseconds)"

npx claude-flow@alpha memory store \
  --key "performance/baseline/metrics" \
  --value '{
    "throughput": 145.2,
    "latency": 38.5,
    "utilization": 0.78,
    "errorRate": 0.012,
    "timestamp": "'$(date -Iseconds)'"
  }'

Phase 2: Profile System

Objective

Deep profiling of system components to identify performance characteristics.

Scripts

# Profile swarm execution
npx claude-flow@alpha performance profile-swarm \
  --duration 300 \
  --sample-rate 100 \
  --output swarm-profile.json

# Profile individual agents
for AGENT in $(npx claude-flow@alpha agent list --format json | jq -r '.[].id'); do
  npx claude-flow@alpha performance profile-agent \
    --agent-id "$AGENT" \
    --duration 60 \
    --output "profiles/agent-$AGENT.json"
done

# Profile memory usage
npx claude-flow@alpha memory profile \
  --show-hotspots \
  --show-leaks \
  --output memory-profile.json

# Profile network communication
npx claude-flow@alpha performance profile-network \
  --show-latency \
  --show-bandwidth \
  --output network-profile.json

# Generate flamegraph
npx claude-flow@alpha performance flamegraph \
  --input swarm-profile.json \
  --output flamegraph.svg

# Analyze CPU hotspots
npx claude-flow@alpha performance hotspots \
  --type cpu \
  --threshold 5 \
  --output cpu-hotspots.json

Profiling Analysis

# Identify slow functions
SLOW_FUNCTIONS=$(jq '[.profile[] | select(.time > 100)]' swarm-profile.json)

# Identify memory hogs
MEMORY_HOGS=$(jq '[.memory[] | select(.usage > 100)]' memory-profile.json)

# Identify network bottlenecks
NETWORK_ISSUES=$(jq '[.network[] | select(.latency > 50)]' network-profile.json)

echo "Slow Functions: $(echo $SLOW_FUNCTIONS | jq length)"
echo "Memory Hogs: $(echo $MEMORY_HOGS | jq length)"
echo "Network Issues: $(echo $NETWORK_ISSUES | jq length)"

Phase 3: Analyze Issues

Objective

Identify and categorize performance issues and bottlenecks.

Scripts

# Run comprehensive analysis
npx claude-flow@alpha performance analyze \
  --input swarm-profile.json \
  --detect-bottlenecks \
  --detect-memory-leaks \
  --detect-deadlocks \
  --output analysis-results.json

# Identify bottlenecks by type
npx claude-flow@alpha performance bottlenecks \
  --categorize \
  --priority-order \
  --output bottleneck-report.json

# Analyze agent performance
npx claude-flow@alpha agent analyze-performance \
  --all \
  --identify-underperformers \
  --output agent-analysis.json

# Analyze coordination overhead
npx claude-flow@alpha performance coordination-overhead \
  --calculate \
  --breakdown \
  --output coordination-analysis.json

# Root cause analysis
npx claude-flow@alpha performance root-cause \
  --issue "high-latency" \
  --trace-back \
  --output root-cause-analysis.json

Issue Classification

Critical Issues:

• Deadlocks
• Memory leaks
• Complete performance degradation
• System instability

High Priority:

• Bottlenecks causing >30% slowdown
• High error rates (>5%)
• Resource exhaustion
• Coordination failures

Medium Priority:

• Moderate slowdowns (10-30%)
• Suboptimal resource utilization
• Inefficient algorithms
• Poor load balancing

Low Priority:

• Minor optimizations (<10% impact)
• Code style issues
• Documentation gaps

Memory Patterns

# Store analysis results
npx claude-flow@alpha memory store \
  --key "performance/analysis/issues" \
  --value '{
    "critical": 0,
    "high": 3,
    "medium": 8,
    "low": 12,
    "timestamp": "'$(date -Iseconds)'"
  }'

# Store bottleneck information
npx claude-flow@alpha memory store \
  --key "performance/analysis/bottlenecks" \
  --file bottleneck-report.json

Phase 4: Optimize Performance

Objective

Apply optimizations based on analysis and measure improvements.

Scripts

# Get optimization recommendations
npx claude-flow@alpha performance recommend \
  --based-on analysis-results.json \
  --prioritize \
  --output recommendations.json

# Apply automatic optimizations
npx claude-flow@alpha performance optimize \
  --recommendations recommendations.json \
  --auto-apply safe-optimizations

# Manual optimizations
# 1. Fix identified bottlenecks
# 2. Optimize hot paths
# 3. Reduce coordination overhead
# 4. Improve resource utilization

# Optimize swarm topology
npx claude-flow@alpha swarm optimize-topology \
  --based-on analysis-results.json

# Optimize agent allocation
npx claude-flow@alpha agent rebalance \
  --strategy performance-optimized

# Optimize memory usage
npx claude-flow@alpha memory optimize \
  --reduce-footprint \
  --clear-unused

# Apply neural optimizations
npx claude-flow@alpha neural train \
  --pattern convergent \
  --iterations 10

Optimization Techniques

Parallelization:

# Increase parallelism for independent tasks
npx claude-flow@alpha swarm configure \
  --max-parallel-tasks 8

Caching:

# Enable result caching
npx claude-flow@alpha performance cache \
  --enable \
  --strategy lru \
  --max-size 1000

Load Balancing:

# Rebalance agent workloads
npx claude-flow@alpha swarm rebalance \
  --strategy adaptive \
  --target-variance 0.1

Resource Allocation:

# Optimize resource allocation
npx claude-flow@alpha agent configure --all \
  --memory-limit auto \
  --cpu-limit auto

Phase 5: Validate Results

Objective

Measure improvements and validate optimization effectiveness.

Scripts

# Collect post-optimization metrics
npx claude-flow@alpha performance baseline \
  --duration 300 \
  --output optimized-metrics.json

# Run comparison benchmark
npx claude-flow@alpha benchmark run \
  --type swarm \
  --iterations 10 \
  --output optimized-benchmark.json

# Compare before/after
npx claude-flow@alpha performance compare \
  --baseline baseline-metrics.json \
  --current optimized-metrics.json \
  --output improvement-report.json

# Calculate improvements
THROUGHPUT_IMPROVEMENT=$(jq '.improvements.throughput.percentage' improvement-report.json)
LATENCY_IMPROVEMENT=$(jq '.improvements.latency.percentage' improvement-report.json)

echo "Throughput improved by: $THROUGHPUT_IMPROVEMENT%"
echo "Latency improved by: $LATENCY_IMPROVEMENT%"

# Validate improvements meet targets
npx claude-flow@alpha performance validate \
  --improvements improvement-report.json \
  --targets performance-targets.json

# Generate final report
npx claude-flow@alpha performance report \
  --type comprehensive \
  --include-baseline \
  --include-analysis \
  --include-optimizations \
  --include-results \
  --output final-performance-report.md

# Archive performance data
npx claude-flow@alpha performance archive \
  --output performance-archive-$(date +%Y%m%d).tar.gz

Validation Criteria

Minimum Improvements:

• Throughput: +15%
• Latency: -20%
• Resource utilization: More balanced (variance <10%)
• Error rate: -50% or <1%

Validation Checks:

# Check if improvements meet targets
if (( $(echo "$THROUGHPUT_IMPROVEMENT >= 15" | bc -l) )); then
  echo "✓ Throughput target met"
else
  echo "✗ Throughput target not met"
fi

if (( $(echo "$LATENCY_IMPROVEMENT >= 20" | bc -l) )); then
  echo "✓ Latency target met"
else
  echo "✗ Latency target not met"
fi

Success Criteria

• [ ] Baseline established
• [ ] System profiled
• [ ] Issues identified and categorized
• [ ] Optimizations applied
• [ ] Improvements validated

Performance Targets

• Throughput improvement: ≥15%
• Latency reduction: ≥20%
• Resource utilization variance: <10%
• Error rate: <1%
• Optimization overhead: <5%

Best Practices

1. Baseline First: Always establish baseline before optimizing
2. Measure Everything: Comprehensive metrics collection
3. Identify Bottlenecks: Focus on critical path
4. Incremental Optimization: Apply optimizations incrementally
5. Validate Improvements: Always measure after optimizing
6. Document Changes: Record all optimization actions
7. Regression Testing: Ensure optimizations don't break functionality
8. Continuous Monitoring: Track performance over time

Common Issues & Solutions

Issue: No Performance Improvement

Symptoms: Metrics unchanged after optimization Solution: Re-analyze bottlenecks, verify optimizations applied correctly

Issue: Performance Regression

Symptoms: Performance worse after optimization Solution: Rollback changes, re-evaluate optimization strategy

Issue: Inconsistent Results

Symptoms: Performance varies significantly between runs Solution: Increase measurement duration, check for external factors

Integration Points

• advanced-swarm: For topology optimization
• swarm-orchestration: For coordination optimization
• cascade-orchestrator: For workflow optimization

References

• Performance Analysis Methodologies
• Profiling Techniques
• Optimization Patterns
• Benchmarking Best Practices