BMAD-METHOD/.claude/orchestrator/parallel-execution-engine.md

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Parallel Execution Engine - Performance Optimization System

System Overview

The Parallel Execution Engine optimizes workflow performance by executing independent agents simultaneously, managing resource allocation, and coordinating synchronization points for maximum efficiency.

Parallel Execution Architecture

Dependency Analysis Engine

dependency_analyzer:
  analysis_scope: "full_workflow_dependency_graph"
  
  dependency_types:
    data_dependency:
      description: "Agent B requires structured data from Agent A"
      example: "pm requires analyst.project_brief.structured_data"
      blocking: true
      
    context_dependency:  
      description: "Agent B benefits from Agent A's context but can work without it"
      example: "ux_expert can use architect.technology_stack if available"
      blocking: false
      
    validation_dependency:
      description: "Agent B needs Agent A for output validation"  
      example: "architect validates pm's technical requirements"
      blocking: false  # Can be done post-completion
      
    resource_dependency:
      description: "Agents compete for the same computational resources"
      example: "multiple agents using same template engine"
      blocking: false  # Managed by resource allocator
      
  dependency_graph_generation:
    algorithm: "topological_sort_with_parallel_identification"
    optimization: "minimize_critical_path_length"
    output: "execution_groups_with_parallel_opportunities"

Execution Group Definition

execution_groups:
  group_1_foundation:
    agents: ["analyst"]
    dependencies: []
    parallel_capable: false
    estimated_duration: "3-5_minutes"
    resource_requirements:
      cpu: "medium"
      memory: "low"
      context_access: "write_only"
      
  group_2_requirements:
    agents: ["pm"]
    dependencies: ["analyst.completion"]
    parallel_capable: false  # Single critical path
    estimated_duration: "4-6_minutes"
    resource_requirements:
      cpu: "high"
      memory: "medium"
      context_access: "read_analyst_write_pm"
      
  group_3_design_architecture:
    agents: ["ux_expert", "architect"]
    dependencies: ["pm.completion"]
    parallel_capable: true  # Key optimization opportunity
    synchronization: "barrier_sync"
    estimated_duration: "6-8_minutes"
    
    agent_specifications:
      ux_expert:
        inputs: ["pm.user_stories", "pm.user_personas", "analyst.target_users"]
        outputs: ["ui_spec", "user_journeys", "component_specifications"]
        resource_requirements:
          cpu: "medium"
          memory: "medium"
          context_access: "read_multiple_write_ux"
          
      architect:
        inputs: ["pm.functional_requirements", "pm.non_functional_requirements", "analyst.technical_constraints"]  
        outputs: ["system_architecture", "technology_stack", "api_specifications"]
        resource_requirements:
          cpu: "high"
          memory: "high"
          context_access: "read_multiple_write_architect"
          
    synchronization_strategy:
      type: "smart_barrier"
      timeout: "10_minutes"
      partial_failure_handling: "continue_with_available_results"
      cross_validation: "post_completion_validation"
      
  group_4_implementation:
    agents: ["developer"]
    dependencies: ["ux_expert.completion", "architect.completion"]
    parallel_capable: false
    estimated_duration: "8-12_minutes"
    resource_requirements:
      cpu: "very_high"
      memory: "high"
      context_access: "read_multiple_write_developer"
      
  group_5_quality:
    agents: ["qa"]
    dependencies: ["developer.completion"]
    parallel_capable: false
    estimated_duration: "4-6_minutes"
    resource_requirements:
      cpu: "medium"
      memory: "medium"
      context_access: "read_all_write_qa"

Resource Management System

resource_manager:
  resource_pool:
    computational:
      total_cpu_units: 100
      total_memory_units: 100
      concurrent_agent_limit: 3
      
    context_store:
      concurrent_readers: 5
      concurrent_writers: 1
      lock_timeout: 30  # seconds
      
    template_engine:
      concurrent_renders: 3
      cache_size: "50MB"
      render_timeout: 120  # seconds
      
  allocation_strategy:
    algorithm: "priority_based_fair_sharing"
    priorities:
      critical_path_agents: 10
      parallel_group_agents: 7  
      validation_agents: 5
      
    resource_sharing:
      cpu_oversubscription: 1.2  # Allow 120% allocation
      memory_strict: true  # No oversubscription
      context_queuing: true
      
  load_balancing:
    agent_assignment:
      strategy: "least_loaded_instance"
      health_check: "response_time_based"
      failover: "automatic_reassignment"
      
    resource_monitoring:
      metrics: ["cpu_usage", "memory_usage", "response_time", "queue_depth"]
      sampling_interval: 10  # seconds
      alert_thresholds:
        cpu_usage: 90
        memory_usage: 85
        response_time: 180  # seconds
        queue_depth: 3

Parallel Coordination Protocol

Smart Barrier Synchronization

barrier_synchronization:
  implementation: "smart_barrier_with_partial_results"
  
  phases:
    phase_1_initiation:
      - start_all_parallel_agents_simultaneously
      - establish_shared_context_locks
      - begin_progress_monitoring
      
    phase_2_execution_monitoring:
      - track_individual_agent_progress
      - monitor_resource_utilization
      - detect_potential_conflicts_early
      - provide_progress_updates
      
    phase_3_completion_detection:
      - wait_for_all_agents_OR_timeout
      - collect_completed_results
      - assess_partial_completion_viability
      - make_continuation_decision
      
    phase_4_synchronization:
      - merge_parallel_results
      - resolve_any_conflicts
      - validate_cross_agent_consistency
      - prepare_context_for_next_group
      
  timeout_handling:
    default_timeout: "10_minutes"
    timeout_strategies:
      continue_with_partial:
        condition: "at_least_one_agent_completed_successfully"
        action: "use_completed_results_fill_gaps_with_defaults"
        quality_impact: "medium"
        
      extend_timeout:
        condition: "agents_making_progress_but_need_more_time"
        extension: "5_minutes_maximum"
        max_extensions: 1
        
      abort_and_fallback:
        condition: "no_agents_completing_OR_critical_errors"
        action: "fall_back_to_sequential_execution"
        notification: "alert_system_administrators"

Context Conflict Resolution

context_conflicts:
  conflict_types:
    write_write_conflict:
      description: "Multiple agents trying to write to same context location"
      resolution: "first_writer_wins_with_merge_opportunity"
      example: "both ux_expert and architect updating user_requirements"
      
    read_write_conflict:
      description: "Agent reading while another agent writing"
      resolution: "consistent_snapshot_isolation"
      example: "pm reading analyst_data while analyst updating"
      
    consistency_conflict:
      description: "Parallel agents producing inconsistent outputs"
      resolution: "post_completion_conflict_detection_and_resolution"
      example: "ux_expert specifies mobile_first while architect assumes desktop_primary"
      
  resolution_mechanisms:
    optimistic_concurrency:
      strategy: "allow_parallel_execution_detect_conflicts_later"
      conflict_detection: "content_hash_comparison"
      resolution: "three_way_merge_with_validation"
      
    context_versioning:
      strategy: "version_all_context_changes"
      merge_strategy: "semantic_merge_where_possible"
      fallback: "manual_conflict_resolution"
      
    conflict_prevention:
      strategy: "pre_allocate_context_ownership"
      ownership_rules:
        analyst: ["project_metadata", "problem_analysis", "user_research"]
        pm: ["requirements", "user_stories", "acceptance_criteria"]
        architect: ["technical_architecture", "system_design", "api_specs"]
        ux_expert: ["user_experience", "ui_specifications", "design_system"]

Performance Optimization Features

Intelligent Caching System

caching_system:
  cache_levels:
    agent_output_cache:
      scope: "completed_agent_outputs"
      key_strategy: "content_hash_based"
      ttl: "session_lifetime"
      size_limit: "100MB"
      
    template_render_cache:
      scope: "rendered_template_outputs"
      key_strategy: "template_id_plus_variable_hash"
      ttl: "1_hour"
      size_limit: "50MB"
      
    context_snapshot_cache:
      scope: "context_state_at_major_milestones"
      key_strategy: "workflow_step_plus_content_hash"
      ttl: "session_lifetime"
      size_limit: "200MB"
      
  cache_optimization:
    prefetching:
      strategy: "predict_next_likely_agent_needs"
      prefetch_templates: true
      prefetch_context: true
      
    compression:
      algorithm: "lz4_fast_compression"
      compression_threshold: "10KB"
      
    eviction:
      policy: "lru_with_priority_boosting"
      priority_factors: ["access_frequency", "computation_cost", "cache_hit_ratio"]

Performance Monitoring

performance_monitoring:
  metrics_collection:
    execution_metrics:
      - total_workflow_time
      - individual_agent_execution_time
      - parallel_efficiency_ratio
      - context_lock_wait_time
      - resource_utilization_percentage
      
    quality_metrics:
      - output_quality_scores
      - validation_failure_rate
      - context_conflict_frequency
      - retry_rate
      
    system_metrics:
      - memory_usage_peak
      - cpu_utilization_average
      - cache_hit_ratios
      - network_latency_impact
      
  performance_optimization:
    adaptive_resource_allocation:
      trigger: "performance_below_threshold"
      actions:
        - increase_cpu_allocation
        - adjust_parallel_group_sizes
        - modify_timeout_values
        - enable_aggressive_caching
        
    bottleneck_detection:
      algorithm: "critical_path_analysis"
      automatic_optimization:
        - reorder_non_critical_operations
        - increase_parallelization_opportunities
        - optimize_context_access_patterns

Integration with Workflow Engine

Enhanced Workflow Execution

# Enhanced workflow step definition with parallel capabilities
enhanced_workflow_step:
  id: "step_3_parallel_design"
  name: "Parallel Design & Architecture"
  type: "parallel_group"
  
  parallel_configuration:
    execution_mode: "truly_parallel"
    resource_isolation: false  # Allow resource sharing
    context_sharing: "read_only_shared_write_isolated"
    
  agents:
    - id: "ux_expert_parallel"
      agent: "ux_expert"
      priority: 7
      timeout: "8_minutes"
      context_requirements:
        read: ["pm.user_stories", "analyst.target_users"]
        write: ["ux_outputs"]
      resource_requirements:
        cpu: 30  # units
        memory: 25  # units
        
    - id: "architect_parallel"  
      agent: "architect"
      priority: 8  # Slightly higher priority
      timeout: "10_minutes"
      context_requirements:
        read: ["pm.requirements", "analyst.technical_constraints"]
        write: ["architecture_outputs"]
      resource_requirements:
        cpu: 40  # units
        memory: 35  # units
        
  synchronization:
    barrier_type: "smart_barrier"
    success_criteria: "at_least_one_agent_succeeds"
    failure_criteria: "all_agents_fail_or_timeout"
    
  cross_validation:
    enabled: true
    validation_agents: ["architect", "ux_expert"]  # Validate each other
    validation_timeout: "3_minutes"

This Parallel Execution Engine dramatically improves system performance by executing independent agents simultaneously while maintaining data consistency and quality through intelligent coordination mechanisms.