Parallel Execution System for Swarm Benchmarking

Overview

The parallel execution system provides efficient, scalable execution of multiple benchmark tasks with comprehensive resource management, task scheduling, and progress monitoring.

Key Components

1. ParallelExecutor (parallel_executor.py)

The core execution engine that manages concurrent task execution with multiple execution modes:

• Thread-based execution: For I/O-bound tasks
• Process-based execution: For CPU-bound tasks
• Asyncio-based execution: For async/await compatible tasks
• Hybrid execution: Automatically chooses the best mode based on task characteristics

Features:

• Priority-based task queue with configurable size limits
• Resource monitoring and enforcement (CPU, memory)
• Automatic resource violation detection and throttling
• Execution metrics tracking (throughput, latency, resource usage)
• Graceful shutdown with task cleanup

2. TaskScheduler (task_scheduler.py)

Advanced task scheduling with multiple algorithms:

Scheduling Algorithms:

• Round Robin: Simple fair distribution
• Least Loaded: Assigns tasks to least busy agents
• Capability-Based: Matches task requirements to agent capabilities
• Priority-Based: Assigns high-priority tasks to best-performing agents
• Dynamic: Multi-factor scheduling considering capabilities, workload, and performance
• Work Stealing: Allows idle agents to steal tasks from busy agents

Features:

• Task dependency resolution with topological sorting
• Agent capability indexing for O(1) capability matching
• Work stealing queue for load balancing
• Scheduling metrics (load balance score, capability match score)
• Dynamic workload rebalancing

3. OrchestrationManager (orchestration_manager.py)

High-level orchestration for managing complex benchmark suites:

Features:

• Parallel benchmark suite execution
• Auto-scaling based on resource utilization
• Progress tracking and reporting
• Agent pool management with diverse agent types
• Comprehensive metrics aggregation
• Adaptive execution with optimization support

Usage Examples

Basic Parallel Execution

from swarm_benchmark.core import ParallelExecutor, ExecutionMode, ResourceLimits

# Configure resource limits
limits = ResourceLimits(
    max_cpu_percent=80.0,
    max_memory_mb=1024.0,
    max_concurrent_tasks=10
)

# Create executor
executor = ParallelExecutor(
    mode=ExecutionMode.HYBRID,
    limits=limits
)

# Start executor
await executor.start()

# Submit tasks
task_ids = []
for task in tasks:
    task_id = await executor.submit_task(task, priority=1)
    task_ids.append(task_id)

# Wait for completion
await executor.wait_for_completion(timeout=300)

# Get results
results = await executor.get_all_results()

# Shutdown
await executor.stop()

Advanced Orchestration

from swarm_benchmark.core import (
    OrchestrationManager, 
    OrchestrationConfig,
    SchedulingAlgorithm
)

# Configure orchestration
config = OrchestrationConfig(
    execution_mode=ExecutionMode.HYBRID,
    scheduling_algorithm=SchedulingAlgorithm.DYNAMIC,
    enable_work_stealing=True,
    auto_scaling=True,
    max_parallel_benchmarks=10
)

# Create manager
orchestrator = OrchestrationManager(config)

# Run benchmark suite
results = await orchestrator.run_benchmark_suite(
    objectives=["objective1", "objective2", "objective3"],
    config=benchmark_config
)

# Get comprehensive metrics
metrics = orchestrator.get_orchestration_metrics()

Resource Management

Resource Monitoring

• Real-time CPU and memory usage tracking
• Network I/O monitoring (when available)
• Resource violation detection and logging
• Peak usage tracking for capacity planning

Resource Limits

ResourceLimits(
    max_cpu_percent=80.0,      # Maximum CPU usage percentage
    max_memory_mb=1024.0,      # Maximum memory in MB
    max_concurrent_tasks=10,   # Maximum parallel tasks
    max_queue_size=1000,       # Maximum queued tasks
    task_timeout=300,          # Task timeout in seconds
    monitoring_interval=1.0    # Resource check interval
)

Task Scheduling

Task Priority

Tasks can be assigned priorities (higher number = higher priority):

• Priority 1-3: Low priority (eligible for work stealing)
• Priority 4-6: Normal priority
• Priority 7-9: High priority (assigned to best agents)
• Priority 10: Critical (immediate execution)

Agent Capabilities

Agents have capabilities that are matched to task requirements:

• Research: research, analysis, web_search
• Development: development, coding, architecture
• Analysis: analysis, data_processing, statistics
• Testing: testing, validation, quality_assurance
• Optimization: optimization, performance, profiling

Metrics and Monitoring

Execution Metrics

ExecutionMetrics(
    tasks_queued=0,            # Number of tasks waiting
    tasks_running=0,           # Currently executing tasks
    tasks_completed=0,         # Successfully completed tasks
    tasks_failed=0,            # Failed tasks
    total_execution_time=0.0,  # Total execution time
    average_execution_time=0.0,# Average per task
    peak_cpu_usage=0.0,        # Peak CPU percentage
    peak_memory_usage=0.0,     # Peak memory in MB
    throughput=0.0             # Tasks per second
)

Scheduling Metrics

SchedulingMetrics(
    total_scheduled=0,         # Total tasks scheduled
    scheduling_time=0.0,       # Time spent scheduling
    load_balance_score=0.0,    # Load distribution quality (0-1)
    capability_match_score=0.0,# Capability matching quality (0-1)
    max_agent_load=0,          # Maximum tasks per agent
    min_agent_load=0           # Minimum tasks per agent
)

Performance Optimization

Execution Modes

Choose the appropriate execution mode based on your workload:

1. ASYNCIO: Best for I/O-bound tasks with async/await support
2. THREAD: Good for I/O-bound tasks without async support
3. PROCESS: Best for CPU-bound tasks that can be parallelized
4. HYBRID: Automatically selects based on task characteristics

Optimization Tips

1. Use Work Stealing for dynamic workloads with varying task durations
2. Enable Auto-scaling for unpredictable workloads
3. Set appropriate resource limits to prevent system overload
4. Use capability-based scheduling for heterogeneous tasks
5. Monitor metrics to identify bottlenecks and optimize

Error Handling

The system provides comprehensive error handling:

1. Task Failures: Failed tasks are tracked separately with error details
2. Resource Violations: Automatic throttling when limits are exceeded
3. Timeout Handling: Tasks exceeding timeout are cancelled gracefully
4. Graceful Shutdown: All running tasks are completed or cancelled properly

Integration with Benchmark Engine

The parallel execution system integrates seamlessly with the benchmark engine:

# Using OptimizedBenchmarkEngine with parallel execution
engine = OptimizedBenchmarkEngine(
    config=benchmark_config,
    enable_optimizations=True
)

# The engine automatically uses parallel execution
result = await engine.run_benchmark(objective)

Best Practices

1. Start with conservative resource limits and increase based on monitoring
2. Use priority levels to ensure critical tasks complete first
3. Enable work stealing for better load distribution
4. Monitor queue wait times to identify capacity issues
5. Use appropriate execution modes for your task types
6. Implement proper error handling for task failures
7. Set reasonable timeouts to prevent hanging tasks
8. Use auto-scaling for variable workloads

Troubleshooting

High CPU Usage

• Reduce max_concurrent_tasks
• Lower max_cpu_percent limit
• Use THREAD mode instead of PROCESS for I/O tasks

High Memory Usage

• Reduce max_memory_mb limit
• Limit queue size with max_queue_size
• Use streaming/chunking for large data

Poor Load Balance

• Switch to DYNAMIC or WORK_STEALING scheduling
• Enable work stealing
• Check agent capability distribution

Task Timeouts

• Increase task_timeout for long-running tasks
• Break large tasks into smaller subtasks
• Check for resource contention

Future Enhancements

1. Distributed Execution: Support for multi-node execution
2. GPU Support: Resource monitoring and scheduling for GPU tasks
3. Advanced Scheduling: Machine learning-based task scheduling
4. Checkpointing: Save and resume long-running benchmarks
5. Real-time Dashboard: Web-based monitoring interface