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**Table of Contents**

- [Hive Mind Swarm Performance Benchmark Results](#hive-mind-swarm-performance-benchmark-results)
  - [Executive Summary](#executive-summary)
    - [Key Performance Indicators (Baseline)](#key-performance-indicators-baseline)
  - [Running Benchmarks](#running-benchmarks)
    - [Prerequisites](#prerequisites)
    - [Execute All Benchmarks](#execute-all-benchmarks)
    - [Benchmark Suites](#benchmark-suites)
      - [1. Hive Coordination Benchmarks (`ggen-core`)](#1-hive-coordination-benchmarks-ggen-core)
      - [2. Swarm Coordination Benchmarks (`ggen-ai`)](#2-swarm-coordination-benchmarks-ggen-ai)
      - [3. Swarm Primitives Benchmarks (`ggen-domain`)](#3-swarm-primitives-benchmarks-ggen-domain)
  - [Expected Performance Characteristics](#expected-performance-characteristics)
    - [Consensus Mechanism (HiveQueen)](#consensus-mechanism-hivequeen)
    - [Agent Coordination (SwarmCoordinator)](#agent-coordination-swarmcoordinator)
    - [Lock-Free Primitives (Domain)](#lock-free-primitives-domain)
  - [Performance Optimization Opportunities](#performance-optimization-opportunities)
    - [Critical Path Optimizations (80/20)](#critical-path-optimizations-8020)
      - [1. Consensus Mechanism](#1-consensus-mechanism)
      - [2. Agent Spawning](#2-agent-spawning)
      - [3. Lock-Free Snapshot Reads](#3-lock-free-snapshot-reads)
    - [Secondary Optimizations](#secondary-optimizations)
      - [4. Memory Pool for Agents](#4-memory-pool-for-agents)
      - [5. Pipeline Stage Batching](#5-pipeline-stage-batching)
      - [6. Adaptive Timeout Tuning](#6-adaptive-timeout-tuning)
  - [Bottleneck Analysis](#bottleneck-analysis)
    - [Identified Bottlenecks](#identified-bottlenecks)
      - [High Priority (Fix First)](#high-priority-fix-first)
      - [Medium Priority](#medium-priority)
      - [Low Priority](#low-priority)
  - [Resource Utilization](#resource-utilization)
    - [Expected CPU Usage](#expected-cpu-usage)
    - [Expected Memory Usage](#expected-memory-usage)
    - [Expected Network I/O](#expected-network-io)
  - [Scalability Analysis](#scalability-analysis)
    - [Horizontal Scalability](#horizontal-scalability)
    - [Vertical Scalability](#vertical-scalability)
  - [Comparison with Baselines](#comparison-with-baselines)
    - [Industry Benchmarks](#industry-benchmarks)
  - [Next Steps](#next-steps)
    - [Immediate Actions](#immediate-actions)
    - [Short-term (1-2 weeks)](#short-term-1-2-weeks)
    - [Long-term (1-2 months)](#long-term-1-2-months)
  - [Appendix: Benchmark Configuration](#appendix-benchmark-configuration)
    - [Hardware Requirements](#hardware-requirements)
    - [Software Requirements](#software-requirements)
    - [Environment Variables](#environment-variables)

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# Hive Mind Swarm Performance Benchmark Results

## Executive Summary

This document presents comprehensive performance benchmarks for the ggen Hive Mind swarm coordination system, focusing on the critical 20% of operations that represent 80% of usage.

### Key Performance Indicators (Baseline)

| Metric | Target | Status | Notes |
|--------|--------|--------|-------|
| Consensus Latency (p50) | < 100ms | ⏱️ TBD | For 10 packs |
| Consensus Latency (p99) | < 500ms | ⏱️ TBD | For 10 packs |
| Agent Spawning | < 50ms | ⏱️ TBD | For 8 agents |
| Throughput | > 100 ops/sec | ⏱️ TBD | Sustained |
| Memory Overhead | < 10MB | ⏱️ TBD | Per 100 agents |
| Snapshot Read Latency | < 1μs | ⏱️ TBD | Lock-free |
| Conflict Detection | < 200ms | ⏱️ TBD | For 50 packs |

## Running Benchmarks

### Prerequisites
```bash
# Install Rust toolchain
rustup update

# Install criterion dependencies
cargo install cargo-criterion
```

### Execute All Benchmarks
```bash
# Run all swarm benchmarks
cargo bench --workspace

# Run specific benchmark suite
cargo bench --bench hive_coordination
cargo bench --bench swarm_coordination
cargo bench --bench swarm_primitives

# Generate HTML reports
cargo criterion
```

### Benchmark Suites

#### 1. Hive Coordination Benchmarks (`ggen-core`)

**Focus**: HiveQueen orchestration and consensus mechanisms

```bash
cd crates/ggen-core
cargo bench --bench hive_coordination
```

**Benchmarks**:
- `consensus_latency` - Consensus orchestration for 1-20 packs
- `agent_spawning` - Agent initialization overhead (4-32 agents)
- `conflict_detection` - Conflict detection performance (5-50 packs)
- `config_analysis` - Configuration analysis phase
- `parallel_agents` - Parallel agent execution (2-16 concurrent)
- `memory_overhead` - Memory allocation patterns
- `orchestration_throughput` - End-to-end throughput (10-100 iterations)

#### 2. Swarm Coordination Benchmarks (`ggen-ai`)

**Focus**: Pipeline execution and agent coordination

```bash
cd crates/ggen-ai
cargo bench --bench swarm_coordination
```

**Benchmarks**:
- `pipeline_execution` - Pipeline latency (3-15 stages)
- `dependency_resolution` - Dependency graph traversal (2-8 depth)
- `concurrent_execution` - Concurrent swarm execution (2-20 swarms)
- `semaphore_coordination` - Semaphore overhead (5-50 permits)
- `timeout_overhead` - Timeout handling cost (10-500ms)
- `artifact_conversion` - Artifact serialization (10-200 artifacts)
- `swarm_memory` - Memory allocation for large swarms (10-200 instances)

#### 3. Swarm Primitives Benchmarks (`ggen-domain`)

**Focus**: Low-level lock-free coordination primitives

```bash
cd crates/ggen-domain
cargo bench --bench swarm_primitives
```

**Benchmarks**:
- `snapshot_reads` - Lock-free snapshot read performance (10-1000 readers)
- `snapshot_writes` - Snapshot staging and commit (10-200 iterations)
- `commutative_aggregation` - Commutative proposal merging (10-500 proposals)
- `conditional_aggregation` - Conditional proposal with conflicts (10-200 proposals)
- `priority_scheduling` - Priority queue operations (10-1000 tasks)
- `scheduler_queue_depth` - Queue depth calculation (100-5000 tasks)
- `conflict_detection` - Pairwise conflict detection (10-500 proposals)
- `proposal_merging` - Sequential merge performance (2-20 proposals)
- `scheduling_hint_sorting` - Sort key calculation and sorting (100-5000 hints)

## Expected Performance Characteristics

### Consensus Mechanism (HiveQueen)

**Complexity Analysis**:
- **Time**: O(n²) for conflict detection (n = number of packs)
- **Space**: O(n + a) where a = number of agents
- **Parallelization**: Multi-agent analysis can run concurrently

**Expected Latencies**:
| Pack Count | Expected p50 | Expected p99 | Throughput |
|------------|--------------|--------------|------------|
| 1-3 | < 50ms | < 100ms | 200+ ops/sec |
| 4-10 | < 100ms | < 300ms | 100+ ops/sec |
| 11-20 | < 250ms | < 750ms | 50+ ops/sec |
| 21-50 | < 500ms | < 1500ms | 20+ ops/sec |

### Agent Coordination (SwarmCoordinator)

**Complexity Analysis**:
- **Time**: O(s + d) where s = stages, d = dependency edges
- **Space**: O(s * a) where a = artifacts per stage
- **Parallelization**: Independent stages can execute concurrently

**Expected Latencies**:
| Pipeline Depth | Expected p50 | Expected p99 | Throughput |
|----------------|--------------|--------------|------------|
| 3-5 stages | < 100ms | < 300ms | 150+ ops/sec |
| 6-10 stages | < 200ms | < 600ms | 75+ ops/sec |
| 11-15 stages | < 400ms | < 1200ms | 40+ ops/sec |

### Lock-Free Primitives (Domain)

**Complexity Analysis**:
- **Snapshot Reads**: O(1) lock-free
- **Proposal Aggregation**: O(n) for n proposals
- **Priority Scheduling**: O(1) enqueue, O(1) dequeue
- **Conflict Detection**: O(n²) pairwise comparison

**Expected Latencies**:
| Operation | Expected p50 | Expected p99 | Throughput |
|-----------|--------------|--------------|------------|
| Snapshot Read | < 100ns | < 500ns | 10M+ ops/sec |
| Snapshot Write | < 5μs | < 20μs | 200K+ ops/sec |
| Proposal Merge | < 10μs | < 50μs | 100K+ ops/sec |
| Queue Enqueue | < 1μs | < 5μs | 1M+ ops/sec |
| Queue Dequeue | < 1μs | < 5μs | 1M+ ops/sec |

## Performance Optimization Opportunities

### Critical Path Optimizations (80/20)

#### 1. Consensus Mechanism
**Current**: O(n²) conflict detection
**Optimization**: Implement conflict-free hash maps
**Expected Gain**: 40-60% reduction in latency for >10 packs

```rust
// Before: Pairwise comparison
for i in 0..n {
    for j in (i+1)..n {
        check_conflict(i, j);
    }
}

// After: Hash-based conflict detection
let mut namespace_map: HashMap<String, PackRef> = HashMap::new();
for pack in packs {
    if let Some(conflict) = namespace_map.insert(pack.namespace, pack) {
        // Conflict detected in O(1)
    }
}
```

#### 2. Agent Spawning
**Current**: Sequential agent creation
**Optimization**: Parallel agent initialization
**Expected Gain**: 3-5x speedup for >8 agents

```rust
// Before: Sequential
let mut agents = vec![];
for role in roles {
    agents.push(HiveAgent::new(role));
}

// After: Parallel with rayon
let agents: Vec<_> = roles.par_iter()
    .map(|role| HiveAgent::new(role))
    .collect();
```

#### 3. Lock-Free Snapshot Reads
**Current**: Arc cloning overhead
**Optimization**: Epoch-based reclamation
**Expected Gain**: 20-30% reduction in read latency

```rust
// Before: Arc clone on every read
pub fn current(&self) -> Arc<SnapshotDescriptor> {
    Arc::clone(&self.descriptor)
}

// After: Crossbeam epoch-based
pub fn current(&self) -> &SnapshotDescriptor {
    let guard = epoch::pin();
    self.descriptor.load(Ordering::Acquire, &guard)
}
```

### Secondary Optimizations

#### 4. Memory Pool for Agents
**Expected Gain**: 30-40% reduction in allocation overhead

#### 5. Pipeline Stage Batching
**Expected Gain**: 25-35% improvement in throughput

#### 6. Adaptive Timeout Tuning
**Expected Gain**: 15-25% reduction in false timeouts

## Bottleneck Analysis

### Identified Bottlenecks

#### High Priority (Fix First)
1. **Conflict Detection** - O(n²) complexity
   - **Impact**: Critical for >10 packs
   - **Effort**: Medium (2-4 hours)
   - **Gain**: 40-60% latency reduction

2. **Sequential Agent Spawning** - No parallelization
   - **Impact**: High for >8 agents
   - **Effort**: Low (1-2 hours)
   - **Gain**: 3-5x speedup

3. **Snapshot Arc Cloning** - Allocation overhead
   - **Impact**: Medium for high-read workloads
   - **Effort**: High (4-8 hours, requires crossbeam)
   - **Gain**: 20-30% read latency reduction

#### Medium Priority
4. **Pipeline Stage Execution** - No stage batching
   - **Impact**: Medium for deep pipelines
   - **Effort**: Medium (3-5 hours)
   - **Gain**: 25-35% throughput

5. **Memory Allocation** - No pooling
   - **Impact**: Low-medium for large swarms
   - **Effort**: High (6-8 hours)
   - **Gain**: 30-40% allocation reduction

#### Low Priority
6. **Artifact Serialization** - Not optimized
   - **Impact**: Low
   - **Effort**: Low (1-2 hours)
   - **Gain**: 10-15% improvement

## Resource Utilization

### Expected CPU Usage
- **Consensus**: 20-40% per core (single-threaded)
- **Parallel Agents**: 60-80% across all cores
- **Lock-Free Ops**: < 5% (highly efficient)

### Expected Memory Usage
- **HiveQueen**: ~100KB base + ~10KB per pack
- **SwarmCoordinator**: ~50KB base + ~5KB per stage
- **Agent**: ~20KB per agent instance
- **Snapshot**: ~5KB per snapshot version

### Expected Network I/O
- **Inter-Agent Communication**: 10-50KB/sec per agent
- **Memory Coordination**: 1-5KB/sec overhead

## Scalability Analysis

### Horizontal Scalability
- **Linear scaling**: Up to 8-16 agents (CPU bound)
- **Sublinear scaling**: 16-32 agents (coordination overhead)
- **Diminishing returns**: >32 agents (Amdahl's law)

### Vertical Scalability
- **CPU**: Scales well with cores (embarrassingly parallel agents)
- **Memory**: Linear growth with pack/agent count
- **I/O**: Not a bottleneck for typical workloads

## Comparison with Baselines

### Industry Benchmarks

| System | Consensus Latency | Throughput | Agent Spawning |
|--------|-------------------|------------|----------------|
| **ggen Hive** | TBD | TBD | TBD |
| Kubernetes Scheduler | 50-200ms | 100 ops/sec | 20-50ms |
| Apache Kafka | 2-10ms | 1M+ msgs/sec | N/A |
| Akka Cluster | 10-50ms | 10K actors/sec | 1-5ms |

**Target**: Match or exceed Kubernetes for coordination, approach Akka for agent spawning

## Next Steps

### Immediate Actions
1. **Run baseline benchmarks** - Establish current performance
2. **Identify top 3 bottlenecks** - Focus on 80/20 wins
3. **Implement quick wins** - Parallel agent spawning (1-2 hours)

### Short-term (1-2 weeks)
4. **Hash-based conflict detection** - O(n²) → O(n)
5. **Profile memory allocations** - Identify pool candidates
6. **Optimize snapshot reads** - Reduce Arc overhead

### Long-term (1-2 months)
7. **Epoch-based reclamation** - Lock-free improvements
8. **Memory pooling** - Agent/snapshot pools
9. **Adaptive tuning** - Machine learning for timeout/batch sizing

## Appendix: Benchmark Configuration

### Hardware Requirements
- **CPU**: 4+ cores (8+ recommended)
- **RAM**: 8GB minimum (16GB recommended)
- **Storage**: 1GB for benchmark artifacts

### Software Requirements
- **Rust**: 1.70+
- **Criterion**: 0.7+
- **Tokio**: 1.47+

### Environment Variables
```bash
# Enable detailed profiling
export CARGO_PROFILE_BENCH_DEBUG=true

# Set benchmark iterations
export CRITERION_SAMPLE_SIZE=100
export CRITERION_MEASUREMENT_TIME=10

# Enable flamegraph generation
export CARGO_PROFILE_BENCH_DEBUG=true
cargo bench --bench hive_coordination -- --profile-time=10
```

---

**Report Generated**: 2025-11-19
**Benchmark Version**: v1.0.0
**Status**: Baseline benchmarks pending execution
