Computational Genetic Genealogy

Why Performance Matters

Computational genetic genealogy presents significant performance challenges due to several inherent characteristics:

Key Performance Challenges

• Quadratic Complexity: Many key algorithms have O(n²) complexity or worse, where n is the number of individuals
• Memory Intensity: Processing genetic data requires substantial memory resources
• Data Volume: Genetic data sets can be extremely large (gigabytes to terabytes)
• Optimization Trade-offs: Speed improvements often come at the cost of accuracy or memory usage
• Interactive Requirements: Many applications require near-real-time response for usability

As genetic testing becomes more widespread, these performance challenges are increasingly important to address. Effective performance tuning enables Bonsai v3 to scale to datasets with thousands or even millions of individuals.

Finding the Performance Hotspots

The first step in performance tuning is identifying where the bottlenecks are. Bonsai v3 includes tools and methodologies for comprehensive performance profiling:

Profiling Approaches

1. Time Profiling: Measuring execution time of different code sections
   • Function-level timing
   • Method invocation counts
   • Critical path analysis
2. Memory Profiling: Analyzing memory usage patterns
   • Peak memory usage
   • Memory allocation frequency
   • Garbage collection impact
3. I/O Profiling: Examining data access patterns
   • File read/write operations
   • Database query performance
   • Network communication overheads

The Importance of Realistic Profiling

For effective performance optimization, it's crucial to profile with realistic data and usage patterns:

• Representative Datasets: Use datasets that match real-world scale and characteristics
• Typical Workflows: Profile complete workflows, not just isolated functions
• End-to-End Measurements: Measure performance from the user's perspective, not just internal metrics
• Environment Matching: Profile in environments similar to production deployment

Improving Asymptotic Efficiency

The most powerful performance improvements come from algorithmic optimizations that reduce the fundamental computational complexity. Bonsai v3 implements several key algorithmic improvements:

Key Algorithmic Strategies

1. Filtering and Pruning: Eliminating unnecessary computations
   • Early rejection of impossible relationships
   • Pruning search spaces based on constraints
   • Progressive filtering based on quick preliminary checks
2. Divide and Conquer: Breaking problems into manageable subproblems
   • Hierarchical clustering of individuals
   • Pedigree decomposition into subgraphs
   • Multi-level relationship analysis
3. Dynamic Programming: Avoiding redundant calculations
   • Memoization of expensive function results
   • Bottom-up computation of subproblems
   • Incremental updates for changing data

# Pseudocode for hierarchical relationship analysis
def analyze_relationships_hierarchical(individuals):
    """
    Analyze relationships using a hierarchical approach.
    
    Args:
        individuals: List of individuals to analyze
        
    Returns:
        Dictionary mapping individual pairs to relationships
    """
    # Phase 1: Cluster individuals into groups likely to be related
    clusters = cluster_by_genetic_similarity(individuals)
    
    # Phase 2: Perform coarse-grained analysis to identify potential relatives
    potential_relatives = {}
    for cluster in clusters:
        coarse_results = analyze_cluster_coarse(cluster)
        potential_relatives.update(coarse_results)
    
    # Phase 3: Perform detailed analysis only on potential relatives
    detailed_relationships = {}
    for pair, relatedness_score in potential_relatives.items():
        if relatedness_score > RELATEDNESS_THRESHOLD:
            detailed_relationships[pair] = analyze_pair_detailed(pair)
    
    return detailed_relationships

Algorithm Selection Trade-offs

Choosing the right algorithm involves several key trade-offs:

Efficient Memory Management

Genetic genealogy applications often process large volumes of data, making memory optimization critical. Bonsai v3 implements several approaches to reduce memory usage:

Key Memory Optimization Strategies

1. Data Structure Selection: Choosing memory-efficient data structures
   • Compact array representations instead of object hierarchies
   • Integer IDs instead of string identifiers
   • Bit vectors for boolean data
2. Lazy Loading and Streaming: Processing data incrementally
   • Loading data only when needed
   • Processing data in chunks
   • Releasing memory after processing
3. Caching Strategies: Intelligent memory usage for caching
   • LRU (Least Recently Used) cache eviction
   • Size-based cache limits
   • Selective caching of high-value results

# Memory-intensive representation (approximately 136 bytes per segment)
class IBDSegment:
    def __init__(self, chrom, start_pos, end_pos, cm_length, snp_count):
        self.chrom = chrom          # string: ~24 bytes
        self.start_pos = start_pos  # int: 8 bytes
        self.end_pos = end_pos      # int: 8 bytes
        self.cm_length = cm_length  # float: 8 bytes
        self.snp_count = snp_count  # int: 8 bytes
        # Plus object overhead: ~64 bytes
        # Plus various pointers and alignment: ~16 bytes

# Memory-efficient representation (approximately 32 bytes per segment)
# Using a tuple: (chrom_id, start_pos, end_pos, cm_length, snp_count)
# Where chrom_id is a small integer (1-22, 23=X, 24=Y)

Memory Profiling and Monitoring

Effective memory optimization requires understanding memory usage patterns:

• Peak Memory Usage: Identify the points where memory consumption peaks
• Object Counts: Track how many instances of each type are created
• Lifetime Analysis: Understand how long objects remain in memory
• Allocation Hotspots: Identify code sections with frequent allocations

Leveraging Multiple Processors

Many genetic genealogy algorithms can benefit from parallel execution. Bonsai v3 implements several parallelization strategies to take advantage of multi-core processors:

Key Parallelization Approaches

1. Data Parallelism: Processing different data chunks in parallel
   • Dividing individuals into batches
   • Processing different chromosomes in parallel
   • Parallel evaluation of relationship hypotheses
2. Task Parallelism: Executing different tasks simultaneously
   • Pipeline stages running in parallel
   • Background preprocessing while interactive analysis continues
   • Concurrent I/O and computation
3. Asynchronous Processing: Non-blocking execution models
   • Asynchronous data loading
   • Callback-based processing
   • Producer-consumer patterns

# Pseudocode for parallelized relationship likelihood computation
def compute_all_pairwise_likelihoods_parallel(individual_pairs, num_workers=8):
    """
    Compute relationship likelihoods for multiple pairs in parallel.
    
    Args:
        individual_pairs: List of (id1, id2) tuples to analyze
        num_workers: Number of parallel workers
        
    Returns:
        Dictionary mapping pairs to relationship likelihoods
    """
    # Create a pool of worker processes
    with ProcessPoolExecutor(max_workers=num_workers) as executor:
        # Submit tasks to the pool
        future_to_pair = {
            executor.submit(compute_pairwise_likelihood, id1, id2): (id1, id2)
            for id1, id2 in individual_pairs
        }
        
        # Collect results as they complete
        results = {}
        for future in as_completed(future_to_pair):
            pair = future_to_pair[future]
            try:
                results[pair] = future.result()
            except Exception as e:
                results[pair] = {"error": str(e)}
    
    return results

Parallelization Challenges

Effective parallelization requires addressing several challenges:

• Load Balancing: Ensuring work is evenly distributed across workers
• Synchronization Overhead: Minimizing coordination requirements
• Resource Contention: Managing shared resource access
• Data Dependencies: Handling cases where tasks depend on each other's results

Efficient Data Access Patterns

Many genetic genealogy workflows involve substantial data loading and storage operations. Bonsai v3 implements several I/O optimizations to improve overall performance:

Key I/O Optimization Strategies

1. Data Format Selection: Choosing efficient storage formats
   • Binary formats instead of text formats
   • Column-oriented storage for analytical queries
   • Compressed formats for reduced I/O volume
2. Access Pattern Optimization: Aligning I/O with usage patterns
   • Sequential reads instead of random access
   • Batched operations instead of single operations
   • Memory mapping for large files
3. Caching and Prefetching: Anticipating data needs
   • Preloading likely-to-be-needed data
   • Caching frequently accessed data
   • Background loading of data before it's needed

Optimizing for Different Storage Media

I/O optimization strategies vary based on the storage medium:

• HDD (Hard Disk Drive): Minimize seeking, maximize sequential access, large block reads
• SSD (Solid State Drive): Reduce write amplification, leverage parallelism, smaller reads
• Network Storage: Minimize round trips, batch operations, consider latency
• Memory: Optimize locality, reduce allocation overhead, direct memory access

Low-Level Performance Improvements

Beyond algorithmic and architectural optimizations, Bonsai v3 implements various low-level optimizations to squeeze maximum performance from the underlying hardware:

Key Implementation Optimizations

1. Vectorization: Using SIMD instructions for parallel data processing
   • Vectorized segment comparisons
   • Parallel floating-point operations
   • Batch processing of genetic data
2. Memory Layout: Organizing data for efficient access
   • Structure of Arrays (SoA) instead of Array of Structures (AoS)
   • Alignment for cache line optimization
   • Contiguous memory allocation
3. Computational Shortcuts: Mathematical and logical optimizations
   • Fast approximation functions
   • Lookup tables for expensive calculations
   • Short-circuit evaluation

# Pseudocode for vectorized IBD segment comparison
def compare_segments_vectorized(segments1, segments2):
    """
    Compare two sets of segments using vectorized operations.
    
    Args:
        segments1: First set of segments as structured numpy array
        segments2: Second set of segments as structured numpy array
        
    Returns:
        Array of overlap measures
    """
    # Extract start and end positions as contiguous arrays
    starts1 = segments1['start_pos']
    ends1 = segments1['end_pos']
    starts2 = segments2['start_pos']
    ends2 = segments2['end_pos']
    
    # Calculate overlap matrix using vectorized operations
    # For each pair of segments (i, j):
    # overlap[i, j] = min(ends1[i], ends2[j]) - max(starts1[i], starts2[j])
    # This performs the whole calculation in parallel using SIMD
    starts1_matrix = starts1[:, np.newaxis]
    ends1_matrix = ends1[:, np.newaxis]
    starts2_matrix = starts2[np.newaxis, :]
    ends2_matrix = ends2[np.newaxis, :]
    
    max_starts = np.maximum(starts1_matrix, starts2_matrix)
    min_ends = np.minimum(ends1_matrix, ends2_matrix)
    overlap = np.maximum(0, min_ends - max_starts)
    
    return overlap

Language and Library Selection

Bonsai v3 strategically uses different languages and libraries for different components based on performance characteristics:

Adapting to Different Usage Scenarios

Different genetic genealogy applications have different performance requirements and constraints. Bonsai v3 provides configuration options to tune performance for specific workloads:

Key Configurable Parameters

• Memory Limits: Controlling maximum memory usage
  • Cache size limits
  • Batch processing thresholds
  • Precomputation settings
• Parallelism Settings: Configuring parallel execution
  • Worker thread/process count
  • Task prioritization rules
  • Load balancing approaches
• Algorithm Selection: Choosing between alternative implementations
  • Exact vs. approximate algorithms
  • Memory-optimized vs. speed-optimized variants
  • Specialized algorithms for specific data patterns

Dynamic Configuration Adjustment

Bonsai v3 can dynamically adjust its configuration based on runtime conditions:

• Workload Adaptation: Adjusting parameters based on dataset characteristics
• Resource Sensing: Modifying behavior based on available system resources
• Performance Monitoring: Changing strategies based on observed performance
• User Priority: Adjusting optimization targets based on user preferences

Performance tuning is a critical aspect of computational genetic genealogy, enabling the processing of large-scale datasets within reasonable time and resource constraints. Bonsai v3 implements a comprehensive set of performance optimizations, from high-level algorithmic improvements to low-level implementation details, providing efficient operation across a wide range of usage scenarios.

By understanding and applying these performance optimization techniques, you can adapt Bonsai v3 to your specific needs, whether you're analyzing small family groups interactively or processing large populations in batch mode.

In the next lab, we'll explore custom prior probability models in Bonsai v3, which allow for incorporating demographic information and domain-specific knowledge to enhance the accuracy of relationship predictions.