Performance & Optimization Guide

This guide provides detailed information on optimizing Lavoisier’s performance for different computational environments and analysis requirements.

Table of Contents

1. Performance & Optimization Guide
   1. System Requirements & Scaling
      1. Minimum System Requirements
      2. Performance Scaling Characteristics
         1. CPU Scaling
         2. Memory Scaling
      3. Platform-Specific Optimizations
         1. Linux (Recommended)
         2. macOS
         3. Windows
   2. Memory Optimization
      1. Adaptive Memory Management
         1. Memory Pool Configuration
         2. Out-of-Core Processing
      2. Memory Profiling and Debugging
         1. Built-in Memory Profiler
         2. Memory Optimization Settings
   3. Parallel Processing Optimization
      1. Multi-Core Utilization
         1. Automatic Core Detection and Allocation
         2. Custom Parallel Strategies
      2. NUMA Optimization
         1. NUMA-Aware Configuration
      3. GPU Acceleration
         1. CUDA Configuration
         2. ROCm Support (AMD GPUs)
   4. I/O Optimization
      1. File System Optimization
         1. High-Performance File I/O
         2. Storage Optimization
      2. Network I/O (Distributed Processing)
         1. Cluster Configuration
   5. Algorithm-Specific Optimizations
      1. Peak Detection Optimization
         1. Wavelet Transform Acceleration
      2. Spectral Matching Optimization
         1. Similarity Search Acceleration
      3. Machine Learning Optimization
         1. Model Inference Acceleration
   6. Monitoring and Profiling
      1. Performance Monitoring
         1. Real-Time Performance Dashboard
         2. Benchmarking Tools
      2. Profiling Tools
         1. CPU Profiling
         2. Memory Profiling
   7. Configuration Optimization
      1. Adaptive Configuration
         1. Auto-Tuning System
      2. Environment-Specific Optimization
         1. Cloud Optimization
         2. HPC Cluster Optimization
   8. Troubleshooting Performance Issues
      1. Common Performance Bottlenecks
         1. Memory Bottlenecks
         2. I/O Bottlenecks
      2. Performance Tuning Recommendations
         1. Systematic Optimization Approach

---

System Requirements & Scaling

Minimum System Requirements

Component	Minimum	Recommended	High-Performance
CPU	4 cores, 2.5 GHz	16 cores, 3.0 GHz	32+ cores, 3.5+ GHz
RAM	16 GB	64 GB	128+ GB
Storage	100 GB SSD	1 TB NVMe SSD	Multi-TB NVMe RAID
GPU	Optional	RTX 3070+	RTX 4090+ or A100

Performance Scaling Characteristics

CPU Scaling

Lavoisier demonstrates excellent CPU scaling with near-linear performance improvements up to 32 cores for typical workloads:

Cores:  4    8    16   32   64   128
Speed:  1x   1.9x 3.7x 7.1x 12x  18x

Memory Scaling

Memory requirements scale with dataset size and analysis complexity:

• Small datasets (< 1GB): 16GB RAM sufficient
• Medium datasets (1-10GB): 64GB RAM recommended
• Large datasets (10-100GB): 128GB+ RAM optimal
• Very large datasets (100GB+): Streaming processing with 256GB+ RAM

Platform-Specific Optimizations

Linux (Recommended)

# Optimize CPU governor for performance
echo performance | sudo tee /sys/devices/system/cpu/cpu*/cpufreq/scaling_governor

# Increase memory limits
echo 'vm.max_map_count=262144' >> /etc/sysctl.conf
echo 'vm.swappiness=1' >> /etc/sysctl.conf

# Set NUMA policy for optimal memory allocation
numactl --interleave=all python -m lavoisier analyze data.mzML

macOS

# Increase memory limits
sudo sysctl -w kern.maxfiles=65536
sudo sysctl -w kern.maxfilesperproc=65536

# Use native accelerate framework
export LAVOISIER_USE_ACCELERATE=1

Windows

# Set memory allocation for large datasets
$env:LAVOISIER_MEMORY_LIMIT = "32GB"

# Enable Windows performance mode
powercfg /setactive 8c5e7fda-e8bf-4a96-9a85-a6e23a8c635c

---

Memory Optimization

Adaptive Memory Management

Lavoisier implements sophisticated memory management strategies that automatically adapt to available system resources:

Memory Pool Configuration

from lavoisier.core import MemoryManager

# Configure memory pools for optimal performance
memory_manager = MemoryManager(
    pool_sizes={
        'spectrum_buffer': '4GB',
        'feature_cache': '2GB',
        'result_buffer': '1GB'
    },
    allocation_strategy='adaptive',  # 'aggressive', 'conservative', 'adaptive'
    garbage_collection_threshold=0.8
)

# Apply configuration globally
memory_manager.apply_global_config()

Out-of-Core Processing

For datasets exceeding available memory, enable streaming processing:

from lavoisier import StreamingAnalyzer

analyzer = StreamingAnalyzer(
    chunk_size='auto',  # Automatically determine optimal chunk size
    memory_limit='80%',  # Use 80% of available memory
    cache_strategy='lru',  # Least Recently Used caching
    compression_level='adaptive'  # Dynamic compression based on data characteristics
)

# Process large dataset in chunks
results = analyzer.process_large_dataset(
    input_path="large_dataset.mzML",
    output_path="results/",
    progress_callback=lambda p: print(f"Progress: {p:.1%}")
)

Memory Profiling and Debugging

Built-in Memory Profiler

from lavoisier.utils import MemoryProfiler

# Enable memory profiling
with MemoryProfiler() as profiler:
    results = analyzer.process_file("data.mzML")

# Analyze memory usage patterns
profiler.generate_report("memory_report.html")
profiler.identify_bottlenecks()

Memory Optimization Settings

# config/memory_optimization.yaml
memory:
  pool_management:
    enable_pools: true
    initial_pool_size: "2GB"
    max_pool_size: "16GB"
    growth_factor: 1.5
  
  garbage_collection:
    strategy: "adaptive"
    threshold: 0.85
    frequency: "auto"
  
  caching:
    enable_smart_cache: true
    cache_size: "4GB"
    eviction_policy: "lru"
    prefetch_strategy: "predictive"

---

Parallel Processing Optimization

Multi-Core Utilization

Automatic Core Detection and Allocation

from lavoisier.core import ParallelProcessor

# Automatic optimization based on system capabilities
processor = ParallelProcessor(
    cores='auto',  # Automatically detect and use optimal core count
    numa_aware=True,  # Enable NUMA-aware processing
    hyperthreading=False,  # Disable for compute-intensive tasks
    thread_affinity=True  # Pin threads to specific cores
)

# Configure workload distribution
processor.configure(
    distribution_strategy='dynamic',  # 'static', 'dynamic', 'work_stealing'
    load_balancing='predictive',  # 'round_robin', 'predictive', 'adaptive'
    synchronization='minimal'  # Minimize synchronization overhead
)

Custom Parallel Strategies

# For CPU-bound tasks (spectral processing)
cpu_strategy = ParallelStrategy(
    backend='ray',  # 'ray', 'dask', 'multiprocessing'
    workers='auto',
    memory_per_worker='4GB',
    task_granularity='medium'
)

# For I/O-bound tasks (file processing)
io_strategy = ParallelStrategy(
    backend='asyncio',
    concurrent_tasks=32,
    buffer_size='256MB',
    task_granularity='fine'
)

NUMA Optimization

NUMA-Aware Configuration

from lavoisier.core import NUMAOptimizer

numa_optimizer = NUMAOptimizer()

# Analyze system topology
topology = numa_optimizer.analyze_topology()
print(f"NUMA nodes: {topology.num_nodes}")
print(f"Cores per node: {topology.cores_per_node}")

# Configure NUMA-aware processing
numa_config = numa_optimizer.optimize_for_workload(
    workload_type='ms_analysis',
    data_locality='high',  # Optimize for data locality
    memory_binding='local'  # Bind memory to local NUMA node
)

# Apply NUMA optimization
processor.apply_numa_config(numa_config)

GPU Acceleration

CUDA Configuration

from lavoisier.gpu import CUDAAccelerator

# Initialize CUDA acceleration
cuda_accelerator = CUDAAccelerator(
    device_selection='auto',  # Automatically select best GPU
    memory_fraction=0.8,  # Use 80% of GPU memory
    mixed_precision=True,  # Enable mixed precision for faster processing
    stream_optimization=True
)

# Configure GPU-accelerated analysis
analyzer = MSAnalyzer(
    accelerator=cuda_accelerator,
    gpu_tasks=['peak_detection', 'spectral_matching', 'ml_inference'],
    fallback_to_cpu=True  # Graceful fallback if GPU unavailable
)

ROCm Support (AMD GPUs)

from lavoisier.gpu import ROCmAccelerator

rocm_accelerator = ROCmAccelerator(
    device='auto',
    optimization_level='aggressive',
    memory_pool_size='auto'
)

---

I/O Optimization

File System Optimization

High-Performance File I/O

from lavoisier.io import OptimizedFileReader

# Configure optimized file reading
file_reader = OptimizedFileReader(
    buffer_size='64MB',  # Larger buffers for sequential reads
    prefetch_strategy='aggressive',  # Prefetch data based on access patterns
    compression_aware=True,  # Optimize for compressed files
    memory_mapping=True  # Use memory-mapped files for large datasets
)

# Parallel file processing
parallel_reader = ParallelFileReader(
    concurrent_files=4,  # Process multiple files simultaneously
    io_threads=8,  # Dedicated I/O threads
    coordination_strategy='pipeline'  # Pipeline I/O and processing
)

Storage Optimization

# config/storage_optimization.yaml
storage:
  read_optimization:
    buffer_size: "64MB"
    prefetch_size: "256MB"
    readahead_strategy: "aggressive"
  
  write_optimization:
    write_buffer_size: "128MB"
    sync_strategy: "delayed"
    compression_level: "adaptive"
  
  cache_optimization:
    page_cache_size: "2GB"
    metadata_cache: "512MB"
    directory_cache: "256MB"

Network I/O (Distributed Processing)

Cluster Configuration

from lavoisier.distributed import ClusterManager

# Configure distributed cluster
cluster = ClusterManager(
    nodes=['node1:8786', 'node2:8786', 'node3:8786'],
    scheduler_options={
        'bandwidth_limit': '1GB/s',
        'compression': 'lz4',
        'serialization': 'msgpack'
    },
    worker_options={
        'memory_limit': '32GB',
        'nthreads': 16,
        'processes': False  # Use threads for GIL-free operations
    }
)

# Optimize network communication
cluster.optimize_communication(
    protocol='tcp',  # or 'infiniband' for HPC environments
    compression='adaptive',
    batching_strategy='dynamic'
)

---

Algorithm-Specific Optimizations

Peak Detection Optimization

Wavelet Transform Acceleration

from lavoisier.algorithms import OptimizedPeakDetector

# Configure optimized peak detection
peak_detector = OptimizedPeakDetector(
    wavelet_backend='pywt-fast',  # Optimized PyWavelets backend
    fft_backend='fftw',  # Use FFTW for faster FFTs
    parallel_scales=True,  # Parallelize across wavelet scales
    memory_efficient=True  # Trade memory for speed
)

# Enable specialized optimizations
peak_detector.enable_optimizations([
    'vectorized_operations',  # SIMD vectorization
    'cache_friendly_access',  # Optimize memory access patterns
    'loop_unrolling',  # Unroll critical loops
    'branch_prediction'  # Optimize conditional branches
])

Spectral Matching Optimization

Similarity Search Acceleration

from lavoisier.matching import AcceleratedMatcher

# Configure high-speed spectral matching
matcher = AcceleratedMatcher(
    similarity_algorithm='enhanced_dot_product',
    index_type='lsh_forest',  # Locality-sensitive hashing
    search_strategy='approximate',  # Trade accuracy for speed
    cache_size='8GB',  # Large cache for frequently accessed spectra
    batch_processing=True
)

# Optimize for specific use cases
matcher.configure_for_use_case(
    use_case='high_throughput',  # 'high_accuracy', 'high_throughput', 'balanced'
    accuracy_threshold=0.95,
    speed_priority=0.8
)

Machine Learning Optimization

Model Inference Acceleration

from lavoisier.ml import OptimizedInference

# Configure optimized ML inference
inference_engine = OptimizedInference(
    model_format='onnx',  # Optimized model format
    execution_provider='cuda',  # or 'tensorrt', 'openvino'
    optimization_level='aggressive',
    batch_size='auto',  # Automatically determine optimal batch size
    precision='mixed'  # Use mixed precision for speed
)

# Enable hardware-specific optimizations
inference_engine.enable_optimizations([
    'tensorrt_acceleration',  # NVIDIA TensorRT
    'graph_optimization',  # Computational graph optimization
    'kernel_fusion',  # Fuse operations for efficiency
    'constant_folding'  # Pre-compute constant operations
])

---

Monitoring and Profiling

Performance Monitoring

Real-Time Performance Dashboard

from lavoisier.monitoring import PerformanceMonitor

# Initialize performance monitoring
monitor = PerformanceMonitor(
    metrics=['cpu_usage', 'memory_usage', 'io_throughput', 'gpu_utilization'],
    sampling_interval=1.0,  # Sample every second
    alert_thresholds={
        'cpu_usage': 90,
        'memory_usage': 85,
        'io_wait': 20
    }
)

# Start monitoring
with monitor:
    results = analyzer.process_dataset("large_dataset.mzML")

# Generate performance report
monitor.generate_report("performance_report.html")

Benchmarking Tools

from lavoisier.benchmarks import PerformanceBenchmark

# Run comprehensive benchmark
benchmark = PerformanceBenchmark(
    test_datasets=['small', 'medium', 'large'],
    algorithms=['peak_detection', 'spectral_matching', 'annotation'],
    metrics=['throughput', 'accuracy', 'memory_usage', 'energy_consumption']
)

results = benchmark.run_comprehensive_benchmark()
benchmark.compare_with_baseline(results)

Profiling Tools

CPU Profiling

from lavoisier.profiling import CPUProfiler

# Profile CPU-intensive operations
with CPUProfiler(output_format='flamegraph') as profiler:
    results = analyzer.process_file("data.mzML")

# Analyze hotspots
hotspots = profiler.identify_hotspots(threshold=0.05)
profiler.suggest_optimizations(hotspots)

Memory Profiling

from lavoisier.profiling import MemoryProfiler

# Track memory allocations and deallocations
with MemoryProfiler(track_allocations=True) as profiler:
    results = analyzer.process_large_dataset("dataset/")

# Identify memory leaks and inefficiencies
leaks = profiler.detect_memory_leaks()
profiler.suggest_memory_optimizations()

---

Configuration Optimization

Adaptive Configuration

Auto-Tuning System

from lavoisier.optimization import AutoTuner

# Initialize auto-tuning system
tuner = AutoTuner(
    optimization_target='throughput',  # 'throughput', 'accuracy', 'memory', 'balanced'
    search_strategy='bayesian',  # Bayesian optimization for parameter search
    evaluation_budget=50,  # Number of configurations to evaluate
    hardware_aware=True  # Consider hardware characteristics
)

# Optimize configuration for specific workload
optimal_config = tuner.optimize_for_workload(
    workload_type='metabolomics_analysis',
    dataset_characteristics={'size': 'large', 'complexity': 'high'},
    performance_constraints={'max_memory': '64GB', 'max_time': '2h'}
)

# Apply optimized configuration
analyzer.apply_configuration(optimal_config)

Environment-Specific Optimization

Cloud Optimization

# AWS EC2 optimization
if environment.is_aws_ec2():
    config.enable_optimizations([
        'ebs_throughput_optimization',
        'instance_store_caching',
        'enhanced_networking',
        'placement_group_awareness'
    ])

# Google Cloud optimization
elif environment.is_gcp():
    config.enable_optimizations([
        'persistent_disk_optimization',
        'local_ssd_caching',
        'custom_machine_type_optimization'
    ])

# Azure optimization
elif environment.is_azure():
    config.enable_optimizations([
        'premium_ssd_optimization',
        'accelerated_networking',
        'proximity_placement_groups'
    ])

HPC Cluster Optimization

# SLURM cluster configuration
if environment.is_slurm_cluster():
    config.configure_for_hpc(
        job_scheduler='slurm',
        interconnect='infiniband',
        storage_system='lustre',
        mpi_implementation='openmpi'
    )
    
    # Enable HPC-specific optimizations
    config.enable_optimizations([
        'mpi_communication_optimization',
        'parallel_file_system_optimization',
        'numa_topology_awareness',
        'job_packing_optimization'
    ])

---

Troubleshooting Performance Issues

Common Performance Bottlenecks

Memory Bottlenecks

# Diagnose memory issues
from lavoisier.diagnostics import MemoryDiagnostics

diagnostics = MemoryDiagnostics()
memory_issues = diagnostics.analyze_memory_usage()

if memory_issues.has_memory_leaks():
    print("Memory leaks detected:")
    for leak in memory_issues.memory_leaks:
        print(f"  {leak.location}: {leak.size_mb} MB")

if memory_issues.has_excessive_allocation():
    print("Excessive memory allocation detected")
    print(f"Recommendation: Increase chunk size to {memory_issues.recommended_chunk_size}")

I/O Bottlenecks

# Diagnose I/O performance
from lavoisier.diagnostics import IODiagnostics

io_diagnostics = IODiagnostics()
io_analysis = io_diagnostics.analyze_io_patterns()

if io_analysis.is_io_bound():
    print("I/O bottleneck detected")
    print(f"Read throughput: {io_analysis.read_throughput_mbps} MB/s")
    print(f"Recommended buffer size: {io_analysis.recommended_buffer_size}")

Performance Tuning Recommendations

Systematic Optimization Approach

1. Profile Before Optimizing

   lavoisier profile --input data.mzML --output profile_report.html
   

2. Identify Bottlenecks

   lavoisier analyze-bottlenecks --profile profile_report.html
   

3. Apply Targeted Optimizations

   lavoisier optimize --target cpu_usage --config optimized_config.yaml
   

4. Validate Performance Improvements

   lavoisier benchmark --before baseline.json --after optimized.json
   

This comprehensive performance guide enables users to maximize Lavoisier’s computational efficiency across diverse hardware environments and analysis requirements. The combination of automatic optimization, manual tuning options, and detailed monitoring ensures optimal performance for any metabolomics analysis workflow.