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Parallel Processing

This example demonstrates how to use parallel processing for analyzing large datasets.

Setup

First, let's set up our project directory and load the package:

using StochasticGene

# Create project directory
rna_setup("parallel_example")
cd("parallel_example")

Setting Up Parallel Processing

Initialize parallel processing with the desired number of workers:

# Set up parallel processing
setup_parallel(nworkers=4)  # Adjust based on your system

Fitting Models in Parallel

Basic Parallel Fitting

# Fit multiple models in parallel
fits = fit_parallel(
    G = 2,
    R = 0,
    transitions = ([1,2], [2,1]),
    datatype = "rna",
    datapath = "data/",
    gene = "MYC",
    nchains = 4  # Number of parallel chains
)

Hierarchical Parallel Fitting

# Fit hierarchical models in parallel
fits = fit_hierarchical(
    G = 2,
    R = 0,
    transitions = ([1,2], [2,1]),
    datatype = "rna",
    datapath = "data/",
    gene = "MYC",
    nchains = 4,
    hierarchical = true
)

Analyzing Results

Let's examine the parallel fitting results:

# Print basic statistics
println(stats)

# Plot the results
using Plots
plot(fits)

# Save results
save_results(fits, "results/")

Advanced Usage

Cluster Computing

For large-scale analysis on a computing cluster:

# Set up cluster-specific parameters
setup_parallel(
    nworkers = 16,
    cluster = true,
    memory_limit = "4G"
)

# Run large-scale analysis
fits = fit_parallel(
    G = 2,
    R = 0,
    transitions = ([1,2], [2,1]),
    datatype = "rna",
    datapath = "data/",
    gene = "MYC",
    nchains = 16
)

Resource Management

Monitor and manage parallel resources:

# Check worker status
check_workers()

# Clean up resources
cleanup_parallel()

Best Practices

  1. Resource Allocation

    • Match worker count to available CPU cores
    • Consider memory requirements per worker
    • Monitor system load

  2. Error Handling

    • Implement proper error handling for parallel tasks
    • Log errors and warnings
    • Implement retry mechanisms

  3. Performance Optimization

    • Use appropriate chunk sizes
    • Balance load across workers
    • Monitor memory usage

Next Steps

  • Try different parallel configurations
  • Experiment with hierarchical models
  • Scale up to cluster computing

For more advanced examples, see: