Passing Parameters to Parallel R Jobs

Introduction

Parallel processing is a crucial aspect of many computational tasks, especially in fields like data science and scientific computing. In R, using the multicore package can be an effective way to speed up computations by executing multiple jobs concurrently. However, when working with parallel R jobs, passing parameters from the main program to each job can be challenging. This post explores ways to overcome this limitation.

Background

The multicore package provides a simple and efficient way to parallelize R code using multiple CPU cores. When you create a multicore context, it executes a specified function with different input arguments across all available cores. However, accessing shared variables or external resources from within the parallelized function can be difficult.

Using External Storage for Shared Variables

One possible solution to this problem is to use an external storage mechanism, such as Redis or a database, to store parameters that can be accessed by all workers in the parallel job. This approach allows you to decouple the main program from the execution of the parallelized function, enabling easier maintenance and updating of parameters.

Overview of Redis and doredis

Redis is an open-source, in-memory data store that provides fast and reliable storage for data. It supports various data types, including strings, lists, sets, and more. In R, packages like Rdired and doredis provide interfaces to interact with Redis from the R environment.

Setting up a Redis Server

To use Redis as a shared parameter store, you need to set up a Redis server on your machine or in a cloud-based environment. This can be done using various methods, such as:

• Installing Redis on your local machine and accessing it through the command line.
• Using a cloud-based Redis service, like Redis Labs’ managed Redis offering.

Storing Parameters in Redis

Once you have set up a Redis server, you can store parameters in a Redis database. This can be done using R’s Rdired package or other tools that interact with Redis from the command line.

For example, to store a parameter named param1 with value "hello" in Redis:

# Create a connection to the Redis server
conn <- redis_connect("localhost", port = 6379)

# Store the parameter in Redis
redis_set(conn, "param1", "hello")

Retrieving Parameters from Redis in R

To retrieve parameters stored in Redis in your parallel job, you can use R’s doredis package to interact with Redis from within the function.

For example:

# Load the doredis package
library(doredis)

# Establish a connection to the Redis server
conn <- redis_connect("localhost", port = 6379)

# Retrieve a parameter from Redis
param1_value <- redis_get(conn, "param1")

print(param1_value) # Output: hello

Passing Parameters to Parallel Jobs using doredis

With Redis as the shared parameter store and doredis for interacting with it in R, you can now pass parameters to parallel jobs while decoupling the main program from the execution.

Here’s a simple example of how this could be implemented:

# Define a function that takes arguments and runs in parallel
multicore_function <- function(arg1, arg2) {
  # Load the doredis package and establish connection to Redis
  library(doredis)
  conn <- redis_connect("localhost", port = 6379)

  # Retrieve parameters from Redis
  param1_value <- redis_get(conn, "param1")
  param2_value <- redis_get(conn, "param2")

  # Run the function with retrieved values
  result <- run_function_in_parallel(
    func = function(x, y) x + y,
    args = list(arg1 = as.integer(param1_value), arg2 = as.integer(param2_value)),
    cores = 4,
    timeLimit = 60
  )

  # Return the result of the parallel execution
  return(result)
}

# Define a main function that initializes Redis, stores parameters, and runs the parallel job
main_function <- function() {
  # Store parameters in Redis for the parallel job
  redis_set(conn, "param1", "hello")
  redis_set(conn, "param2", 42)

  # Run the parallel job using multicore_function
  result <- multicore_function()
}

In this example, multicore_function takes two arguments, retrieves them from Redis using doredis, and runs a simple function with these values in parallel. The main program initializes the Redis connection, stores parameters, and then calls main_function to execute the parallel job.

Conclusion

Passing parameters to parallel R jobs while decoupling the main program can be achieved by leveraging an external storage mechanism like Redis or a database. With packages like Rdired and doredis, interacting with these shared storage mechanisms becomes straightforward, enabling efficient management of parameters across multiple workers.

By combining Redis as a parameter store with doredis for R interaction, you can efficiently manage shared data across parallel jobs in your R programs, allowing for greater flexibility and reusability. This solution simplifies the process of maintaining and updating parameters during execution while providing a reliable mechanism for working with distributed computing tasks.

In the next sections, we will explore more advanced aspects of parallel processing, such as handling dependencies between tasks, managing error cases, and optimizing performance.

Last modified on 2023-12-22