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doRedis: A simple parallel backend for foreach using Redis. NOTE Set the following parameter in your redis.conf file before using doRedis: timeout 0 DESCRIPTION Steve Weston's foreach package is a remarkable parametric evaluation device for the R language. Similarly to lapply-like functions, foreach maps functions to data and aggregates results. Even better, foreach lets you do this in parallel across multiple CPU cores and computers. And even better yet, foreach abstracts the parallel computing details away into modular back-end code. Code written using foreach works sequentially in the absence of a parallel back-end, and works uniformly across a growing variety of back ends. Think of foreach as the lingua Franca of parallel computing for R. Redis is a powerful, fast networked database with many innovative features, among them a blocking stack-like data structure (Redis "lists"). This feature makes Redis useful as a lightweight backend for parallel computing, similar to REvolution Computing's NetWorkSpaces. The rredis package provides a native R interface to Redis. The doRedis package defines a simple parallel backend for foreach that uses Redis. Here is a quick example procedure for experimenting with doRedis: 1. Install Redis on your *nix machine (not available for Windows). 2. Install foreach, rredis and doRedis packages. 3. Start the redis server running (see the redis documentation). We assume that the server is running on the host "localhost" and port 6379 (the default Redis port). We assume in the examples below that the worker R processes and the master are running on the same machine. 4. Open one or more R sessions that will act as back-end worker processes. Run the following in each session: require('doRedis') redisWorker('jobs') (The R session will display status messages but otherwise block for work.) Note: You can add more workers to a work queue at any time. Also note that each back-end worker may advertise for work on multiple queues simultaneously (see the documentation and examples). 5. Open another R session that will act as the master process. Run the following example (a simple sampling approximation of pi): require('doRedis') registerDoRedis('jobs') foreach(j=1:10,.combine=sum,.multicombine=TRUE) %dopar% 4*sum((runif(1000000)^2 + runif(1000000)^2)<1)/10000000 removeQueue('jobs') DISCUSSION The "jobs" parameter of the redisWorker and registerDoRedis function specifies a Redis "list" that will be used to transfer data between the master and worker processes. Think of this name as a reference to a job queue. You are free to configure multiple queues. The doRedis parallel backend supports dynamic pools of back-end workers. New workers may be added to work queues at any time and can be immediately used by in-flight foreach computations. The doRedis backend accepts a parameter called "chunkSize" that sets the number of function evaluations to be doled out per job. The default value is one. Increasing chunkSize can improve performance greatly for quick-running function evaluations. Here is an example that sets the chunkSize to 100: foreach(j=1:5, .options.redis=list(chunkSize=100)) %dopar% ... Setting chunkSize too large will adversely impact load-balancing across the workers. The redisWorker function is used to manually invoke worker processes that advertise for jobs on one or more queues. The function also has parameters for a Redis host and port number. For example, if the Redis server is running on a host called "Cazart" with the default Redis port 6379: redisWorker('jobs', host='Cazart', port=6379) The registerDoRedis function also contains host and port parameters. Neither the worker nor master R session needs to be running on the same machine as the Redis server. The startLocalWorkers function invokes one or more background R worker processes on the local machine (using the redisWorker function). It's a convenient way to invoke several workers at once. Workers self-terminate when their work queues have been deleted with the removeQueue function.