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PTools

Collection of utilities for parallel processing in Julia

Readme

PTools

A collection of utilities for parallel computation in Julia

Currently the following are available.

  • WorkerSet - The ability to logically pool a set of workers for specific tasks.

  • pfork - Parallelism using the UNIX fork system call.

  • ServerTasks - These are long running tasks that simply processes incoming requests in a loop. Useful in situations where state needs to be maintained across function calls. State can be maintained and retrieved using the task_local_storage methods.

  • SharedMemory - Useful in the event of parallel procesing on a single large multi-core machine. Avoids the overhead associated with sending/recieving large data sets.

  • QueuedTasks - Schedule tasks to be executed by remote worker processes. Set/change priorities on the task to control order of execution.

Platforms

  • Tested on Ubuntu 13.04
  • Should work on OSX
  • SharedMemory will not work on Windows. ServerTasks should.
  • pfork is a UNIX only implementation

WorkerSet

  • A WorkerSet is just an array of process ids

  • A WorkerSet is created using WorkerSet(w::Array{Integer}, mode) where mode is either of WS_MODE_RR or WS_MODE_FF where

  • WS_MODE_RR enables the workers to be used in a round-robin fashion

  • WS_MODE_FF tracks which of the workers are busy and sends the request only to a free one. It queues the requests if all the workers in the set are busy.

  • The remotecall* functions have been extended to support WorkerSet

    remotecall(ws::WorkerSet, f, args...) 
    remotecall_fetch(ws::WorkerSet, f, args...) 
    remotecall_wait(ws::WorkerSet, f, args...)
    
  • The behaviour is the same, except that the functions are executed only on the processes belonging to the WorkerSet and follow the model as specified by mode.

pfork

  • It uses the UNIX fork() system call to execute a function in parallel.

  • pfork(nforks::Integer, f::Function, args...) forks nforks times and executes f in each child.

  • The first parameter to f is the forked child index.

  • pfork returns an array of size nforks, where the nth element is the value returned by the nth forked child.

  • Passing huge amounts of data to the function in the child process is a non-issue since it is a fork.

  • In the event the parent process has a shared memory segment, the children have full visibility into the same and can modify the segment in place. They don't have to bother with returning huge amounts of data either.

  • Currently can only be executed when nprocs() == 1, i.e., this model is incompatible with the worker model.

  • Unpredictable behavior if the function to be executed in the forked children calls yield() AND other I/O tasks are concurrently active. Can be used only where f is fully compute bound.

Usage of Server Tasks

Typical usage pattern will be

  • start_stasks - Start Server Tasks, optionally with shared memory mappings.

  • Execute a series of functions in parallel on these tasks using multiple invocations of pmap_stasks

  • SomeFunction

  • SomeOtherFunction

  • SomeOtherFunction . . .

  • stop_stasks - Stop all Server Tasks and free shared memory if required.

The user specified functions in pmap_stasks can store and retrieve state information using the task_local_storage functions.

Example for shared memory and server tasks

The best way to understand what is available is by example:

  • specify shared memory configuration. html using PTools shmcfg = [ShmCfg(:svar1, Int32, 64*1024), ShmCfg(:svar2, Uint8, (100,100))]
  • the above line requests for a 64K Int32 array bound to svar1, and a 100x100 Uint8 array bound to svar2

  • Start tasks.

    h = start_stasks(shmcfg)
    ntasks = count_stasks(h)
    
  • The tasks are started and symbols pointing to shared memory segments are added as task local storage. A handle is returned.

  • The shared memory segments are also mapped in the current tasks local storage.

  • NOTE: If nprocs() > 1, then only the Worker Julia processes are used to start the Server Tasks, i.e., if nprocs() == 5, then ntasks above would be 4.

  • Prepare arguments for our pmap call

    offset_list = [i for i in 1:ntasks]
    ntasks_list = [ntasks for i in 1:ntasks]
    
  • Execute our function in parallel.

    resp = pmap_stasks(h, (offset, ntasks) -> begin
                            # get local refernces to shared memory mapped arrays
                            svar1 = task_local_storage(:svar1)
                            svar2 = task_local_storage(:svar2)
                            
                            mypid = myid()
                            for x in offset:ntasks:64*1024
                                svar1[x] = mypid
                            end
                            
                            true
                            
                        end,
                        
                        offset_list, ntasks_list)
    
  • Access shared memory segments and view changes

    svar1 = task_local_storage(:svar1)
    println(svar1)
    

    svar1 will the values as updated by the Server Tasks.

  • Finally stop the tasks

    stop_stasks(h, shmcfg)
    

This causes all tasks to be stopped and the shared memory unmapped.

Exported functions for ServerTasks

start_stasks(shmcfg=false, shmpfx=false) where shmcfg is an optional parameter. It is either a ShmCfg(name::Symbol, t::Type, d::dims) or Array{ShmCfg, 1}. Returns a handle to the set of ServerTasks.

pmap_stasks(h::STasks, f::Function, lsts...) is similar to pmap, except that the first parameter is the handle returned by start_tasks. NOTE: that the length of lsts and number of ServerTasks must be identical.

stop_stasks(h::STasks, shmcfg=false, shmpfx=false) stops all tasks and also frees the shared memory

count_stasks(h::STasks) returns the number of ServerTasks - can be used to partition the compute job.

NOTE: shmpfx should be set to a distinct string in case you are sharing the server with other users or have multiple self concurrent jobs active.

Exported functions for Shared Memory support

pmap_shm_create(shmcfg, shmpfx="") - creates and maps the shared memory segments to global symbols in each Julia process. shmcfg can be ShmCfg(name::Symbol, t::Type, d::dims) or Array{ShmCfg, 1}

unlink_shm(shmcfg, shmpfx="") - frees the shared memory

NOTE: For a single run, it is important that shmpfx is passed with same value to all the methods.

Under Linux, you can view the shared memory mappings under /dev/shm In the event of abnormal program termination, where unlink_shm has not been called it is important to manually delete all segments allocated by the program. The name of the segments will be of the form /dev/julia__

First Commit

05/03/2013

Last Touched

almost 3 years ago

Commits

29 commits