A Parallel Processing Template for Divide & Conquer Problems The Next CEO of Stack OverflowGeneral multithreaded file processingA Simple Thread Pool in ScalaQueue for distributing tasks between threadsDynamic Multi-threading lock with while loopMultithreaded brute force prime generatorRelaying stdin data from one thread to anotherConcurrent FIFO in C++11Dynamically extensible threadpool implementation in CSimple versatile thread poolMulti-threaded tree search
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A Parallel Processing Template for Divide & Conquer Problems
The Next CEO of Stack OverflowGeneral multithreaded file processingA Simple Thread Pool in ScalaQueue for distributing tasks between threadsDynamic Multi-threading lock with while loopMultithreaded brute force prime generatorRelaying stdin data from one thread to anotherConcurrent FIFO in C++11Dynamically extensible threadpool implementation in CSimple versatile thread poolMulti-threaded tree search
$begingroup$
I’ve written a program for solving a problem using standard single-threaded code. However, it looks like it could be recast as a multi-threaded problem using divide & conquer. Since this is a first attempt at multi-thread programming (and debugging), I thought it might be advisable to separate the problem itself from the parallel management. The following is a multi-threaded divide & conquer skeleton, which hopefully the actual problem could fit into. I would appreciate a code check and any relevant advice about how to better code multi-threaded applications like this.
The skeleton uses the Lparallel library with queues for communicating among the threads. There is a main thread which initializes the queues & threads, submits the problem, and then blocks waiting for a solution. The problem is submitted to a pool of threads, whose number can range up to one less than the number of hardware processors available. Each idle thread blocks until a (sub)problem becomes available on the problems queue, which it may then then capture. If the problem size is above a minimal problem-size-cutoff, the thread splits off a subproblem (randomly to simulate the uncertainty in how much future processing the subproblem will require). The subproblem is pushed onto the problems queue so another idle thread can deal with it subsequently, and the thread then continues processing with what’s remaining of the problem after the split. When a thread finishes working on its problem, it reports that it is now idle via the idles queue. When any thread (namely, the last thread running) observes that all threads are idle, it reports problem complete via the done queue.
To run the following code, simply switch to the :test package and execute (main) after loading. The printout shows when any thread receives a problem (its size—eg, 34), and if it is split, what the random split is—eg, (13 21). If it is not split because it is too small, the printout just shows the simulated processing of what’s remaining.
Secondary questions: 1) The function main uses up a whole thread just waiting for problem completion. Is there a way to use this thread too? 2) Can the number of queues or queue accesses be reduced? 3) Would a so-called sequence diagram be the best way to verify correctness? Ie, is this worth learning, or is there a better way. (Ps: The program seems to run OK, but I’m not even sure how to simulate the duration of a thread.) Thanks for any time you can spare.
#-:lparallel (ql:quickload :lparallel)
(defpackage :test (:use :cl :lparallel :lparallel.queue :lparallel.kernel-util))
(in-package :test)
(defparameter *num-threads* 2) ;available problem-solving threads (excludes main thread-0)
(defparameter *problem* 100) ;estimated size of the main problem
(defparameter *problem-size-cutoff* 5) ;don't split small (x2) problems
(defun thread (problems idles done)
"Definition for a problem-solving thread."
(loop do
(let ((problem (pop-queue problems))) ;blocks until a problem is available
(pop-queue idles) ;one (this) thread is no longer idle
(loop do
(print problem)
(when (and (> problem (* 2 *problem-size-cutoff*)) ;don't split if small problem
(>= (queue-count idles) 1)) ;ie, some threads are idle
(let ((n (+ (random (- problem (* 2 *problem-size-cutoff*)))
*problem-size-cutoff*))) ;split problem
(sleep .01) ;time to run splitting algorithm
(push-queue n problems) ;n is random size of subproblem split
(setf problem (- problem n)) ;remaining problem size after split
(print (list n problem))))
(decf problem) (sleep .1) ;work more on current problem
(when (= problem 0) ;is this thread's task complete?
(push-queue t idles) ;this thread now idle
(if (= (queue-count idles) *num-threads*) ;all threads idle
(progn (push-queue t done) ;signal all done to main
(return-from thread))
(return))))))) ;return from inner loop to get new problem
(defun main ()
"The main consumer of parallel thread processing."
(with-temp-kernel (1)
(let ((c (make-channel))
(problems (make-queue)) ;holds current problems to be solved
(idles (make-queue)) ;holds a t for each current idle thread
(done (make-queue))) ;signals t when all processing is done
(loop for i from 1 to *num-threads* ;get all threads up & running
do (submit-task c #'thread problems idles done)
(push-queue t idles)) ;each t in idles means a thread is currently idle
(push-queue *problem* problems) ;send the main problem to the thread pool
(pop-queue done)))) ;block until thread pool reports done
multithreading common-lisp multiprocessing divide-and-conquer
$endgroup$
add a comment |
$begingroup$
I’ve written a program for solving a problem using standard single-threaded code. However, it looks like it could be recast as a multi-threaded problem using divide & conquer. Since this is a first attempt at multi-thread programming (and debugging), I thought it might be advisable to separate the problem itself from the parallel management. The following is a multi-threaded divide & conquer skeleton, which hopefully the actual problem could fit into. I would appreciate a code check and any relevant advice about how to better code multi-threaded applications like this.
The skeleton uses the Lparallel library with queues for communicating among the threads. There is a main thread which initializes the queues & threads, submits the problem, and then blocks waiting for a solution. The problem is submitted to a pool of threads, whose number can range up to one less than the number of hardware processors available. Each idle thread blocks until a (sub)problem becomes available on the problems queue, which it may then then capture. If the problem size is above a minimal problem-size-cutoff, the thread splits off a subproblem (randomly to simulate the uncertainty in how much future processing the subproblem will require). The subproblem is pushed onto the problems queue so another idle thread can deal with it subsequently, and the thread then continues processing with what’s remaining of the problem after the split. When a thread finishes working on its problem, it reports that it is now idle via the idles queue. When any thread (namely, the last thread running) observes that all threads are idle, it reports problem complete via the done queue.
To run the following code, simply switch to the :test package and execute (main) after loading. The printout shows when any thread receives a problem (its size—eg, 34), and if it is split, what the random split is—eg, (13 21). If it is not split because it is too small, the printout just shows the simulated processing of what’s remaining.
Secondary questions: 1) The function main uses up a whole thread just waiting for problem completion. Is there a way to use this thread too? 2) Can the number of queues or queue accesses be reduced? 3) Would a so-called sequence diagram be the best way to verify correctness? Ie, is this worth learning, or is there a better way. (Ps: The program seems to run OK, but I’m not even sure how to simulate the duration of a thread.) Thanks for any time you can spare.
#-:lparallel (ql:quickload :lparallel)
(defpackage :test (:use :cl :lparallel :lparallel.queue :lparallel.kernel-util))
(in-package :test)
(defparameter *num-threads* 2) ;available problem-solving threads (excludes main thread-0)
(defparameter *problem* 100) ;estimated size of the main problem
(defparameter *problem-size-cutoff* 5) ;don't split small (x2) problems
(defun thread (problems idles done)
"Definition for a problem-solving thread."
(loop do
(let ((problem (pop-queue problems))) ;blocks until a problem is available
(pop-queue idles) ;one (this) thread is no longer idle
(loop do
(print problem)
(when (and (> problem (* 2 *problem-size-cutoff*)) ;don't split if small problem
(>= (queue-count idles) 1)) ;ie, some threads are idle
(let ((n (+ (random (- problem (* 2 *problem-size-cutoff*)))
*problem-size-cutoff*))) ;split problem
(sleep .01) ;time to run splitting algorithm
(push-queue n problems) ;n is random size of subproblem split
(setf problem (- problem n)) ;remaining problem size after split
(print (list n problem))))
(decf problem) (sleep .1) ;work more on current problem
(when (= problem 0) ;is this thread's task complete?
(push-queue t idles) ;this thread now idle
(if (= (queue-count idles) *num-threads*) ;all threads idle
(progn (push-queue t done) ;signal all done to main
(return-from thread))
(return))))))) ;return from inner loop to get new problem
(defun main ()
"The main consumer of parallel thread processing."
(with-temp-kernel (1)
(let ((c (make-channel))
(problems (make-queue)) ;holds current problems to be solved
(idles (make-queue)) ;holds a t for each current idle thread
(done (make-queue))) ;signals t when all processing is done
(loop for i from 1 to *num-threads* ;get all threads up & running
do (submit-task c #'thread problems idles done)
(push-queue t idles)) ;each t in idles means a thread is currently idle
(push-queue *problem* problems) ;send the main problem to the thread pool
(pop-queue done)))) ;block until thread pool reports done
multithreading common-lisp multiprocessing divide-and-conquer
$endgroup$
add a comment |
$begingroup$
I’ve written a program for solving a problem using standard single-threaded code. However, it looks like it could be recast as a multi-threaded problem using divide & conquer. Since this is a first attempt at multi-thread programming (and debugging), I thought it might be advisable to separate the problem itself from the parallel management. The following is a multi-threaded divide & conquer skeleton, which hopefully the actual problem could fit into. I would appreciate a code check and any relevant advice about how to better code multi-threaded applications like this.
The skeleton uses the Lparallel library with queues for communicating among the threads. There is a main thread which initializes the queues & threads, submits the problem, and then blocks waiting for a solution. The problem is submitted to a pool of threads, whose number can range up to one less than the number of hardware processors available. Each idle thread blocks until a (sub)problem becomes available on the problems queue, which it may then then capture. If the problem size is above a minimal problem-size-cutoff, the thread splits off a subproblem (randomly to simulate the uncertainty in how much future processing the subproblem will require). The subproblem is pushed onto the problems queue so another idle thread can deal with it subsequently, and the thread then continues processing with what’s remaining of the problem after the split. When a thread finishes working on its problem, it reports that it is now idle via the idles queue. When any thread (namely, the last thread running) observes that all threads are idle, it reports problem complete via the done queue.
To run the following code, simply switch to the :test package and execute (main) after loading. The printout shows when any thread receives a problem (its size—eg, 34), and if it is split, what the random split is—eg, (13 21). If it is not split because it is too small, the printout just shows the simulated processing of what’s remaining.
Secondary questions: 1) The function main uses up a whole thread just waiting for problem completion. Is there a way to use this thread too? 2) Can the number of queues or queue accesses be reduced? 3) Would a so-called sequence diagram be the best way to verify correctness? Ie, is this worth learning, or is there a better way. (Ps: The program seems to run OK, but I’m not even sure how to simulate the duration of a thread.) Thanks for any time you can spare.
#-:lparallel (ql:quickload :lparallel)
(defpackage :test (:use :cl :lparallel :lparallel.queue :lparallel.kernel-util))
(in-package :test)
(defparameter *num-threads* 2) ;available problem-solving threads (excludes main thread-0)
(defparameter *problem* 100) ;estimated size of the main problem
(defparameter *problem-size-cutoff* 5) ;don't split small (x2) problems
(defun thread (problems idles done)
"Definition for a problem-solving thread."
(loop do
(let ((problem (pop-queue problems))) ;blocks until a problem is available
(pop-queue idles) ;one (this) thread is no longer idle
(loop do
(print problem)
(when (and (> problem (* 2 *problem-size-cutoff*)) ;don't split if small problem
(>= (queue-count idles) 1)) ;ie, some threads are idle
(let ((n (+ (random (- problem (* 2 *problem-size-cutoff*)))
*problem-size-cutoff*))) ;split problem
(sleep .01) ;time to run splitting algorithm
(push-queue n problems) ;n is random size of subproblem split
(setf problem (- problem n)) ;remaining problem size after split
(print (list n problem))))
(decf problem) (sleep .1) ;work more on current problem
(when (= problem 0) ;is this thread's task complete?
(push-queue t idles) ;this thread now idle
(if (= (queue-count idles) *num-threads*) ;all threads idle
(progn (push-queue t done) ;signal all done to main
(return-from thread))
(return))))))) ;return from inner loop to get new problem
(defun main ()
"The main consumer of parallel thread processing."
(with-temp-kernel (1)
(let ((c (make-channel))
(problems (make-queue)) ;holds current problems to be solved
(idles (make-queue)) ;holds a t for each current idle thread
(done (make-queue))) ;signals t when all processing is done
(loop for i from 1 to *num-threads* ;get all threads up & running
do (submit-task c #'thread problems idles done)
(push-queue t idles)) ;each t in idles means a thread is currently idle
(push-queue *problem* problems) ;send the main problem to the thread pool
(pop-queue done)))) ;block until thread pool reports done
multithreading common-lisp multiprocessing divide-and-conquer
$endgroup$
I’ve written a program for solving a problem using standard single-threaded code. However, it looks like it could be recast as a multi-threaded problem using divide & conquer. Since this is a first attempt at multi-thread programming (and debugging), I thought it might be advisable to separate the problem itself from the parallel management. The following is a multi-threaded divide & conquer skeleton, which hopefully the actual problem could fit into. I would appreciate a code check and any relevant advice about how to better code multi-threaded applications like this.
The skeleton uses the Lparallel library with queues for communicating among the threads. There is a main thread which initializes the queues & threads, submits the problem, and then blocks waiting for a solution. The problem is submitted to a pool of threads, whose number can range up to one less than the number of hardware processors available. Each idle thread blocks until a (sub)problem becomes available on the problems queue, which it may then then capture. If the problem size is above a minimal problem-size-cutoff, the thread splits off a subproblem (randomly to simulate the uncertainty in how much future processing the subproblem will require). The subproblem is pushed onto the problems queue so another idle thread can deal with it subsequently, and the thread then continues processing with what’s remaining of the problem after the split. When a thread finishes working on its problem, it reports that it is now idle via the idles queue. When any thread (namely, the last thread running) observes that all threads are idle, it reports problem complete via the done queue.
To run the following code, simply switch to the :test package and execute (main) after loading. The printout shows when any thread receives a problem (its size—eg, 34), and if it is split, what the random split is—eg, (13 21). If it is not split because it is too small, the printout just shows the simulated processing of what’s remaining.
Secondary questions: 1) The function main uses up a whole thread just waiting for problem completion. Is there a way to use this thread too? 2) Can the number of queues or queue accesses be reduced? 3) Would a so-called sequence diagram be the best way to verify correctness? Ie, is this worth learning, or is there a better way. (Ps: The program seems to run OK, but I’m not even sure how to simulate the duration of a thread.) Thanks for any time you can spare.
#-:lparallel (ql:quickload :lparallel)
(defpackage :test (:use :cl :lparallel :lparallel.queue :lparallel.kernel-util))
(in-package :test)
(defparameter *num-threads* 2) ;available problem-solving threads (excludes main thread-0)
(defparameter *problem* 100) ;estimated size of the main problem
(defparameter *problem-size-cutoff* 5) ;don't split small (x2) problems
(defun thread (problems idles done)
"Definition for a problem-solving thread."
(loop do
(let ((problem (pop-queue problems))) ;blocks until a problem is available
(pop-queue idles) ;one (this) thread is no longer idle
(loop do
(print problem)
(when (and (> problem (* 2 *problem-size-cutoff*)) ;don't split if small problem
(>= (queue-count idles) 1)) ;ie, some threads are idle
(let ((n (+ (random (- problem (* 2 *problem-size-cutoff*)))
*problem-size-cutoff*))) ;split problem
(sleep .01) ;time to run splitting algorithm
(push-queue n problems) ;n is random size of subproblem split
(setf problem (- problem n)) ;remaining problem size after split
(print (list n problem))))
(decf problem) (sleep .1) ;work more on current problem
(when (= problem 0) ;is this thread's task complete?
(push-queue t idles) ;this thread now idle
(if (= (queue-count idles) *num-threads*) ;all threads idle
(progn (push-queue t done) ;signal all done to main
(return-from thread))
(return))))))) ;return from inner loop to get new problem
(defun main ()
"The main consumer of parallel thread processing."
(with-temp-kernel (1)
(let ((c (make-channel))
(problems (make-queue)) ;holds current problems to be solved
(idles (make-queue)) ;holds a t for each current idle thread
(done (make-queue))) ;signals t when all processing is done
(loop for i from 1 to *num-threads* ;get all threads up & running
do (submit-task c #'thread problems idles done)
(push-queue t idles)) ;each t in idles means a thread is currently idle
(push-queue *problem* problems) ;send the main problem to the thread pool
(pop-queue done)))) ;block until thread pool reports done
multithreading common-lisp multiprocessing divide-and-conquer
multithreading common-lisp multiprocessing divide-and-conquer
asked 2 mins ago
davypoughdavypough
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