Example of multi-threaded application: array of threads

From Lazarus wiki
Revision as of 17:13, 16 June 2013 by Crorden (talk | contribs) (Results.)

Here I want to show an example how to create a lot of threads and wait while they will not finish their jobs (I don't need any synchronisation). I'm writing this tutorial because it was not obvious for me to write such a program after reading Multithreaded Application Tutorial. I was writing my application for OSX, but the resulting code should work on any system.

Let's assume that we have the following loop:

var results: integer;
    File: text;


for i:=1 to n do begin


This loop computes the "power" function in serial, n times one after another. Lets use threads to achieve the same task in parallel.

Managing memory.

Since multiple threads will be working simultaneously, you need to ensure that they do not have memory contention issues. We will have contention issues if multiple threads are writing to the same locations of memory. Some algorithms do not lend themselves to multi-threading, because each computation depends earlier results. On the other hand, multi-threading works very efficiently on problems where the computations can be performed independently and in parallel. In this example we will solve a problem that is completely independent and therefore easy to attack with multiple threads. Advanced algorithms will have to use memory locking features to avoid contention.

In our example, we will have each thread write to distinct memory locations. Specifically, we will create an array 1..n and compute the value power(i,0.5) where i is in the range 1..n. Each thread will be given an independent portion of the range to compute. Consider n=1000. If we use one thread, it will be tasked with the whole range 1..1000, whereas if we use two threads one will tackle 1..500 and the other 501..1000. This way threads will be working to fill different portions of our memory array.

1. Add threads class.

I use a separate unit for defining the behavior of the threads. Note that I am setting the "FreeOnTerminate" to false - so my program will need to dispose of each thread when it is done. This makes it easier to juggle multiple threads (if you set FreeOnTerminate to true and launch multiple very fast jobs it is possible that the thread will be released before your program checks whether the thread is completed - and checking a non-existent thread can cause an exception). By setting FreeOnTerminate to false I can ensure that each thread completed successfully.

unit mythreads;
{$mode objfpc}{$H+}
  Classes, SysUtils, Math;
  TData = array of double;
  PData = ^TData;
    TMyThread = class(TThread)
      tPtr: PData;
      tstart,tfinish: integer;
      procedure Execute; override;
      property Terminated;
      Constructor Create(lstart, lfinish: integer; var lPtr: PData);


  constructor TMyThread.Create(lstart, lfinish: integer; var lPtr: PData);
    FreeOnTerminate := False;
    tstart := lstart;
    tfinish := lfinish;
    tPtr := lPtr;
    inherited Create(false);
  procedure TMyThread.Execute;
    i: integer;
    for i := tstart to tfinish do
        tPtr^[i] := power(i,0.5);


2. Write the main program.

You need to add 'cthreads' to the main unit, not to unit with threads!

Note that there are two ways to determine whether all the threads have completed. You can use the in-built "waitFor" function - this works very nicely but on my OSX computer I noted that it refreshes only every 100ms. This is perfect for real world programs (we only use threading for computationally slow problems) and reduces thread overhead. However, for quick example benchmarks it can hide the benefits of threading (as operations require a minimum of 100ms regardless of the number of threads). Therefore, in this example I detect the threads terminated status every 2ms. This provides more accurate benchmark timing.

Remember to free each thread when you are done with it. Since we set "FreeOnTerminate := False" the program needs to do this explicitly.

Tips: in my Lazarus IDE I was not able to debug multi-threading applications if I don't use 'pthreads'. I have read that if you use 'cmem', the program works faster, but I strongly recommend you to check it for any particular case (my program hangs when I use 'cmem').

uses //    cmem,pthreads,
  cthreads, Classes, SysUtils, CustApp, MyThreads;

procedure DoThreading (nThreadsIn, nValues: integer);
 threadArray: array  of TMyThread;
 dataArray: TData;
 lData : PData;
 nThreads, i,lStart,lFinish: integer;
 StartMS: double;
     if (nThreadsIn < 1) or (nValues < 1) then exit;
     nThreads := nThreadsIn;
     if  nThreads > nValues then nThreads := nValues;
     setlength(threadArray,nThreads+1);//+1 since indexed 0..n-1
     setlength(dataArray, nValues+1);//+1 since indexed 0..n-1
     lData := @dataArray;
     lStart := 1;
     for i:=1 to nThreads do begin
         if i < nThreads then
            lFinish:=i*(nValues div nThreads)
             lFinish:=  nValues;
         threadArray[i]:= TMyThread.Create(lStart, lFinish, lData);
         lStart := lFinish+1;
     for i:=1 to nThreads do if not ThreadArray[i].Terminated then Sleep(2);
     //for i:=1 to nThreads do threadArray[i].waitFor;  //appears to sleep for 100ms on OSX
     for i:=1 to nThreads do threadArray[i].Free;
     Writeln(inttostr(nThreads)+' Threads processed '+inttostr(nValues)+' values in '+floattostr(timestamptomsecs(datetimetotimestamp(now))-StartMS)+'ms, with '+inttostr(nValues)+'^0.5 = '+floattostr(dataArray[nValues]));



The results show there is a delay in creating threads, but that for large tasks multiple parallel threads outperform serial processing.

  • Computer reports 4 cores: probably optimal number of threads
  • Serially processed 10 values in 0ms, with 10^0.5 = 3.16227766016838
  • 1 Threads processed 10 values in 3ms, with 10^0.5 = 3.16227766016838
  • 2 Threads processed 10 values in 5ms, with 10^0.5 = 3.16227766016838
  • 4 Threads processed 10 values in 9ms, with 10^0.5 = 3.16227766016838
  • 8 Threads processed 10 values in 19ms, with 10^0.5 = 3.16227766016838
  • Serially processed 100000000 values in 10214ms, with 100000000^0.5 = 10000
  • 1 Threads processed 100000000 values in 10320ms, with 100000000^0.5 = 10000
  • 2 Threads processed 100000000 values in 5894ms, with 100000000^0.5 = 10000
  • 4 Threads processed 100000000 values in 3801ms, with 100000000^0.5 = 10000
  • 8 Threads processed 100000000 values in 3733ms, with 100000000^0.5 = 10000