POSIX Threads Tutorial

by Mark Hays


Overview
This is a brief tutorial on how write parallel applications using POSIX threads. Basically, this document is a short version of DEC's 300+ page manual; it is intended to provide sufficient information to enable the development of reasonably sophisticated parallel scientific codes in C.

Table of Contents


Latest News on this Document

1 May 1998:   The code and documentation contained herein now conforms to POSIX 1003.1c-1995. The previous version of this document is based on POSIX 1003.1c draft 4 which (AFAIK) only works on DEC OSF/1 3.2. The old code can still be downloaded here if you need it; however, the draft 4 code will no longer be maintained.

The revised code described here has only been tested on Linux systems (LinuxThreads and glibc native threads). If you encounter problems or have corrections, please send an email to system.


What are threads?
A thread is a sequence of instructions to be executed within a program. Normal UNIX processes consist of a single thread of execution that starts in main(). In other words, each line of your code is executed in turn, exactly one line at a time. Before threads, the normal way to achieve multiple instruction sequences (ie, doing several things at once, in parallel) was to use the fork() and exec() system calls to create several processes -- each being a single thread of execution.

In the UNIX operating system, every process has the following:

  1. a virtual address space (ie, stack, data, and code segments)
  2. system resources (eg, open files)
  3. a thread of execution
These resources are private to each process; for example, processes cannot peek into one another's address space, open and close files for one another, etc. To achieve interprocesses cooperation, external channels (to the process) of communication are needed. UNIX systems provide several mechanisms for accomplishing interprocess communication (IPC), including (relevant manpages are listed in square brackets):

Earlier this semester, Anu gave a talk on PVM. PVM is based on IPC mechanisms; e.g., it uses sockets to communicate between processes on different machines across a network. On multiprocessor machines, PVM can be configured to use the System V IPCs to achieve communication between processes on different CPUs. One nice side effect of using these standard mechanisms is that PVM code is fairly portable between UNIX platforms.

There are advantages and disadvantages to this method of achieving parallelism. Since you have to arrange for the communication yourself, it is completely clear what data is being shared. On the negative side, IPC mechanisms are implemented as system calls (they require kernel intervention to complete) and involve a fair amount of overhead. It can happen that the communication mechanism itself becomes a processing bottleneck.

In a threaded environment, this model of parallelism is turned on its head. Instead of there being several singly threaded processes, we have one process consisting of multiple threads. In other words, each user process can have several threads of execution -- different parts of your code can be executing simultaneously. The collection of threads form a single process: each thread in the process sees the same virtual address space, files, etc.

This last fact has interesting implications. For example, when one thread opens a file, it is immediately possible for all other threads to access the file. Similarly, if one thread alters the value of a global variable, all other threads instantly see the new value. In a multithreaded process, the cost of communication is negligible. The downside of this feature is that a threaded process cannot exist across machines (because the underlying resources are bound to the local host).

When writing multithreaded applications, you must take the initiative to protect shared resources. For example, consider the code

                i = i + 1
This statement is well defined for a single thread of execution: get i, add 1, and put the result back. For multiple threads of execution, the statement is ill defined. If 2 or more CPUs attempt to perform this operation simultaneously, the result is indeterminate -- the variable i will get incremented anywhere from one to N times, where N is the number of threads that execute this statement. In a multithreaded environment, it is necessary for the programmer to ensure that access to shared resources is managed in a sane fashion.

Why should you use threads? Well, maybe you shouldn't. Developing a threaded program can take longer than a nonthreaded program. If you don't need the benefits of threads or don't intend to run your code many times, it's probably not worth the trouble. On the other hand, threading your code has several payoffs:

Speed
Communication between threads is very fast. If your algorithm is bound by communication speed, threads will put a smile back on your face.
I/O throughput
In a traditional UNIX process, a blocked I/O request stops the whole process dead in its tracks. Things are much nicer in POSIX threads: a blocked I/O request only puts the calling thread to sleep -- the other threads go about their business. We'll see one way to accomplish this later in this document.
Portability
POSIX threads are fairly portable. They can be emulated on uniprocessor systems at a slight (or sometimes no) penalty in speed. On shared memory multiprocessor machines, there is often kernel support for threads; this means that your application can take full advantage of all available CPUs and achieve (near) maximum performance.
It is important to point out that threads libraries are predicated on shared memory. Threads won't help you on distributed memory machines (like the IBM SP2) -- in such cases, you're better off looking for a specially tuned version of PVM or MPI.

POSIX threads
The pthreads library is a set of C calls that provides a mechanism for writing multithreaded code. There are three major classes of calls in this library:

  1. calls to create and destroy threads.
  2. calls to synchronize threads and lock program resources.
  3. calls to manage thread scheduling.
Below, we'll discuss the first two classes in detail.

The third class will not be considered in this document. Thread scheduling can not only considerably complicate your algorithm, but it can also get you into trouble when migrating your code from uniprocessor to multiprocessor machines. From what I have seen so far, fiddling with thread priorities isn't worth the trouble in many scientific applications, anyway.

Each thread sees the same global data and files; however, each thread has its own stack and registers (so that variables in the automatic and register storage classes are private).

+-----------------------------------------------------------------------+
| Process                                                               |
|                                                                       | 
|   +-------+     +-------------+  +-------------+  +-------------+     |
|   | Files |     | Thread      |  | Thread      |  | Thread      |     |
|   +-------+     |+-----------+|  |+-----------+|  |+-----------+|     |
|                 || Registers ||  || Registers ||  || Registers ||     |
|                 |+-----------+|  |+-----------+|  |+-----------+|     |
|                 |             |  |             |  |             |     |
| ....................................................................  |
| .               |             |  |             |  |             |  .  |
| . Memory        |             |  |             |  |             |  .  |
| .               | +---------+ |  | +---------+ |  | +---------+ |  .  |
| . +--------+    | |  Stack  | |  | |  Stack  | |  | |  Stack  | |  .  |
| . |  Heap  |    | |         | |  | |         | |  | |         | |  .  |
| . +--------+    | |         | |  | |         | |  | |         | |  .  |
| .               | |         | |  | |         | |  | |         | |  .  |
| . +--------+    | |         | |  | |         | |  | |         | |  .  |
| . | Static |    | |         | |  | |         | |  | |         | |  .  |
| . +--------+    | |         | |  | |         | |  | |         | |  .  |
| .               | |         | |  | |         | |  | |         | |  .  |
| . +--------+    | |         | |  | |         | |  | |         | |  .  |
| . |  Code  |    | +---------+ |  | +---------+ |  | +---------+ |  .  |
| . +--------+    +-------------+  +-------------+  +-------------+  .  |
| .                                                                  .  |
| ....................................................................  |
|                                                                       |
+-----------------------------------------------------------------------+
                        (From the DECthreads manual)

Thread creation and destruction
The first example we'll consider is the usual "Hello, world" program. The basic flow of the program is as follows:

         Time             Master Thread
           |                    |
           |                    |        create workers with pthread_create()
           |                    |
           |                  // \\      workers start up
          \ /                / | | \
           |                 | | | |
                             | | | |     workers do their jobs
                             | | | |
                             \ \ / /
                              \\ //      workers terminate
                                |
                                |        join workers with pthread_join()
                                |
                          Master Thread
Starting from an initial thread in main(), we'll create several worker threads. Each of these will print its message while the original thread waits for them to complete. As each one exits, the master thread will join it and destroy the system resources it uses. Finally, the original thread will call exit() and the whole process will terminate.

Here's the hello world program (click here for source):

 1: /******************************************************************
 2:  * simple.c -- multithreaded "hello world"
 3:  *
 4:  * Author: Mark Hays <hays@math.arizona.edu>
 5:  */
 6: 
 7: /* Linux with glibc:
 8:  *   _REENTRANT to grab thread-safe libraries
 9:  *   _POSIX_SOURCE to get POSIX semantics
10:  */
11: #ifdef __linux__
12: #  define _REENTRANT
13: #  define _POSIX_SOURCE
14: #endif
15: 
16: /* Hack for LinuxThreads */
17: #ifdef __linux__
18: #  define _P __P
19: #endif
20: 
21: #include <pthread.h>
22: 
23: #include <string.h>   /* for strerror() */
24: 
25: #include <stdio.h>
26: 
27: #define NTHREADS 4
28: 
29: #define errexit(code,str)                          \
30:   fprintf(stderr,"%s: %s\n",(str),strerror(code)); \
31:   exit(1);
32: 
33: /******** this is the thread code */
34: void *hola(void * arg)
35: {
36:   int myid=*(int *) arg;
37: 
38:   printf("Hello, world, I'm %d\n",myid);
39:   return arg;
40: }
41: 
42: /******** this is the main thread's code */
43: int main(int argc,char *argv[])
44: {
45:   int worker;
46:   pthread_t threads[NTHREADS];                /* holds thread info */
47:   int ids[NTHREADS];                          /* holds thread args */
48:   int errcode;                                /* holds pthread error code */
49:   int *status;                                /* holds return code */
50: 
51:   /* create the threads */
52:   for (worker=0; worker<NTHREADS; worker++) {
53:     ids[worker]=worker;
54:     if (errcode=pthread_create(&threads[worker],/* thread struct             */
55:                    NULL,                    /* default thread attributes */
56:                    hola,                    /* start routine             */
57:                    &ids[worker])) {         /* arg to routine            */
58:       errexit(errcode,"pthread_create");
59:     }
60:   }
61:   /* reap the threads as they exit */
62:   for (worker=0; worker<NTHREADS; worker++) {
63:     /* wait for thread to terminate */
64:     if (errcode=pthread_join(threads[worker],(void *) &status)) { 
65:       errexit(errcode,"pthread_join");
66:     }
67:     /* check thread's exit status and release its resources */
68:     if (*status != worker) {
69:       fprintf(stderr,"thread %d terminated abnormally\n",worker);
70:       exit(1);
71:     }
72:   }
73:   return(0);
74: }
75: 
76: /* EOF simple.c */

On Linux systems, you compile this program with the command:

        prompt$ cc -o simple simple.c -lpthread

Here's a blow-by-blow account of what is going on. The program starts in main() on line 43. It will create NTHREADS threads (NTHREADS is defined on line 27) on line 54, each of which starts executing the function hola() on line 34. The main thread waits for each of the worker threads to terminate by calling pthread_join() for each thread on line 64. Meanwhile, each of the worker threads prints a message and its thread ID and then exits. Once the main thread has reaped all of the workers, the main program exits.

Now for the details. Threads are created with the pthread_create() call. The first argument is a pointer to a pthread_t -- an opaque data structure (i.e., it is a blick box) that describes the thread. Each thread must have a unique pthread_t to describe it. In this case, we store the descriptor for each thread in the array threads[] defined on line 46. The second argument to pthread_create() is of type pthread_attr_t and specifies the attributes of the thread being created (scheduling priority, etc). Normally you can use the value NULL for this argument. The third argument is the name of the function in which the thread should start -- this function is expected to return a void *. The last argument is passed untouched to the start routine. In this case, we just pass the thread's number (loop count) to the start routine. In more complicated situations, you can pass a pointer to a block of thread-specific data. pthread_create() returns 0 on success and an error number on failure. If it fails, we print a message with perror() and exit (line 58). As a general rule, you should always check for system and library call errors. When programming with pthreads, it is suicidal not to check for error conditions. Henry Spencer's Sixth Commandment for C programmers sums it up nicely:

If a function be advertised to return an error code in the event of difficulties, thou shalt check for that code, yea, even though the checks triple the size of thy code and produce aches in thy typing fingers, for if thou thinkest "it cannot happen to me," the gods shall surely punish thee for thy arrogance.

The main thread next waits for each of the workers to finish in lines 62-72. It calls pthread_join() to wait for the thread to terminate. The thread we're waiting on is specified in the first argument and its exit code is placed into the second argument (declared on line 49 as type int *). We check the exit code and issue a message (and exit) if it's not what we expect.

The code that each worker thread executes occupies lines 34-40. First, we cast the argument to the start routine to an int (since we passed a pointer to an int to pthread_create()). The thread then prints its message and returns its thread ID. At this point, the thread terminates and is reaped when the master thread calls pthread_join(). You can also have a thread terminate itself with the pthread_exit() call.

The call pthread_join() causes the calling thread to go to sleep until the specified thread terminates. In the examples given in this document, the master thread cannot proceed until all the workers complete their tasks. Once a thread has been joined, its system resources are reclaimed. If you need to terminate a running thread, use pthread_cancel() and pthread_testcancel().

Once you join a thread, you cannot reassign it a new task. The reason is that once a thread has been joined, it has (by definition) terminated. If you need more threads to perform another task, you have to call pthread_create() again.

Aside from returning a thread's exit status, the major function of pthread_join() is to reclaim the system resources used by the terminated thread. If you forget to call pthread_join() in a program that creates many threads, you will run into problems sooner or later (like what happens when you call malloc() many times and never call free()).

Sometimes you do not care about the thread's exit status; in this case, having to call pthread_join() becomes an annoyance. The pthread_detach() function is helpful in such cases. You call pthread_detach() with a pthread_t argument. This argument must be the descriptor for a running thread. After the call completes, it is no longer possible for the rest of the program to reference that thread in any way. The selected thread simply runs to completion, at which point its systems resources are automatically reclaimed. This might be a useful way to implement runtime graphics: you have one of these "free" threads send your data to some graphics program.

The source for simple.c looks a little nastier than it needs to. For example, much of the code is related to error checking of the pthread_xxx calls. To help eliminate some of this clutter, I have written a small library of macros and C code to hide some of these details. The macros and library calls all have the prefix "pt_" to distinguish them from the pthreads calls they wrap. They all have the form:

                pt_xxx(object *obj,..., char *msg)
The "object *" is always a pointer to a pthreads object (or one of the derived types discussed later). The "char *msg" is a message that is printed in case of an error. When errors occur, your code will exit. Since most pthreads routines only return the EINVAL error, this seems acceptable -- usually it means that you've attempted to operate on an invalid pointer. Let's use these macros to clean things up a bit -- the new code is called simple2.c (click here for simple2.c; click here for pt.h ):
 1: /******************************************************************
 2:  * simple2.c -- multithreaded "hello world" using pt_xxx macros
 3:  *
 4:  * Author: Mark Hays <hays@math.arizona.edu>
 5:  */
 6: 
 7: #include "pt.h"
 8: 
 9: #include <stdio.h>
10: 
11: #define NTHREADS 4
12: 
13: /******** this is the thread code */
14: pt_addr_t hola(pt_addr_t arg)
15: {
16:   printf("Hello, world, I'm %d\n",*(int *) arg);
17:   return arg;
18: }
19: 
20: /******** this is the main thread's code */
21: int main(int argc,char *argv[])
22: {
23:   int worker;
24:   pthread_t threads[NTHREADS];                /* holds thread info */
25:   int ids[NTHREADS];                          /* holds thread id   */
26:   int *status;                                /* holds return code */
27: 
28:   /* create the threads */
29:   for (worker=0; worker<NTHREADS; worker++) {
30:     ids[worker]=worker;
31:     pt_create(&threads[worker],               /* thread struct     */
32:               hola,                           /* start routine     */
33:               &ids[worker],                   /* arg to routine    */
34:               "pt_create");                   /* error message     */
35:   }
36:   /* reap the threads as they exit */
37:   for (worker=0; worker<NTHREADS; worker++) {
38: 
39:     /* wait for thread to terminate */
40:     pt_wait(&threads[worker],(pt_addr_t) &status,"pt_wait");
41: 
42:     /* check thread's exit status */
43:     if (*status != worker) {
44:       fprintf(stderr,"thread %d terminated abnormally\n",worker);
45:       exit(1);
46:     }
47:   }
48:   return(0);
49: }
50: 
51: /* EOF simple2.c */

What have we done? First, we include pt.h (click here to view) instead of pthread.h on line 7. It is very important to include this file before any other header files. Next, we have changed the declaration of hola() to accept and return an pt_addr_t instead of a void *. The major changes are in main(), though. Instead of calling pthread_create(), we use the macro pt_create(). This is simply a wrapper that handles error checking and typecasting of arguments. Finally, the call to pthread_join() has been replaced with the pt_wait() macro (which does error checking on the underlying pthread_join() call). The following summarizes the usage of these macros:

                void pt_create(pthread_t *thread,
                               pt_startroutine_t start,
                               pt_addr_t arg,
                               char *msg)

                void pt_wait(pthread_t *thread,
                             pt_addr_t *status,
                             char *msg)
In pt_wait(), you can pass in NULL for the status argument if you don't care what the thread's return status is.

Not to beat a dead horse, but can we do any better than this? Actually, yes. Having several threads execute the same function in parallel is a very common scenario. The mini-library provides a C function called pt_fork() that combines most of lines 29-47 in a single call. The usage of pt_fork() is:

                void pt_fork(int nthreads,
                             pt_startroutine_t start,
                             pt_addr_t arg,
                             pt_addr_t *exitcodes)
As in pt_wait(), you can pass in NULL for exitcodes if your threads don't return any useful exit status. Here's pt_fork() in action (click here for source):
 1: /******************************************************************
 2:  * simple3.c -- multithreaded "hello world" using pt_fork()
 3:  *
 4:  * Author: Mark Hays <hays@math.arizona.edu>
 5:  */
 6: 
 7: #include "pt.h"
 8: 
 9: #include <stdio.h>
10: 
11: #define NTHREADS 4
12: 
13: /******** this is the thread code */
14: pt_addr_t hola(pt_arg_t *info)
15: {
16:   int id=pt_myid(info);
17: 
18:   printf("Hello, world, I'm %d\n",id);
19:   /* stash our id into our slot */
20:   *((int *) pt_data(info) + id)=id;
21:   /* no meaningful return value */
22:   return(NULL);
23: }
24: 
25: /******** this is the main thread's code */
26: int main(int argc, char *argv[])
27: {
28:   int i;
29:   int retvals[NTHREADS];
30: 
31:   /* init retvals */
32:   for (i=0; i<NTHREADS; i++) retvals[i]=-1;
33: 
34:   /* do it */
35:   pt_fork(NTHREADS,    /* # of threads to create                */
36:           hola,        /* routine they execute                  */
37:           retvals,     /* a piece of global data they all share */
38:           NULL);       /* ignore exit codes                     */
39: 
40:   /* check return values */
41:   for (i=0; i<NTHREADS; i++)
42:     if (retvals[i]!=i) {
43:       fprintf(stderr,"thread %d didn't return a value\n",i);
44:       exit(1);
45:     }
46:   return(0);
47: }
48: 
49: /* EOF simple3.c */

The major change in main() is the use of pt_fork() on lines 35-38. It creates NTHREADS threads that each execute hola() and waits for them to complete. Since exitcodes is NULL, the exit values of the worker thread are ignored; instead, each thread places its status directly into the retvals array. This is done to avoid casting the integer status to a void *, which can get hairy on 64 bit machines. The declaration of hola() has also changed. In order to use pt_fork(), the start routine must be declared to accept a single argument -- a pointer to a pt_arg_t structure. Inside the start routine, the following macros operate on this structure (which shouldn't otherwise be manipulated):

pt_nthreads(info)
returns the number of threads created by pt_fork().
pt_myid(info)
returns the calling thread's number between 0 and pt_nthreads(info)-1.
pt_self(info)
returns a pointer to the pthread_t corresponding to the calling thread -- some pthreads routines need to know this.
pt_data(info)
returns the argument arg passed to pt_fork().
pt_thread(info,id)
returns a pointer to the pthread_t corresponding to thread #id.
pt_cancel(info,id)
sends a cancellation request to thread #id -- see the pthread_cancel() manpage for more info.
pt_cancel_all(info)
sends cancellation requests to all except the calling thread.

To compile simple2 and simple3, type

        prompt$ make simple2 simple3
They are linked with libpt.a which make automatically builds -- see the Makefile for details. The library source consists of the C files pt.c and ptf77.c.

In the first three examples, there is no shared data; therefore, it can be used as the basis for any communication-free algorithm.


Resource locking
Earlier we noted that the statement i=i+1 did not make sense in a threaded environment. In order to fix this, it is necessary to invent some kind of locking mechanism that serializes access to the variable i. Before getting into this, let's have a brief look at what sorts of things need to be locked.

For concreteness, consider the following code fragment and assume that multiple threads will execute func() simultaneously:

        int i;                  /* i is shared */

        void func(arg1,arg2)
                int arg1;       /* arg1 is private */
                float *arg2;    /* arg2 is private, *arg2 is shared */
        {
        int zzz;                /* zzz is private (auto storage class) */
        static float f;         /* f is shared (static storage class) */

        }
Any thread may freely modify the integers arg1 and zzz because they live on each thread's stack. Threads may freely alter arg2, but not *arg2 or arg2[zzz]. The variables i and f are shared among all threads. Some kind of locking mechanism is needed before i, f, and *arg2 can be accessed in a well-defined way.

Pthreads provides an object called a "mutex" (short for "mutual exclusion") to facilitate this sort of resource locking. Once you create a mutex, there are two things you can do with it:

Mutexes have the property that they can only be locked by one thread at a time. If two threads attempt to lock the same mutex, only one of them succeeds. The other one blocks until the first thread releases the mutex. In general, you use mutexes to protect some piece of shared data. In order to be useful, the mutex itself must be global so that all threads can see it (this is true of all pthreads synchronization objects).

Let's modify the previous code so that we can properly access i (we'll show how to create mutexes later):

        int i;                          /* i is shared */
        pthread_mutex_t ilock;          /* controls access to i */

        ...

        void func(arg1,arg2)
                int arg1;               /* arg1 is private */
                float *arg2;            /* arg2 is private, *arg2 is shared */
        {
        int zzz;                        /* zzz is private (auto storage class) */
        static float f;                 /* f is shared (static storage class) */

        pthread_mutex_lock(&ilock); /* lock i */
        something = i++;                /* access i */
        pthread_mutex_unlock(&ilock);       /* unlock i */
        }
It is up to the programmer to determine exactly what the mutex protects. For example, the mutex ilock could be used to protect i and *arg2. You could have another mutex that serializes access to f. The mutex itself has no idea what its role is in your program -- it is totally up to you to use a mutex to protect shared data.

It is apparent that serializing access to a piece of data can bring all other threads to a screeching halt. Equally apparent is fact that all global data that can be read and written by simultaneously executing threads must be protected by a mutex. In order to minimize the ill effects, it is important to get in and out as quickly as possible. For example, if a thread needs to read some global data, consider copying the data into the thread's private storage instead of keeping a mutex locked for a long period of time. If you need to update a piece of global data, try to calculate the new value with the mutex unlocked -- lock it only when you dump the new value in.

As an example of mutex usage, let's try something more complicated: a parallel matrix multiplication routine. The goal is to multiply an MxN matrix called A by an NxP matrix called B and then store the result into the MxP matrix called C. We could modify the previous example to have each thread do an equal share of the work (this is homework). Instead, we'll have the threads run in a loop that computes single rows of the result, C. This method has certain advantages that will become clear in the discussion below (instead of using the pthread_mutex_xxx routines, we'll use the pt_mutex_xxx wrappers defined in pt.h).

Here's the code (click here for source):

  1: /*****************************************************************
  2:  * mmult.c -- multithreaded matrix multiplication example
  3:  *
  4:  * Author: Mark Hays <hays@math.arizona.edu>
  5:  */
  6: 
  7: #include "pt.h"
  8: 
  9: #include <stdio.h>
 10: #include <stdlib.h>
 11: 
 12: #define NTHREADS 4
 13: 
 14: /* global data area */
 15: int mwork,nwork,pwork;          /* dimensions of matrices  */
 16: double **mata,**matb,**matc;    /* ptrs to the matrices    */
 17: 
 18: int mdone;                      /* # of rows already done  */
 19: pthread_mutex_t mdone_mutex;    /* lock for mdone          */
 20: 
 21: /******************************************************
 22:  * return the next row number to do or -1 if we're done
 23:  */
 24: int nextrow()
 25: {
 26:   int res;
 27: 
 28:   pt_mutex_lock(&mdone_mutex,"mutex lck");     /* lock the mutex      */
 29:   res=mdone++;                                 /* get & incr row cntr */
 30:   pt_mutex_unlock(&mdone_mutex,"mutex unlck"); /* unlock the mutex    */
 31:   return(res<mwork ? res : -1);                /* return -1 if done   */
 32: }
 33: 
 34: /******************************************************************
 35:  * main code for each thread -- it computes rows of the product a*b
 36:  */
 37: pt_addr_t thread_code(pt_arg_t arg)
 38: {
 39:   int n,p;                              /* n and p counters         */
 40:   int m;                                /* row we're working on     */
 41:   double sum;                           /* temporary var            */
 42: 
 43:   while ((m=nextrow())!=-1) {           /* m=row to do              */
 44:     for (p=0; p<pwork; p++) {           /* p=col to do, do each col */
 45:       for (sum=0.0,n=0; n<nwork; n++) {
 46:         sum += mata[m][n]*matb[n][p];   /* compute the elt          */
 47:       }
 48:       matc[m][p]=sum;                   /* save result in c         */
 49:     }
 50:   }
 51:   return(NULL);
 52: }
 53: 
 54: /***********************************
 55:  * this is the main multiply routine
 56:  */
 57: void domult(int m,int n,int p,
 58:        double **a,                    /* a: m x n matrix      */
 59:        double **b,                    /* b: n x p matrix      */
 60:        double **c)                    /* c: m x p matrix=a*b  */
 61: {
 62:   mdone=0; mwork=m; nwork=n; pwork=p;      /* init glb state info  */
 63:   mata=a;  matb=b;  matc=c;
 64: 
 65:   pt_fork(NTHREADS,thread_code,NULL,NULL); /* run code in parallel */
 66: }
 67: 
 68: /**************************
 69:  * allocate an m x n matrix
 70:  */
 71: double **newmat(int m,int n)
 72: {
 73:   double **res=(double **) malloc(m*sizeof(double *));
 74:   int i;
 75: 
 76:   for (i=0; i<m; i++)
 77:     res[i]=(double *) malloc(n*sizeof(double));
 78:   return(res);
 79: }
 80: 
 81: /**************
 82:  * main program
 83:  */
 84: int main(int argc,char *argv[])
 85: {
 86:   double **a=newmat(2,2),**b=newmat(2,2),**c=newmat(2,2);
 87: 
 88:   a[0][0]=1.0; a[0][1]=2.0;                    /* initialize a         */
 89:   a[1][0]=2.0; a[1][1]=1.0;
 90: 
 91:   b[0][0]=1.0; b[0][1]=2.0;                    /* initialize b         */
 92:   b[1][0]=3.0; b[1][1]=4.0;
 93: 
 94:   pt_mutex_init(&mdone_mutex,"init mutex"); /* initialize the mutex */
 95: 
 96:   domult(2,2,2,a,b,c);                         /* do the mmult: ans is */
 97:   printf("%8.4f%8.4f\n",c[0][0],c[0][1]);   /*   7.0000 10.0000      */
 98:   printf("%8.4f%8.4f\n",c[1][0],c[1][1]);   /*   5.0000  8.0000      */
 99: 
100:   pt_mutex_destroy(&mdone_mutex,"del mutex");
101:   return(0);
102: }
103: 
104: /* EOF mmult.c */

The main() routine in this program simply allocates and initializes the matrices a and b. It also creates the result matrix c. On lines 94, a mutex is created. The first argument is a pointer to the mutex descriptor and the second argument is the error message to print in case of difficulties. To deallocate a mutex, you use pt_mutex_destroy() (see line 100). On line 96 we call the parallel matrix multiply routine and pass the matrix dimensions and the three matrices A, B, and C. Once domult() returns, we print out the result and exit.

The domult() routine looks a lot like the main() from the previous example. After initializing some global state information in lines 62-63, domult() creates the worker threads and then reaps them, just as in the previous example. It is OK to use the global variables mwork, mata, etc. in this example because the individual threads only read these values -- the driver routine domult() is the only thing that writes to them. If you need to call a multithreaded code recursively for some reason, you can make the state information local to the driver routine and pass a pointer to the thread code in the pt_fork() call.

The worker threads simply execute the routine thread_code() in lines 37-52. Each thread calls nextrow() to get the index of the next row of c (aka matc) to be computed. It then calculates all elements of the product along this row. When all rows are done, nextrow() returns -1, which causes the calling thread to terminate. The nextrow() routine gets the next row to be done from the counter mdone and then increments mdone. This operation is protected by the mutex mdone_mutex (created in main()).

Why compute a whole row of the product? We could just as easily have a next_element() function that returns the next element to be done. However, experience indicates that the more work a thread can do independently, the less time it sits around waiting to lock mutexes. For an NxN matrix, the modified algorithm would have to lock and unlock the mutex N^2 times, as opposed to N times for the present algorithm. Not only is there more overhead due to making the extra mutex calls, but the number of contentions for access to mdone also increases drastically. Generally, you want to reduce contention wherever possible -- reducing the number of mutex operations is a good place to start.

Why not eliminate the mutex altogether? In fact, if each thread does 1/NTHREADS share of the mwork rows, we don't need a mutex at all and this becomes a contrived example. However, there is some benefit (in the real world) to this methodology. If you set NTHREADS to 4 on a 4 CPU machine and you are the only person using this machine, eliminating the mutex might be satisfactory (if no other jobs were executed while you were doing parallel stuff). That's the perfect world. In the real world, other users will run jobs; for example, emacs, tex, netscape, and doom (during our creative time). If you are lucky, you might get 3 CPUs for most of the time. If you set NTHREADS to 4, the speed of the multiply is bound by the slowest thread (since each must do 1/4 of the total work). Your only option is to recompile your code for different times of day (based on what you think NTHREADS ought to be).

So what does the mutex do for you? Since each thread just asks "what's the next row?", it is no problem if one thread does less work -- the others will pick up the slack. In other words, this "bag of tasks" algorithm has an unintended load-balancing feature. This is Very Useful.


Libraries and global data
We've seen that it is necessary to protect all global data with mutexes so that our code runs in a deterministic manner. What does this imply for libraries? Libraries must clearly follow the same rules; unfortunately, you don't necessarily know whether or not a given library makes use of global data. The DEC threads manual says:

Assume that a call is not thread-safe unless the manpage tells you otherwise.

How can you get burned? As an example consider the malloc() call. This call allocates and returns a pointer to a new chunk of storage; the chunk lives in the shared global data area. The data structures that malloc() uses to keep track of free and allocated blocks, while not directly accessible to the programmer, also live in the global data area. Therefore, if two threads call malloc() without using a mutex, chaos can result. You might think that you could simply wrap all calls to malloc() with a mutex lock/unlock sequence. This will not work. Why? Well, suppose that each thread calls printf():

                printf("i=%d s=%s\n",anint,astring);
The printf() call makes implicit use of malloc() to allocate the buffer that holds the string you are printing. Worse, the printf() call doesn't know about your malloc mutex (and doesn't use it). So, if 2 or more threads call any combination of printf() and malloc(), your code could coredump.

This example illustrates the basic problems you can get into with libraries. Unfortunately, there is no general solution to the problem. The POSIX threads specification does demand that the calls in the C library (including malloc() and printf()) be thread-safe. For other code (that you might download off the 'net), your only recourse may be to "safen" it by hand.

One test for thread-safeness is to ask whether or not the code in question is re-entrant; that is, whether or not it can directly or indirectly call itself recursively. If a piece of code is re-entrant, it is probably thread safe. By the way, this implies that no FORTRAN code is thread-safe (but see below for a counterexample ;-).


Condition variables
So far, our threads have simply run to completion on a given task. In fact, the only communication we've seen is the message "we're done." Sometimes this is not sufficient -- synchronization needs to occur along the way; for example, a calculation might consist of several parts, each dependent on the result of the previous one. One thought is to destroy and respawn new threads for each stage of the computation. This is certainly possible; however, the pthreads library provides a more efficient solution.

The POSIX threads standard gives you an object that helps meet this goal: the condition variable. Mutexes are intended for fine-grained control (as in the last example); condition variables are intended for coarse grained control (as described in the previous paragraph). The mutex stands by itself: once you create it, you lock and unlock it to your heart's content. The condition variable does not stand by itself. In fact, condition variables are usually used in conjunction with two other things: a mutex and a regular old global variable (called a "predicate"). First, let's look at some typical condition variable code (using "raw" pthreads calls without error checking):

        pthread_mutex_lock(&mutex);         /* lock mutex           */
        while (!predicate) {                    /* check predicate      */
          pthread_cond_wait(&condvar,&mutex);   /* go to sleep - recheck
                                                   pred on awakening    */
        }
        pthread_mutex_unlock(&mutex);               /* unlock mutex         */
This code has two possible behaviors. If predicate is nonzero, the thread locks the mutex, falls through the loop, and unlocks the mutex. In other words, nothing happens. If predicate is zero, the thread locks the mutex, enters the loop, and calls pthread_cond_wait(). pthread_cond_wait() does the following: Now that our thread is asleep, we need to wake it up. What typically happens is that another thread does something like:
        pthread_mutex_lock(&mutex);         /* lock the mutex       */
        predicate=1;                            /* set the predicate    */
        pthread_cond_broadcast(&condvar);   /* wake everyone up     */
        pthread_mutex_unlock(&mutex);               /* unlock the mutex     */
The important call is pthread_cond_broadcast() -- it is a wakeup call to all threads waiting on a condition variable. The first thread wakes up still in the call to pthread_cond_wait() -- this call completes after it Now we're back in the while loop. Since the predicate is now nonzero, we fall through the loop, unlock the mutex, and are on our way.

Here's a brief summary of the roles of the various pieces:

  1. The condition variable: calling pthread_cond_wait() puts the calling thread to sleep until another thread calls pthread_cond_broadcast(). The condition variable is used to create this behavior. If you only want to wake a single thread, use pthread_cond_signal().
  2. The predicate: this variable determines whether or not to call pthread_cond_wait(); in other words, whether or not we need to go to sleep (wait). The thread that wakes everyone up doesn't know how many threads are asleep -- it just knows that it will awaken everyone who is asleep. Without the predicate, a straggler thread might arrive late, miss the broadcast, and go to sleep. This is probably not what you want.
  3. The mutex: this ensures safe access to the predicate. The thread that waits reads the predicate and the one that wakes it up writes to the predicate, so there has to be a mutex around somewhere.
  4. To understand the purpose of the while loop, recall that pthread_cond_wait() locks the mutex before it returns. This implies that the sleeping threads wake up one at a time. It may be the case that the first thread to wake up leaves the loop and does something that resets the predicate to zero -- a sign that the other threads should remain asleep. The while loop allows this to happen in a natural fashion. Why does the pthread_cond_wait() call need to know about the mutex? Above, we decided that we needed the predicate and therefore we need the mutex also. Let's look at what happens if we divorce the mutex from the condition variable:
            pthread_mutex_lock(&mutex);         /* lock mutex           */
            while (!predicate) {                    /* check predicate      */
              pthread_mutex_unlock(&mutex);             /* unlock mutex         */
              pthread_cond_wait(&condvar);              /* go to sleep - recheck
                                                       pred on awakening    */
              pthread_mutex_lock(&mutex);               /* lock mutex           */
            }
            pthread_mutex_unlock(&mutex);               /* unlock mutex         */
    
    Besides being longer, it has a serious problem. If the thread enters the loop, there is a moment of indeterminacy just before pthread_cond_wait() is called. It is possible that another thread might acquire the mutex, set the predicate, and issue the broadcast before pthread_cond_wait() gets called. In other words, the thread goes to sleep when it shouldn't. The basic problem is that the mutex unlock and condition variable wait are not guaranteed to be an atomic operation. If, on the other hand, pthread_cond_wait() unlocks the mutex, the UNIX kernel can guarantee that the calling thread will not miss the broadcast -- being the kernel has its privileges.

    A useful pthreads structure
    Before moving on to a real example that uses condition variables, let's define a data structure that repackages some of the condition variable's strangeness. We will need an object that synchronizes N threads at a given point in the code. The interface should look something like:

            [calculation 1]      /* do first computation */
    
            pt_gate_sync(&gate); /* wait for all threads to finish calcn 1 */
    
            [calculation 2]      /* proceed */
    
    The basic constraint is that calculation 2 cannot begin until calculation 1 is complete. The overall strategy is simple: N-1 threads will call pthread_cond_wait() on a condition variable and go to sleep. The Nth thread will call pthread_cond_broadcast() and wake the other threads up. At this point, the calculation will continue.

    First, let's look at the data structure:

    231:  typedef struct _pt_gate_t_ {
    232:    int ngate;              
    233:    int nthreads;           
    234:    pthread_mutex_t mutex;  
    235:    pthread_mutex_t block;  
    236:    pthread_cond_t condvar; 
    237:    pthread_cond_t last;    
    238:  } pt_gate_t;
    239:  
    240:  extern void pt_gate_init(pt_gate_t *gate,int nthreads);
    241:  extern void pt_gate_destroy(pt_gate_t *gate);
    242:  extern void pt_gate_sync(pt_gate_t *gate);
    
    The main data type, pt_gate_t, contains several pthreads objects. There are functions to initialize and destroy this compound object, and a function to perform the behavior specified above. The roles of the various structure members will become clear in a moment.

    Now let's look at the C source:

     68: void pt_gate_init(pt_gate_t *gate,int nthreads)
     69: {
     70:   gate->ngate=0; gate->nthreads=nthreads;
     71:   pt_mutex_init(  &gate->mutex, "gate: init mutex");
     72:   pt_mutex_init(  &gate->block, "gate: init block");
     73:   pt_cond_init (&gate->condvar, "gate: init condvar");
     74:   pt_cond_init (   &gate->last, "gate: init last");
     75: }
     76: 
     77: /*************************************************
     78:  * destroy a gate variable
     79:  */
     80: void pt_gate_destroy(pt_gate_t *gate)
     81: {
     82:   gate->ngate=gate->nthreads=0;
     83:   pt_mutex_destroy(  &gate->mutex, "gate: destroy mutex");
     84:   pt_mutex_destroy(  &gate->block, "gate: destroy block");
     85:   pt_cond_destroy (&gate->condvar, "gate: destroy condvar");
     86:   pt_cond_destroy (   &gate->last, "gate: destroy last");
     87: }
     88: 
     89: /*************************************************
     90:  * enter the gate
     91:  */
     92: void pt_gate_sync(pt_gate_t *gate)
     93: {
     94:   if (gate->nthreads<2) return;           /* trivial case            */
     95:   pt_mutex_lock(&gate->block,             /* lock the block -- new   */
     96:            "gate: lock block");            /*   threads sleep here    */
     97:   pt_mutex_lock(&gate->mutex,             /* lock the mutex          */
     98:            "gate: lock mutex");
     99:   if (++(gate->ngate) < gate->nthreads) { /* are we the last one in? */
    100:     pt_mutex_unlock(&gate->block,         /* no, unlock block and    */
    101:                "gate: unlock block 1");
    102:     pt_cond_wait(&gate->condvar,          /*   go to sleep           */
    103:             &gate->mutex,
    104:             "gate: wait condvar");
    105:   } else {                                /* yes, we're last         */
    106:     pt_cond_broadcast(&gate->condvar,     /* wake everyone up and    */
    107:                  "gate: bcast condvar");
    108:     pt_cond_wait(&gate->last,&gate->mutex,/* go to sleep til they're */
    109:             "gate: wait last");            /* all awake... then       */
    110:     pt_mutex_unlock(&gate->block,         /* release the block       */
    111:                "gate: unlock block 2");
    112:   }
    113:   if (--(gate->ngate)==1) {               /* next to last one out?   */
    114:     pt_cond_broadcast(&gate->last,        /* yes, wake up last one   */
    115:                  "gate: bcast last");
    116:   }
    117:   pt_mutex_unlock(&gate->mutex,           /* release the mutex       */
    118:              "gate: unlock mutex");
    119: }
    
    The gate_init() function simply initializes the members of the gate_t structure. It takes two arguments: a pointer to the gate_t being initialized and N, the number of threads the gate is supposed to synchronize.

    Gate_destroy() frees the resources associated with a gate_t structure.

    The behavior we descibed earlier occurs in pt_gate_sync() and is implemented in lines 92-119. Ignore all statements involving gate->block for a moment. The mutex gate->mutex simply protects access to gate->ngate. The calling thread locks this mutex in line 97. The member gate->nthreads does not need protection: it's value is only updated by the gate_init() call. In line 99, the ngate member is incremented and compared to nthreads. If the calling thread is not the last one, pt_cond_wait is called. This puts the thread to sleep until the last thread calls pt_gate_sync().

    The last thread executes the else clause on line 105. First it calls pthread_cond_broadcast() (once this thread releases gate->mutex, the sleeping threads will begin waking up). Next, it goes to sleep on the gate->last condition variable (whose role will become clearer below), which has the side effect of unlocking gate->mutex. At this point, the other threads wake up one at a time (after locking gate->mutex). Each thread decrements gate->ngate, releases gate->mutex, and finally leaves pt_gate_sync(). Except for the last thread to wake up: it wakes up the thread sleeping on gate->last before exiting pt_gate_sync().

    In order to understand the role of gate->block, consider the following code fragment:

            gate_t gate;
    
            while (condition) {
              [do something]
              enter_gate(&gate);
              [do something else]
            }
    
    If the computations in square brackets do not take very long (for example, you're doing a small problem because you're debugging your algorithm), it would be possible for a thread that just woke up to almost instantly re-enter the gate and goof things up for the threads that are still asleep. This happens more often than you would think, and results in your code mysteriously hanging up. The extra mutex prevents this pathology at fairly low cost. In my opinion, tracking down these cases is the most time consuming part of threads programming; such bugs are inherently intermittent and depend on the system load average, disk activity, etc.

    Since the threads wake up from pthread_cond_wait() one at a time, it is necessary to restrict access to the gate until all the newly awakened threads actually leave the gate (so that ngate==0 and the incoming thread does the right thing at the if in line 99). The code locks access to the gate via the gate->block mutex; in fact, it is the first thing locked on entry to pt_gate_sync(). If a thread goes to sleep in line 102, it must first release the block so that other threads can get in and open the gate. The last thread wakes the sleeping threads up with the gate blocked. Thus, no other threads can enter the gate while the wakeup call occurs. The last thread to leave the gate unlocks the block mutex, thus allowing other threads in. Why do we need the gate->last condition variable? Because, as far as I can tell, it is illegal for a thread to unlock a mutex held by another thread.

    Now that we have the building blocks, let's do an example.


    Parallel LU decomposition
    Below is the source code (courtesy of Anu Rao) for a parallel LU decomposition. (click here for source):

     1: /* Anu Rao 11/1/94 CSC 652
     2:    LU decomposition using the incremental method; row parallelized
     3:      Assumptions: global variables: matrix a */
     4: 
     5: #include "pt.h"
     6: 
     7: #include <stdio.h>
     8: #include <stdlib.h>
     9: 
    10: #define max(a,b) ((a) > (b) ? (a) : (b))
    11: 
    12: double **a;
    13: 
    14: int asize,nworkers;
    15: 
    16: pt_gate_t gate;
    17: 
    18: void LU(pt_arg_t *info)
    19: {
    20:   int myID=pt_myid(info);
    21:   int i, j, k;
    22:   int startrow, endrow, goof;
    23: 
    24:   /* define starting & ending points for processor myID */
    25:   startrow = (myID * asize)/nworkers;
    26:   endrow = startrow + (asize/nworkers) - 1;
    27:   
    28:   for ( k = 0; k <= asize; k++ ) {
    29:     goof = max(k+1,startrow);
    30:     for ( i = goof; i <= endrow; i++ ) 
    31:       a[i][k] /= a[k][k];  
    32:     pt_gate_sync(&gate);
    33: 
    34:     /* update l's and u's by one term */
    35:     for ( i = goof; i <= endrow; i++ ) {
    36:       for ( j = k+1; j < asize; j++ ) 
    37:         a[i][j] -= a[i][k] * a[k][j];
    38:     }  
    39:     pt_gate_sync(&gate);
    40:   }
    41: }
    42: 
    43: int main(int argc,char *argv[])
    44: {
    45:   int i, j;
    46: 
    47:   if (argc!=3) {
    48:     asize=8;
    49:     nworkers=4;
    50:   } else {
    51:     /* read in size of matrix, number of processors */
    52:     asize = atoi(argv[1]);
    53:     nworkers = atoi(argv[2]);
    54:   }
    55: 
    56:   /* allocate a to be an n by n matrix */
    57:   a = (double **)malloc(asize*sizeof(double *));
    58:   for (i = 0; i < asize; i++) {
    59:     a[i] = (double *)malloc(asize*sizeof(double));
    60:   } 
    61: 
    62:   /* set up the gate */
    63:   pt_gate_init(&gate,nworkers);
    64: 
    65:   /* initialize a */
    66:   for (i = 0; i < asize; i++) {
    67:     for (j = 0; j < asize; j++) {
    68:       a[i][j] = 1.0;
    69:       if (i == j) a[i][j] = 2.0;
    70:     }
    71:   }
    72: 
    73:   /* do the decomposition */
    74:   pt_fork(nworkers,LU,NULL,NULL);
    75: 
    76:   /* print results */
    77:   for (i = 0; i < asize; i++) {
    78:     for (j = 0; j < asize; j++) {
    79:       printf("%8.3lf ",a[i][j]);
    80:     }
    81:     printf("\n");
    82:   }
    83: 
    84:   return(0);
    85: }
    86: 
    87: /* EOF ludcmp.c */
    

    Threads and FORTRAN
    It is a sad fact that FORTRAN makes implicit use of global data. For example, in the code fragment

            subroutine sub
            integer i
            
            do i=1,100
              ...
            end do
    
    the temporary loop variable i is probably in the global data segment. The only way this subroutine can run in parallel (using pthreads) is to lock the entire loop with a mutex -- there is no other way to protect the loop counter i. In other words, you cannot run it in parallel using pthreads (this is not quite true: the DEC FORTRAN compiler has an option that will allocate i on the stack; however, this is highly nonportable and probably not worth doing). The fundamental problem is that FORTRAN code is, by definition, non-reentrant; it is illegal for a FORTRAN subroutine to call itself recursively, either directly or indirectly.

    The High Performance FORTRAN (HPF) "standard" provides a set of extensions that are intended to get around this. HPF gives you routines that add arrays, compute dot products, etc in parallel. HPF appears to provide calls that do many common tasks (I haven't used it).

    The only way I know of to run FORTRAN in parallel on a shared memory multiprocessor is to use a parallelizing compiler or HPF. If the compiler can discover parallelism in your algorithm, this strategy should give acceptable results. I have heard mixed reviews of such compilers: some people swear by them; others say they stink.

    If you need to do something nontrivial in parallel, you can always write that part in C using pthreads and have FORTRAN call your C code. However, this reduces the portability of your code.

    Although it is not generally possible to have several threads execute the same routine in parallel in FORTRAN, it is sometimes possible to have different routines executing simultaneously. As an example, let's suppose that our FORTRAN code can be broken up into two pieces: one to do the calculations and another to process and write the data to a file. Once a stage of the overall computation is complete, we can hand the results over to a processing and I/O routine. Having done this, our compuational engine can proceed with the next step.

    This is much different than the pt_fork routine: in pt_fork, each thread is performing a similar task. In the above example, the threads are performing orthogonal tasks. In FORTRAN, this is necessary: it is the only way to achieve address space separation among the threads.

    The source code for this FORTRAN example is in the files rwf.com and rwf.f. It uses support routines from ptf77.c and pt.c. There is a new C data structure defined in pt.c and pt.h that implements a threaded pipeline stage. The above example uses a one stage pipeline. This structure is described in the next section. As always, this code has withstood minutes of testing ;-)


    A Pipeline Structure
    Here is a depiction of how a pipeline stage works:

       main thread  worker thread
               \      /      \
                \    /        \
                gate1         |
               /     \        |
              /       \       |
           setup()     |     stage()
              \       /       |
               \     /        |
                gate2         |
               /    \         /
              /      \_______/
             |
       main continues
    
    The pipeline consists of a worker thread, two gates (each managing two threads), and two functions. The first function, stage(), is the code that is executed by the worker thread. The second function, setup(), contains initialization code that is executed by the master thread. Typically, you'd use it to set up the environment for the stage() function.

    Let's run through a pipeline pass. When the pipeline is created, the worker thread is immediately dropped into gate1. Since this gate manages two threads, the worker thread simply blocks.

    When it is time for the worker to do its thing, the master thread calls pt_pipeline_execute(). The following happen inside of this call: first, the master thread enters gate1. This has the effect of releasing both of the threads from gate1. The worker thread immediately enters gate2 and blocks. Why? So that the master thread can execute the setup() function without interference. As stated before, setup() will typically initialize global data that is processed (and only processed) by the worker in stage(). You can think of gate2 as a sort of "super mutex". Having executed setup(), the master thread drops into gate2, at which point both threads emerge from gate2. The master thread now returns from pt_pipeline_execute() and goes about its business. Meanwhile, the worker thread executes its code in stage(). Once this is complete, it drops back into gate1 where it sleeps until another thread calls pt_pipeline_execute().

    What happens if the master thread calls pt_pipeline_execute() before the previous invocation of stage() completes? Since the master thread enters gate1, it will block until the worker thread finishes stage() and also enters gate1. In other words, the pipeline stage is completely synchronous.

    Here's a small C program that implements multithreaded IO using a one stage pipeline (click here for source):

     1: /***********************************************************************
     2:  * rw.c -- implement I/O in separate thread
     3:  *
     4:  * Author: Mark Hays <hays@math.arizona.edu>
     5:  *
     6:  * NOTE: see the notes in rwf.f for warnings/restrictions.
     7:  *
     8:  */
     9: 
    10: #include "pt.h"
    11: 
    12: #include <stdio.h>
    13: #include <stdlib.h>
    14: 
    15: /* global data we'll pass around */
    16: typedef struct _rw_info_t {
    17:   float *buffer,*maindata;
    18:   int count;
    19: } rw_info_t;
    20: 
    21: /* master does this before slave executes */
    22: void *setup(rw_info_t *info)
    23: {
    24:   float *src=info->maindata,*dst=info->buffer;
    25:   int n=info->count,i;
    26: 
    27:   /* copy main program's data info I/O buffer */
    28:   for (i=0; i<n; i++) *dst++=*src++;
    29: }
    30: 
    31: /* slave runs this */
    32: void *doio(rw_info_t *info)
    33: {
    34:   float *buf=info->buffer;
    35:   int n=info->count,i;
    36: 
    37:   for (i=0; i<n; i++) printf("%12.4f ",*buf++);
    38:   printf("\n");
    39: }
    40: 
    41: #define N 4
    42: 
    43: int main(int argc,char *argv[])
    44: {
    45:   int i,j;
    46:   float *data;
    47:   rw_info_t info;
    48:   pt_pipeline_t p;
    49: 
    50:   /* initialize info structure */
    51:   info.buffer=(float *) malloc(N*sizeof(float));
    52:   info.maindata=data=(float *) malloc(N*sizeof(float));
    53:   info.count=N;
    54: 
    55:   /* initial data */
    56:   for (j=0; j<N; j++) data[j]=j+1;
    57: 
    58:   /* set up the pipeline */
    59:   pt_pipeline_init(&p,     /* pt_pipeline_t struct */
    60:                &info,  /* global (shared) data */
    61:                setup,  /* stage setup routine */
    62:                doio);  /* slave's code */
    63:   /* main code */
    64:   for (i=0; i<5; i++) {
    65:     for (j=0; j<N; j++) data[j]*=2;
    66:     pt_pipeline_execute(&p);
    67:   }
    68:   /* clean up and exit */
    69:   pt_pipeline_destroy(&p);
    70:   return(0);
    71: }
    72: 
    73: /* EOF rw.c */
    

    The important feature is that the main thread copies the data to be written into a buffer that the IO thread uses. This way, the main thread can safely modify its data as the IO thread processes and writes out a copy of the data. For a large enough simulation (or with slow enough IO), this can be a big win in terms of time. The downside is that two copies of the data must be maintained, which can be a problem for large-memory simulations.

    It is very important to call pt_pipeline_destroy(). Besides freeing the pthreads structures, it kills the worker thread after making sure that any currently executing stage() function completes. It accomplishes this by entering gate1 before shutting everything down.

    Links to the FORTRAN version of this program appear in the previous section. The code is quite similar and demonstrates that it is indeed possible to use pthreads and FORTRAN together.


    List of major pthreads routines

    • pthread_cancel
    • pthread_create
    • pthread_cond_broadcast
    • pthread_cond_destroy
    • pthread_cond_init
    • pthread_cond_signal
    • pthread_cond_wait
    • pthread_detach
    • pthread_exit
    • pthread_join
    • pthread_mutex_destroy
    • pthread_mutex_init
    • pthread_mutex_lock
    • pthread_mutex_unlock
    • pthread_testcancel

    Glossary
    Below is a somewhat loose glossary of some terms used in this document.

    Blocking
    A process is said to block when it is forced to wait for a system resource (this is generally transparent to the user). For example, when two threads attempt to acquire a mutex, one of them succeeds and the other blocks until the mutex is available.
    BSD/Berkeley UNIX
    BSD stands for Berkeley software distribution. BSD UNIX is the original variant of the UNIX operating system. Our Sun systems run BSD UNIX.
    Condition variable
    A POSIX threads object that facilitates coarse-grained synchronization among threads. It is generally used in conjunction with a mutex and a predicate.
    Global data
    Data in the static and extern storage classes is global -- it is accessible by all threads in a process. Data in the heap is also global for the same reason. Variables in the register and automatic storage classes are private since they live in the per-thread context (registers and stack).
    IPC
    IPC stands for interprocess communication and covers a number of mechanisms that processes can use to communicate with one another.
    Kernel mode
    Kernel mode is the CPU mode that the UNIX kernel runs in. In this mode, the CPU has unrestricted access to all system resources (including disks, memory, etc.). The CPU usually enters kernel mode during system calls and during the normal timeslicing operations of the UNIX operating system.
    Library call
    A library call is a "normal" function call that runs entirely in user mode.
    Mutex
    A mutex (which stands for "mutual exclusion" is a pthreads object that facilitates fine-grained parallelism. Mutexes are in one of two states: locked and unlocked. A mutex can be locked by one thread at most -- attempting to lock a previously locked mutex causes the calling thread to block.
    POSIX
    POSIX is also known as IEEE standard 1003.1. It is yet another flavor of UNIX. Linux systems are POSIX compliant.
    Predicate
    A predicate is a global variable that is normally used in conjunction with a condition variable.
    Process
    A process is the UNIX kernel's view of your running program. A process consists of one or more threads of execution, a set of system resources (such as open files), some virtual memory resources (text, data, and stack segments), and various other goodies.
    Sleep
    Sleeping is similar to blocking in that the thread involved is suspended for a period of time. The difference is that blocking typically involves waiting on system resources, while sleeping generally involves waiting on an event.
    System call
    A system call is a call that requires kernel intervention to complete. Generally, system calls involve access to global resources (such as disks). System calls require at least 2 context switches to complete: a switch from user mode to kernel mode and back again. For this reason, system calls are slower than library calls.
    System V
    System V is a variant of the UNIX operating system. Our DEC and SGI systems run SysV UNIX.
    Thread
    A thread is a sequence of instructions to be executed within a program. Traditional UNIX process involve a single thread of execution that lives in main(). In a multithreaded process, there are a number of threads. Initially there is a single thread in main(). This thread creates the other threads, each of which can execute arbitrary code.
    User mode
    User mode is the CPU mode that your program usually runs in. The other mode is called kernel mode.

    Downloading the Source

    A compressed tar file containing all of the examples used in this document and this document itself is available here.

    If you are using DCE pthreads on a DEC Alpha running OSF/1 version 3.2, you can get an unmaintained, older version here.

    $Id: threads.prot,v 1.3 1997/02/25 22:16:22 hays Exp $
http://math.arizona.edu/~swig/documentation/pthreads/index.php
Last modified: Tue, 06 Aug 2013 11:52:56 -0600
E-mail: swig@math.arizona.edu
Valid XHTML 1.0! Valid CSS!