I am using MacBook Air M1 2020, Apple M1 7-Core GPU, RAM 8GB.
The problem:
I am comparing pairs of arrays which takes around 11 minutes when executed sequentially. Strangely, the more threads I put to work, the more time it takes to finish(even with NOT using mutex). I've so far tried to run it with 2 and then 4 threads.
What could be the problem? I assumed using 4 threads would be more efficient since I have available 7 cores and the execution time seems(to me) to be long enough to compensate for the overhead caused by handling multiple threads.
This is part of the code which I find relevant to this question:
int const xylen = 1024;
static uint8_t voxelGroups[321536][xylen];
int threadCount = 4;
bool areVoxelGroupsIdentical(uint8_t firstArray[xylen], uint8_t secondArray[xylen]){
return memcmp(firstArray, secondArray, xylen*sizeof(uint8_t)) == 0;
}
void* getIdenticalVoxelGroupsCount(void* threadNumber){
for(int i = (int)threadNumber-1; i < 321536-1; i += threadCount){
for(int j = i+1; j < 321536; j++){
if(areVoxelGroupsIdentical(voxelGroups[i], voxelGroups[j])){
pthread_mutex_lock(&mutex);
identicalVoxelGroupsCount++;
pthread_mutex_unlock(&mutex);
}
}
}
return 0;
}
int main(){
// some code
pthread_create(&thread1, NULL, getIdenticalVoxelGroupsCount, (void *)1);
pthread_create(&thread2, NULL, getIdenticalVoxelGroupsCount, (void *)2);
pthread_create(&thread3, NULL, getIdenticalVoxelGroupsCount, (void *)3);
pthread_create(&thread4, NULL, getIdenticalVoxelGroupsCount, (void *)4);
pthread_join(thread1, NULL);
pthread_join(thread2, NULL);
pthread_join(thread3, NULL);
pthread_join(thread4, NULL);
// some code
}
First of all, the lock serialize all the identicalVoxelGroupsCount increments. Using more thread will not speed up this part. On the contrary, it will be slower because cache-line bouncing: the cache lines containing the lock and the increment variable will move from one core to another serially (see: cache coherence protocols). This is generally much slower than doing all the work sequentially because moving a cache line from one core to another introduce a pretty big latency. You do not need a lock. You can instead increment a local variable and then perform a final reduction only once (eg. by updating an atomic variable in the end of getIdenticalVoxelGroupsCount).
Moreover, the interleaving of the loop iterations is not efficient because most of the cache lines containing voxelGroups will be shared between thread. This this not as critical as the first point because threads are only reading the cache lines. Still, this can increase the memory throughput and result in a bottleneck. A much more efficient approach is to split the iterations in relatively-large contiguous chunks. It could be even better to split the blocks in medium-grained tiles to use the cache more efficiently (although this optimization is independent of the parallelization strategy).
Note that you can use OpenMP to do such kind of operation easily and efficiently in C.
Related
This might be a dumb question, i'm very sorry if that's the case. But i'm struggling to take advantage of the multiple cores in my computer to perform multiple computations at the same time in my Quad-Core MacBook. This is not for any particular project, just a general question, since i want to learn for when i eventually do need to do this kind of things
I am aware of threads, but the seem to run in the same core, so i don't seem to gain any performance using them for compute-bound operations (They are very useful for socket based stuff tho!).
I'm also aware of processed that can be created with fork, but i'm nor sure they are guaranteed to use more CPU, or if they, like threads, just help with IO-bound operations.
Finally i'm aware of CUDA, allowing paralellism in the GPU (And i think OpenCL and Compute Shaders also allows my code to run in the CPU in parallel) but i'm currently looking for something that will allow me to take advantage of the multiple CPU cores that my computer has.
In python, i'm aware of the multiprocessing module, which seems to provide an API very similar to threads, but there i do seem to gain an edge by running multiple functions performing computations in parallel. I'm looking into how could i get this same advantage in C, but i don't seem to be able
Any help pointing me to the right direction would be very much appreciated
Note: I'm trying to achive true parallelism, not concurrency
Note 2: I'm only aware of threads and using multiple processes in C, with threads i don't seem to be able to win the performance boost i want. And i'm not very familiar with processes, but i'm still not sure if running multiple processes is guaranteed to give me the advantage i'm looking for.
A simple program to heat up your CPU (100% utilization of all available cores).
Hint: The thread starting function does not return, program exit via [CTRL + C]
#include <pthread.h>
void* func(void *arg)
{
while (1);
}
int main()
{
#define NUM_THREADS 4 //use the number of cores (if known)
pthread_t threads[NUM_THREADS];
for (int i=0; i < NUM_THREADS; ++i)
pthread_create(&threads[i], NULL, func, NULL);
for (int i=0; i < NUM_THREADS; ++i)
pthread_join(threads[i], NULL);
return 0;
}
Compilation:
gcc -pthread -o thread_test thread_test.c
If i start ./thread_test, all cores are at 100%.
A word to fork and pthread_create:
fork creates a new process (the current process image will be copied and executed in parallel), while pthread_create will create a new thread, sometimes called a lightweight process.
Both, processes and threads will run in 'parallel' to the parent process.
It depends, when to use a child process over a thread, e.g. a child is able to replace its process image (via exec family) and has its own address space, while threads are able to share the address space of the current parent process.
There are of course a lot more differences, for that i recommend to study the following pages:
man fork
man pthreads
I am aware of threads, but the seem to run in the same core, so i don't seem to gain any performance using them for compute-bound operations (They are very useful for socket based stuff tho!).
No, they don't. Except if you block and your threads don't block, you'll see alll of them running. Just try this (beware that this consumes all your cpu time) that starts 16 threads each counting in a busy loop for 60 s. You will see all of them running and makins your cores to fail to their knees (it runs only a minute this way, then everything ends):
#include <assert.h>
#include <pthread.h>
#include <stdio.h>
#include <stdlib.h>
#include <time.h>
#define N 16 /* had 16 cores, so I used this. Put here as many
* threads as cores you have. */
struct thread_data {
pthread_t thread_id; /* the thread id */
struct timespec end_time; /* time to get out of the tunnel */
int id; /* the array position of the thread */
unsigned long result; /* number of times looped */
};
void *thread_body(void *data)
{
struct thread_data *p = data;
p->result = 0UL;
clock_gettime(CLOCK_REALTIME, &p->end_time);
p->end_time.tv_sec += 60; /* 60 s. */
struct timespec now;
do {
/* just get the time */
clock_gettime(CLOCK_REALTIME, &now);
p->result++;
/* if you call printf() you will see them slowing, as there's a
* common buffer that forces all thread to serialize their outputs
*/
/* check if we are over */
} while ( now.tv_sec < p->end_time.tv_sec
|| now.tv_nsec < p->end_time.tv_nsec);
return p;
} /* thread_body */
int main()
{
struct thread_data thrd_info[N];
for (int i = 0; i < N; i++) {
struct thread_data *d = &thrd_info[i];
d->id = i;
d->result = 0;
printf("Starting thread %d\n", d->id);
int res = pthread_create(&d->thread_id,
NULL, thread_body, d);
if (res < 0) {
perror("pthread_create");
exit(EXIT_FAILURE);
}
printf("Thread %d started\n", d->id);
}
printf("All threads created, waiting for all to finish\n");
for (int i = 0; i < N; i++) {
struct thread_data *joined;
int res = pthread_join(thrd_info[i].thread_id,
(void **)&joined);
if (res < 0) {
perror("pthread_join");
exit(EXIT_FAILURE);
}
printf("PTHREAD %d ended, with value %lu\n",
joined->id, joined->result);
}
} /* main */
Linux and all multithread systems work the same, they create a new execution unit (if both don't share the virtual address space, they are both processes --not exactly so, but this explains the main difference between a process and a thread--) and the available processors are given to each thread as necessary. Threads are normally encapsulated inside processes (they share ---not in linux, if that has not changed recently--- the process id, and virtual memory) Processes run each in a separate virtual space, so they can only share things through the system resources (files, shared memory, communication sockets/pipes, etc.)
The problem with your test case (you don't show it so I have go guess) is that probably you will make all threads in a loop in which you try to print something. If you do that, probably the most time each thread is blocked trying to do I/O (to printf() something)
Stdio FILEs have the problem that they share a buffer between all threads that want to print on the same FILE, and the kernel serializes all the write(2) system calls to the same file descriptor, so if the most of the time you pass in the loop is blocked in a write, the kernel (and stdio) will end serializing all the calls to print, making it to appear that only one thread is working at a time (all the threads will become blocked by the one that is doing the I/O) This busy loop will make all the threads to run in parallel and will show you how the cpu is collapsed.
Parallelism in C can be achieved by using the fork() function. This function simulates a thread by allowing two threads to run simultaneously and share data. The first thread forks itself, and the second thread is then executed as if it was launched from main(). Forking allows multiple processes to be Run concurrently without conflicts arising.
To make sure that data is shared appropriately between the two threads, use the wait() function before accessing shared resources. Wait will block execution of the current program until all database connections are closed or all I/O has been completed, whichever comes first.
I'm currently writing a contrived producer/consumer program that uses a bounded buffer and a number of worker threads to multiply matrices (for the sake of exposition). While trying to assure that the concurrency of my program is optimal I'm observing some unusual behavior. Namely, when executing the program I am only able to achieve up to 100% CPU usage (observed in top), despite having 6 cores. Using shift-i to view the relative percentage changes the upper bound to ~%16.7 and I can clearly see when viewing the usage of different cores by pressing 1 that either only one core is fully maxed out or the load of one core is distributed amongst all six.
No matter what stress tests I run (I tried the stress program and a simple stress test that created multiple threads that spun) I cannot get a single process to use more than 100% (or ~%16.7 relative to all available cores) CPU, so I presume the parallelism is bound to a single core. This behavior is observed on an Ubuntu LTS 20.10 running within VirtualBox on a Mac host with a 2.3 GHz 8-Core Intel Core i9 processor. Is there some way I must enable multi-core parallelism in VirtualBox or is this perhaps just an idiosyncrasy of the setup?
For reference here is the simple stress test I used
void *prod_worker(void *arg) {
while (1) {
printf("...");
}
}
int main (int argc, char * argv[])
{
printf("pid: %lun\n", getpid());
getc(stdin);
int numw = atoi(argv[1]);
pthread_t *prod_threads = malloc(sizeof(pthread_t) * numw);
for (int i = 0; i < numw; i++) {
pthread_t prod;
int rcp;
rcp = pthread_create(&prod, NULL, prod_worker, NULL);
if (rcp == THREAD_CREATE_SUCCESS) {
prod_threads[i] = prod;
} else {
printf("Failed to create producer and consumer thread #%d...\n", i);
printf("Error codes: prod = %d\n", rcp);
printf("Retrying...\n");
i--;
}
}
for (int i = 0; i < numw; i++) {
pthread_join(prod_threads[i], NULL);
}
return 0;
}
printf is thread safe, which typically means there is a mutex of some kind on the stdout stream, such that only one thread can print at a time. Thus even though all your threads are "running", at any given time, all but one of them is likely to be waiting to take ownership of the stream, thus doing nothing useful and using no CPU.
You might want to instead try a test where the threads do some computation, instead of just I/O.
I'd like to create multi-threads program in C (Linux) with:
Infinite loop with infinite number of tasks
One thread per one task
Limit the total number of threads, so if for instance total threads number is more then MAX_THREADS_NUMBER, do sleep(), until total threads number become less then MAX_THREADS_NUMBER, continue after.
Resume: I need to do infinite number of tasks(one task per one thread) and I'd like to know how to implement it using pthreads in C.
Here is my code:
#include <stdio.h>
#include <string.h>
#include <pthread.h>
#include <stdlib.h>
#include <unistd.h>
#define MAX_THREADS 50
pthread_t thread[MAX_THREADS];
int counter;
pthread_mutex_t lock;
void* doSomeThing(void *arg)
{
pthread_mutex_lock(&lock);
counter += 1;
printf("Job %d started\n", counter);
pthread_mutex_unlock(&lock);
return NULL;
}
int main(void)
{
int i = 0;
int ret;
if (pthread_mutex_init(&lock, NULL) != 0)
{
printf("\n mutex init failed\n");
return 1;
}
for (i = 0; i < MAX_THREADS; i++) {
ret = pthread_create(&(thread[i]), NULL, &doSomeThing, NULL);
if (ret != 0)
printf("\ncan't create thread :[%s]", strerror(ret));
}
// Wait all threads to finish
for (i = 0; i < MAX_THREADS; i++) {
pthread_join(thread[i], NULL);
}
pthread_mutex_destroy(&lock);
return 0;
}
How to make this loop infinite?
for (i = 0; i < MAX_THREADS; i++) {
ret = pthread_create(&(thread[i]), NULL, &doSomeThing, NULL);
if (ret != 0)
printf("\ncan't create thread :[%s]", strerror(ret));
}
I need something like this:
while (1) {
if (thread_number > MAX_THREADS_NUMBER)
sleep(1);
ret = pthread_create(...);
if (ret != 0)
printf("\ncan't create thread :[%s]", strerror(ret));
}
Your current program is based on a simple dispatch design: the initial thread creates worker threads, assigning each one a task to perform. Your question is, how you make this work for any number of tasks, any number of worker threads. The answer is, you don't: your chosen design makes such a modification basically impossible.
Even if I were to answer your stated questions, it would not make the program behave the way you'd like. It might work after a fashion, but it'd be like a bicycle with square wheels: not very practical, nor robust -- not even fun after you stop laughing at how silly it looks.
The solution, as I wrote in a comment to the original question, is to change the underlying design: from a simple dispatch to a thread pool approach.
Implementing a thread pool requires two things: First, is to change your viewpoint from starting a thread and having it perform a task, to each thread in the "pool" grabbing a task to perform, and returning to the "pool" after they have performed it. Understanding this is the hard part. The second part, implementing a way for each thread to grab a new task, is simple: this typically centers around a data structure, protected with locks of some sort. The exact data structure does depend on what the actual work to do is, however.
Let's assume you wanted to parallelize the calculation of the Mandelbrot set (or rather, the escape time, or the number of iterations needed before a point can be ruled to be outside the set; the Wikipedia page contains pseudocode for exactly this). This is one of the "embarrassingly parallel" problems; those where the sub-problems (here, each point) can be solved without any dependencies.
Here's how I'd do the core of the thread pool in this case. First, the escape time or iteration count needs to be recorded for each point. Let's say we use an unsigned int for this. We also need the number of points (it is a 2D array), a way to calculate the complex number that corresponds to each point, plus some way to know which points have either been computed, or are being computed. Plus mutually exclusive locking, so that only one thread will modify the data structure at once. So:
typedef struct {
int x_size, y_size;
size_t stride;
double r_0, i_0;
double r_dx, i_dx;
double r_dy, i_dy;
unsigned int *iterations;
sem_t done;
pthread_mutex_t mutex;
int x, y;
} fractal_work;
When an instance of fractal_work is constructed, x_size and y_size are the number of columns and rows in the iterations map. The number of iterations (or escape time) for point x,y is stored in iterations[x+y*stride]. The real part of the complex coordinate for that point is r_0 + x*r_dx + y*r_dy, and imaginary part is i_0 + x*i_dx + y*i_dy (which allows you to scale and rotate the fractal freely).
When a thread grabs the next available point, it first locks the mutex, and copies the x and y values (for itself to work on). Then, it increases x. If x >= x_size, it resets x to zero, and increases y. Finally, it unlocks the mutex, and calculates the escape time for that point.
However, if x == 0 && y >= y_size, the thread posts on the done semaphore and exits, letting the initial thread know that the fractal is complete. (The initial thread just needs to call sem_wait() once for each thread it created.)
The thread worker function is then something like the following:
void *fractal_worker(void *data)
{
fractal_work *const work = (fractal_work *)data;
int x, y;
while (1) {
pthread_mutex_lock(&(work->mutex));
/* No more work to do? */
if (work->x == 0 && work->y >= work->y_size) {
sem_post(&(work->done));
pthread_mutex_unlock(&(work->mutex));
return NULL;
}
/* Grab this task (point), advance to next. */
x = work->x;
y = work->y;
if (++(work->x) >= work->x_size) {
work->x = 0;
++(work->y);
}
pthread_mutex_unlock(&(work->mutex));
/* z.r = work->r_0 + (double)x * work->r_dx + (double)y * work->r_dy;
z.i = work->i_0 + (double)x * work->i_dx + (double)y * work->i_dy;
TODO: implement the fractal iteration,
and count the iterations (say, n)
save the escape time (number of iterations)
in the work->iterations array; e.g.
work->iterations[(size_t)x + work->stride*(size_t)y] = n;
*/
}
}
The program first creates the fractal_work data structure for the worker threads to work on, initializes it, then creates some number of threads giving each thread the address of that fractal_work structure. It can then call fractal_worker() itself too, to "join the thread pool". (This pool automatically "drains", i.e. threads will return/exit, when all points in the fractal are done.)
Finally, the main thread calls sem_wait() on the done semaphore, as many times as it created worker threads, to ensure all the work is done.
The exact fields in the fractal_work structure above do not matter. However, it is at the very core of the thread pool. Typically, there is at least one mutex or rwlock protecting the work details, so that each worker thread gets unique work details, as well as some kind of flag or condition variable or semaphore to let the original thread know that the task is now complete.
In a multithreaded server, there is usually only one instance of the structure (or variables) describing the work queue. It may even contain things like minimum and maximum number of threads, allowing the worker threads to control their own number to dynamically respond to the amount of work available. This sounds magical, but is actually simple to implement: when a thread has completed its work, or is woken up in the pool with no work, and is holding the mutex, it first examines how many queued jobs there are, and what the current number of worker threads is. If there are more than the minimum number of threads, and no work to do, the thread reduces the number of threads, and exits. If there are less than the maximum number of threads, and there is a lot of work to do, the thread first creates a new thread, then grabs the next task to work on. (Yes, any thread can create new threads into the process. They are all on equal footing, too.)
A lot of the code in a practical multithreaded application using one or more thread pools to do work, is some sort of bookkeeping. Thread pool approaches very much concentrates on the data, and the computation needed to be performed on the data. I'm sure there are much better examples of thread pools out there somewhere; the hard part is to think of a good task for the application to perform, as the data structures are so task-dependent, and many computations are so simple that parallelizing them makes no sense (since creating new threads does have a small computational cost, it'd be silly to waste time creating threads when a single thread does the same work in the same or less time).
Many tasks that benefit from parallelization, on the other hand, require information to be shared between workers, and that requires a lot of thinking to implement correctly. (For example, although solutions exist for parallelizing molecular dynamics simulations efficiently, most simulators still calculate and exchange data in separate steps, rather than at the same time. It's just that hard to do right, you see.)
All this means that you cannot expect to be able to write the code, unless you understand the concept. Indeed, truly understanding the concepts are the hard part: writing the code is comparatively easy.
Even in the above example, there are certain tripping points: Does the order of posting the semaphore and releasing the mutex matter? (Well, it depends on what the thread that is waiting for the fractal to complete does -- and indeed, if it is waiting yet.) If it was a condition variable instead of a semaphore, it would be essential that the thread that is interested in the fractal completion is waiting on the condition variable, otherwise it would miss the signal/broadcast. (This is also why I used a semaphore.)
I'm debugging a multi-threaded problem with C, pthread and Linux. On my MacOS 10.5.8, C2D, is runs fine, on my Linux computers (2-4 cores) it produces undesired outputs.
I'm not experienced, therefore I attached my code. It's rather simple: each new thread creates two more threads until a maximum is reached. So no big deal... as I thought until a couple of days ago.
Can I force single-core execution to prevent my bugs from occuring?
I profiled the programm execution, instrumenting with Valgrind:
valgrind --tool=drd --read-var-info=yes --trace-mutex=no ./threads
I get a couple of conflicts in the BSS segment - which are caused by my global structs and thread counter variales. However I could mitigate these conflicts with forced signle-core execution because I think the concurrent sheduling of my 2-4 core test-systems are responsible for my errors.
#include <pthread.h>
#include <stdio.h>
#include <stdlib.h>
#include <unistd.h>
#define MAX_THR 12
#define NEW_THR 2
int wait_time = 0; // log global wait time
int num_threads = 0; // how many threads there are
pthread_t threads[MAX_THR]; // global array to collect threads
pthread_mutex_t mut = PTHREAD_MUTEX_INITIALIZER; // sync
struct thread_data
{
int nr; // nr of thread, serves as id
int time; // wait time from rand()
};
struct thread_data thread_data_array[MAX_THR+1];
void
*PrintHello(void *threadarg)
{
if(num_threads < MAX_THR){
// using the argument
pthread_mutex_lock(&mut);
struct thread_data *my_data;
my_data = (struct thread_data *) threadarg;
// updates
my_data->nr = num_threads;
my_data->time= rand() % 10 + 1;
printf("Hello World! It's me, thread #%d and sleep time is %d!\n",
my_data->nr,
my_data->time);
pthread_mutex_unlock(&mut);
// counter
long t = 0;
for(t = 0; t < NEW_THR; t++){
pthread_mutex_lock(&mut);
num_threads++;
wait_time += my_data->time;
pthread_mutex_unlock(&mut);
pthread_create(&threads[num_threads], NULL, PrintHello, &thread_data_array[num_threads]);
sleep(1);
}
printf("Bye from %d thread\n", my_data->nr);
pthread_exit(NULL);
}
return 0;
}
int
main (int argc, char *argv[])
{
long t = 0;
// srand(time(NULL));
if(num_threads < MAX_THR){
for(t = 0; t < NEW_THR; t++){
// -> 2 threads entry point
pthread_mutex_lock(&mut);
// rand time
thread_data_array[num_threads].time = rand() % 10 + 1;
// update global wait time variable
wait_time += thread_data_array[num_threads].time;
num_threads++;
pthread_mutex_unlock(&mut);
pthread_create(&threads[num_threads], NULL, PrintHello, &thread_data_array[num_threads]);
pthread_mutex_lock(&mut);
printf("In main: creating initial thread #%ld\n", t);
pthread_mutex_unlock(&mut);
}
}
for(t = 0; t < MAX_THR; t++){
pthread_join(threads[t], NULL);
}
printf("Bye from program, wait was %d\n", wait_time);
pthread_exit(NULL);
}
I hope that code isn't too bad. I didn't do too much C for a rather long time. :) The problem is:
printf("Bye from %d thread\n", my_data->nr);
my_data->nr sometimes resolves high integer values:
In main: creating initial thread #0
Hello World! It's me, thread #2 and sleep time is 8!
In main: creating initial thread #1
[...]
Hello World! It's me, thread #11 and sleep time is 8!
Bye from 9 thread
Bye from 5 thread
Bye from -1376900240 thread
[...]
I don't now more ways to profile and debug this.
If I debug this, it works - sometimes. Sometimes it doesn't :(
Thanks for reading this long question. :) I hope I didn't share too much of my currently unresolveable confusion.
Since this program seems to be just an exercise in using threads, with no actual goal, it is difficult to suggest how treat your problem rather than treat the symptom. I believe can actually pin a process or thread to a processor in Linux, but doing so for all threads removes most of the benefit of using threads, and I don't actually remember how to do it. Instead I'm going to talk about some things wrong with your program.
C compilers often make a lot of assumptions when they are doing optimizations. One of the assumptions is that unless the current code being examined looks like it might change some variable that variable does not change (this is a very rough approximation to this, and a more accurate explanation would take a very long time).
In this program you have variables which are shared and changed by different threads. If a variable is only read by threads (either const or effectively const after threads that look at it are created) then you don't have much to worry about (and in "read by threads" I'm including the main original thread) because since the variable doesn't change if the compiler only generates code to read that variable once (remembering it in a local temporary variable) or if it generates code to read it over and over the value is always the same so that calculations based on it always come out the same.
To force the compiler not do this you can use the volatile keyword. It is affixed to variable declarations just like the const keyword, and tells the compiler that the value of that variable can change at any instant, so reread it every time its value is needed, and rewrite it every time a new value for it is assigned.
NOTE that for pthread_mutex_t (and similar) variables you do not need volatile. It if were needed on the type(s) that make up pthread_mutex_t on your system volatile would have been used within the definition of pthread_mutex_t. Additionally the functions that access this type take the address of it and are specially written to do the right thing.
I'm sure now you are thinking that you know how to fix your program, but it is not that simple. You are doing math on a shared variable. Doing math on a variable using code like:
x = x + 1;
requires that you know the old value to generate the new value. If x is global then you have to conceptually load x into a register, add 1 to that register, and then store that value back into x. On a RISC processor you actually have to do all 3 of those instructions, and being 3 instructions I'm sure you can see how another thread accessing the same variable at nearly the same time could end up storing a new value in x just after we have read our value -- making our value old, so our calculation and the value we store will be wrong.
If you know any x86 assembly then you probably know that it has instructions that can do math on values in RAM (both getting from and storing the result in the same location in RAM all in one instruction). You might think that this instruction could be used for this operation on x86 systems, and you would almost be right. The problem is that this instruction is still executed in the steps that the RISC instruction would be executed in, and there are several opportunities for another processor to change this variable at the same time as we are doing our math on it. To get around this on x86 there is a lock prefix that may be applied to some x86 instructions, and I believe that glibc header files include atomic macro functions to do this on architectures that can support it, but this can't be done on all architectures.
To work right on all architectures you are going to need to:
int local_thread_count;
int create_a_thread;
pthread_mutex_lock(&count_lock);
local_thread_count = num_threads;
if (local_thread_count < MAX_THR) {
num_threads = local_thread_count + 1;
pthread_mutex_unlock(&count_lock);
thread_data_array[local_thread_count].nr = local_thread_count;
/* moved this into the creator
* since getting it in the
* child will likely get the
* wrong value. */
pthread_create(&threads[local_thread_count], NULL, PrintHello,
&thread_data_array[local_thread_count]);
} else {
pthread_mutex_unlock(&count_lock);
}
Now, since you would have changed the num_threads to volatile you can atomically test and increment the thread count in all threads. At the end of this local_thread_count should be usable as an index into the array of threads. Note that I did not create but 1 thread in this code, while yours was supposed to create several. I did this to make the example more clear, but it should not be too difficult to change it to go ahead and add NEW_THR to num_threads, but if NEW_THR is 2 and MAX_THR - num_threads is 1 (somehow) then you have to handle that correctly somehow.
Now, all of that being said, there may be another way to accomplish similar things by using semaphores. Semaphores are like mutexes, but they have a count associated with them. You would not get a value to use as the index into the array of threads (the function to read a semaphore count won't really give you this), but I thought that it deserved to be mentioned since it is very similar.
man 3 semaphore.h
will tell you a little bit about it.
num_threads should at least be marked volatile, and preferably marked atomic too (although I believe that the int is practically fine), so that at least there is a higher chance that the different threads are seeing the same values. You might want to view the assembler output to see when the writes of num_thread to memory are actually supposedly taking place.
https://computing.llnl.gov/tutorials/pthreads/#PassingArguments
that seems to be the problem. you need to malloc the thread_data struct.
So I've been playing around with pthreads, specifically trying to calculate the product of two matrices. My code is extremely messy because it was just supposed to be a quick little fun project for myself, but the thread theory I used was very similar to:
#include <pthread.h>
#include <stdio.h>
#include <stdlib.h>
#define M 3
#define K 2
#define N 3
#define NUM_THREADS 10
int A [M][K] = { {1,4}, {2,5}, {3,6} };
int B [K][N] = { {8,7,6}, {5,4,3} };
int C [M][N];
struct v {
int i; /* row */
int j; /* column */
};
void *runner(void *param); /* the thread */
int main(int argc, char *argv[]) {
int i,j, count = 0;
for(i = 0; i < M; i++) {
for(j = 0; j < N; j++) {
//Assign a row and column for each thread
struct v *data = (struct v *) malloc(sizeof(struct v));
data->i = i;
data->j = j;
/* Now create the thread passing it data as a parameter */
pthread_t tid; //Thread ID
pthread_attr_t attr; //Set of thread attributes
//Get the default attributes
pthread_attr_init(&attr);
//Create the thread
pthread_create(&tid,&attr,runner,data);
//Make sure the parent waits for all thread to complete
pthread_join(tid, NULL);
count++;
}
}
//Print out the resulting matrix
for(i = 0; i < M; i++) {
for(j = 0; j < N; j++) {
printf("%d ", C[i][j]);
}
printf("\n");
}
}
//The thread will begin control in this function
void *runner(void *param) {
struct v *data = param; // the structure that holds our data
int n, sum = 0; //the counter and sum
//Row multiplied by column
for(n = 0; n< K; n++){
sum += A[data->i][n] * B[n][data->j];
}
//assign the sum to its coordinate
C[data->i][data->j] = sum;
//Exit the thread
pthread_exit(0);
}
source: http://macboypro.com/blog/2009/06/29/matrix-multiplication-in-c-using-pthreads-on-linux/
For the non-threaded version, I used the same setup (3 2-d matrices, dynamically allocated structs to hold r/c), and added a timer. First trials indicated that the non-threaded version was faster. My first thought was that the dimensions were too small to notice a difference, and it was taking longer to create the threads. So I upped the dimensions to about 50x50, randomly filled, and ran it, and I'm still not seeing any performance upgrade with the threaded version.
What am I missing here?
Unless you're working with very large matrices (many thousands of rows/columns), then you are unlikely to see much improvement from this approach. Setting up a thread on a modern CPU/OS is actually pretty expensive in relative terms of CPU time, much more time than a few multiply operations.
Also, it's usually not worthwhile to set up more than one thread per CPU core that you have available. If you have, say, only two cores and you set up 2500 threads (for 50x50 matrices), then the OS is going to spend all its time managing and switching between those 2500 threads rather than doing your calculations.
If you were to set up two threads beforehand (still assuming a two-core CPU), keep those threads available all the time waiting for work to do, and supply them with the 2500 dot products you need to calculate in some kind of synchronised work queue, then you might start to see an improvement. However, it still won't ever be more than 50% better than using only one core.
I'm not entirely sure I understand the source code, but here's what it looks like: You have a loop that runs M*N times. Each time through the loop, you create a thread that fills in one number in the result matrix. But right after you launch the thread, you wait for it to complete. I don't think that you're ever actually running more than one thread.
Even if you were running more than one thread, the thread is doing a trivial amount of work. Even if K was large (you mention 50), 50 multiplications isn't much compared to the cost of starting the thread in the first place. The program should create fewer threads--certainly no more than the number of processors--and assign more work to each.
You don't allow much parallel execution: you wait for the thread immediately after creating it, so there is almost no way for your program to use additional CPUs (i.e. it can never use a third CPU/core). Try to allow more threads to run (probably up to the count of cores you have).
If you have a processor with two cores, then you should just divide the work to be done in two halfs and give each thread one half. The same principle if you have 3, 4, 5 cores. The optimal performance design will always match the number of threads to the number of available cores (by available I mean cores that aren't already being heavily used by other processes).
One other thing you have to consider is that each thread must have its data contiguous and independent from the data for the other threads. Otherwise, memcache misses will slow down sighificantly the processing.
To better understand these issues, I'd recommend the book Patterns for Parallel Programming
http://astore.amazon.com/amazon-books-20/detail/0321228111
Although its code samples are more directed to OpenMP & MPI, and you're using PThreads, still the first half of the book is very rich in fundamental concepts & inner working of multithreading environments, very useful to avoid most of the performance bottlenecks you'll encounter.
Provided the code parallelizes correctly (I won't check it), likely performance boosts only when the code is parallelized in hardware, i.e. threads are really parallel (multi cores, multi cpus, ... other techologies...) and not apparently ("multitasking" way) parallel. Just an idea, I am not sure this is the case.