I'm trying to create a program that creates an array and, with OpenMP, assigns values to each position in that array. That would be trivial, except that I want to specify which positions an array is responsible for.
For example, if I have an array of length 80 and 8 threads, I want to make sure that thread 0 only writes to positions 0-9, thread 1 to 10-19 and so on.
I'm very new to OpenMP, so I tried the following:
#include <omp.h>
#include <stdio.h>
#define N 80
int main (int argc, char *argv[])
{
int nthreads = 8, tid, i, base, a[N];
#pragma omp parallel
{
tid = omp_get_thread_num();
base = ((float)tid/(float)nthreads) * N;
for (i = 0; i < N/nthreads; i++) {
a[base + i] = 0;
printf("%d %d\n", tid, base+i);
}
}
return 0;
}
This program, however, doesn't access all positions, as I expected. The output is different every time I run it, and it might be for example:
4 40
5 51
5 52
5 53
5 54
5 55
5 56
5 57
5 58
5 59
5 50
4 40
6 60
6 60
3 30
0 0
1 10
I think I'm missing a directive, but I don't know which one it is.
The way to ensure that things work the way you want is to have a loop of just 8 iterations as the outer (parallel) loop, and have each thread execute an inner loop which accesses just the right elements:
#pragma omp parallel for private(j)
for(i = 0; i < 8; i++) {
for(j = 0; j < 10; j++) {
a[10*i+j] = 0;
printf("thread %d updated element %d\n", omp_get_thread_num(), 8*i+j);
}
}
I was unable to test this right now but I'm 90% sure this does exactly what you want (and you have "complete control" over how things work when you do it like this). However it may not be the most efficient thing to do. For one thing - when you just want to set a bunch of elements to zero, you want to use a built in function like memset, not a loop...
You're missing a fair bit. The directive
#pragma omp parallel
only tells the run time that the following block of code is to be executed in parallel, essentially by all threads. But it doesn't specify that the work is to be shared out across threads, just that all threads are to execute the block. To share the work your code will need another directive, something like this
#pragma omp parallel
{
#pragma omp for
...
It's the for directive which distributes the work across threads.
However, you are making a mistake in the design of your program which is even more serious than your unfamiliarity with the syntax of OpenMP. Manual decomposition of work across threads, as you propose, is just what OpenMP is designed to help programmers avoid. By trying to do the decomposition yourself you are programming against the grain of OpenMP and run two risks:
Of getting things wrong; in particular of getting wrong matters that the compiler and run-time will get right with no effort or thought on your part.
Of carefully crafting a parallel program which runs more slowly than its serial equivalent.
If you want some control over the allocation of work to threads investigate the schedule clause. I suggest that you start your parallel region something like this (note that I am fusing the two directives into one statement):
#pragma omp parallel for default(none) shared(a,base,N)
{
for (i = 0; i < N; i++) {
a[base + i] = 0;
}
Note also that I have specified the accessibility of variables. This is a good practice especially when learning OpenMP. The compiler will make i private automatically.
As I have written it the run-time will divide the iterations over i into chunks, one for each thread. The first thread will get i = 0..N/num_threads, the second i = (N/num_threads)+1..2N/num_threads and so on.
Later you can add a schedule clause explicitly to the directive. What I have written above is equivalent to
#pragma omp parallel for default(none) shared(a,N) schedule(static)
but you can also experiment with
#pragma omp parallel for default(none) shared(a,N) schedule(dynamic,chunk_size)
and a number of other options which are well documented in the usual places.
#pragma omp parallel is not enough for the for loop to be parallelized.
Ummm... I noticed that you actually try to distribute work by hand. The reason it does not work is most probably becasue of racing conditions on computing the parameters for the for loop.
If I recall properly any variables declared outside of the parallel region are shared among threads. So ALL threads write to i, tid and base at once. You could make it work with appropriate private/shared clauses.
However, a better ways is to let OpenMP distribute the work.
This is sufficient:
#pragma omp parallel private(tid)
{
tid = omp_get_thread_num();
#pramga omp for
for (i = 0; i < N; i++) {
a[i] = 0;
printf("%d %d\n", tid, i);
}
}
Note that private(tid) it makes a local copy of tid for each thread, so they do not overwrite each other on the omp_get_thread_num(). Also it is possible to declare shared(a) because we want each thread to work on the same copy of table. This is implicit now. I believe iterators should be declared private, but I think pragma takes care of it, not 100% how it is this specific case, when its declared outside the parallel region. But I'm sure you can actually set it to shared by hand and mess it up.
EDIT: I noticed original underlying problem so I took out irrelevant parts.
Related
I'm fairly new to OpenMP and I have some Monte Carlo code I am trying to parallelise.
I have a for-loop which must be ran serially which calls the new_value() function:
for(int i = 0; i < MAX_VAL; i++)
new_value();
This function opens a parallel region on each call:
void new_value()
{
#pragma omp parallel default(shared)
{
int thread_rank = omp_get_thread_num();
#pragma omp for schedule(static)
for(int i = 0; i < N; i++)
arr[i] = update(thread_rank);
}
}
Which works but there is a significant amount of overhead associated with the spawning and terminating of threads; I was wondering if anyone knew a way to spawn the threads (and attain thread_rank) before entering the loop without parallelising the loop?
There are several questions asking the same thing but they are either wrong or unanswered, examples of which include:
This question which asks a similar thing and the answer suggests creating a parallel region and then using #pragma omp single on the outer-most loop, but as 'Joe C' said in the answer comments, this does not work. I can confirm that the program just hangs.
This question asks the exact same thing but the (unticked) answer is just to parallelise the outer-most loop running the loop 4000 * num_threads which is neither what the asker wanted nor what I want.
The answer to your second question is actually correct.
#pragma omp parallel
for(int i = 0; i < MAX_VAL; i++)
new_value();
void new_value()
{
int thread_rank = omp_get_thread_num();
#pragma omp for schedule(static)
for(int i = 0; i < N; i++)
arr[i] = update(thread_rank);
}
Is correct and exactly what you want. It has the same semantic as the code in your question. The difference is there is only one parallel region and that the loop variable i is now computed by the whole team. Note that the outer loop is not parallelized in a worksharing manner (omp parallel for).
So when this code is run, num_threads threads will execute the loop header once new_value and reach the omp for all with their private i == 0. They will share the work of the inner loop. Then they will wait until everyone completed the loop at an implicit barrier, increment their private i and repeat... I hope it is clear now that this is the same behavior with respect to the inner loop as before, with less thread management overhead.
I'm trying to learn OpenMP for a program I'm writing. For part of it I'm trying to implement a function to find the average of a large array. Here is my code:
double mean(double* mean_array){
double mean = 0;
omp_set_num_threads( 4 );
#pragma omp parallel for reduction(+:mean)
for (int i=0; i<aSize; i++){
mean = mean + mean_array[i];
}
printf("hello %d\n", omp_get_thread_num());
mean = mean/aSize;
return mean;
}
However if I run the code it runs slower than the sequential version. Also for the print statement I get:
hello 0
hello 0
Which doesn't make much sense to me, shouldn't there be 4 hellos?
Any help would be appreciated.
First, the reason why you are not seeing 4 "hello"s, is because the only part of the program which is executed in parallel is the so called parallel region enclosed within an #pragma omp parallel. In your code that is the loop body (since the omp parallel directive is attached to the for statement), the printf is in the sequential part of the program.
rewriting the code as follows would do the trick:
double mean = 0;
#pragma omp parallel num_threads(4)
{
#pragma omp for reduction(+:mean)
for (int i=0; i<aSize; i++) {
mean += mean_array[i];
}
mean /= aSize;
printf("hello %d\n", omp_get_thread_num());
}
Second, the fact your program runs slower than the sequential version, it can depend on multiple factors. First of all, you need to make sure the array is large enough so that the overhead of creating those threads (which usually happens when the parallel region is created) is negligible. Also, for small arrays you may be running into "cache false sharing" issues in which threads are competing for the same cache line causing performance degradation.
I working to adapt a program to use OpenMP. I have a group of nested for loops. The outermost for loop is a y-axis loops that goes down an image. I would like to run multiple parallel threads on the loop, but I'm having trouble making it fast.
Currently when I run 8 threads it runs like:
thread 0 -> row 0,8,16...
thread 1 -> row 1,9,17...
thread 2 -> row 2,10,18...
thread 3 -> row 3,11,19...
I would like it to run in blocks, so that thread 0 does the first 1/8 of the rows. What is the best way to do this?
Current code:
...
int y_percent = data_size_Y/8;
int thread = 0;
#pragma omp parallel for num_threads(8) firstprivate(vecs, bufferedOut,data_size_X, data_size_Y, kern_cent_X, kern_cent_Y, sum)
for(int y = y_percent*omp_get_thread_num(); y < (omp_get_thread_num()+1)*y_percent; y++){ // the y coordinate of theoutput location we're focusing on
You can use the schedule clause on the pragma statement to specify the chunk size that you are wanting each thread to process. In the example below, I specify the static scheduling method with a chunk size that specifies the number of contiguous iterations each thread should get. In this simple example, each thread will get chunks of 8 iterations each (e.g. thread 0 will get iterations 0-7, thread 1 iterations 8-15, etc). It is worth pointing out that if you aren't concerned with the ordering of chunk distribution (e.g. if you don't care if thread 0 gets the first chunk or not), you can replace static with dynamic. dynamic gives the ability to assign chunks to threads as they need them instead of preassigning chunks to threads from the start (useful for load balancing when some iterations take longer than others). For more information on the scheduling methods, check out the following:
Wikipedia article - Scheduling Clauses
LLNL docs - DO/for Directive
Example:
#include <stdlib.h>
#include <stdio.h>
#include <omp.h>
int main() {
int i;
int iterations = 32;
int num_threads = 4;
#pragma omp parallel for schedule(static, 8) num_threads(num_threads)
for(i=0; i<iterations; i++) {
printf("thread %d: %d\n", omp_get_thread_num(), i);
}
}
You could simply use the following code to achieve that.
#pragma omp parallel for num_threads(8)
for(int y = 0; y < data_size_Y; y++) {
....
}
Generally I think the long list of firstprivate is not necessary. Depending on how you exactly use those variables, most of them should be able to be defined as shared.
I've recently reading up about OpenMP and was trying to parallelize some existing for loops in my program to get a speed-up. However, for some reason I seem to be getting garbage data written to the file. What I mean by that is I don't have Points 1,2,3,4 etc. written to my file, I have Points 1,4,7,8 etc. I suspect this is because I am not keeping track of the threads and it just leads to race conditions?
I have been reading as much as I can find about OpenMP, since it seems like a great abstraction to do multi-threaded programing. I'd appreciate any pointers please to get to the bottom of what I might be doing incorrectly.
Here is what I have been trying to do so far (only the relevant bit of code):
#include <omp.h>
pixelIncrement = Image.rowinc/2;
#pragma omp parallel for
for (int i = 0; i < Image.nrows; i++ )
{
int k =0;
row = Image.data + i * pixelIncrement;
#pragma omp parallel for
for (int j = 0; j < Image.ncols; j++)
{
k++;
disparityThresholdValue = row[j];
// Don't want to save certain points
if ( disparityThresholdValue < threshHold)
{
// Get the data points
x = (int)Image.x[k];
y = (int)Image.y[k];
z = (int)Image.z[k];
grayValue= (int)Image.gray[k];
cloudObject->points[k].x = x;
cloudObject->points[k].y = y;
cloudObject->points[k].z = z;
cloudObject->points[k].grayValue = grayValue;
fprintf( cloudPointsFile, "%f %f %f %d\n", x, y, z, grayValue);
}
}
}
fclose( pointFile );
I did enable OpenMP in my Compiler settings (C/C++ -> Language -> Open MP Support (/openmp).
Any suggestions as to what might be the problem? I am using a Quadcore processor on Windows XP 32-bit.
Are all points written to the file, but just not sequentially, or is the actual point data messed up?
The first case is expected in parallel programming - once you execute something side-by-side you wont be able to guarantee order unless you synchronize the access (at which point you can just leave out the parallelization as it becomes effectively linear). If you need to rely on order, you can parallelize any calculations but need to write it down in one thread.
If the points itself are messed up, check where your variables are declared and if multiple threads are accessing the same.
A few problems here:
#pragma omp parallel for
for (int i = 0; i < Image.nrows; i++ )
{
int k =0;
row = Image.data + i * pixelIncrement;
#pragma omp parallel for
for (int j = 0; j < Image.ncols; j++)
{
k++;
There's no need for the inner parallel for. The outer loop should contain enough work to keep all cores busy.
Also, for the inner loop k is a shared variable and gets incremented in a non-atomic way. x, y, z are also shared among the inner loop threads and overwritten "randomly". Remove the inner directive and see how it goes.
When you have a loop with a nested loop there is no need for a second omp pragma.
It will already paralelize the first loop. Remember that this is valid only if the second loop has to be executed in sequence. You have a sequencial incrementation, so you can not execute the second loop in a random order. OMP pragmas are a very easy and cool way to paralelize code but do not use them too much!
More details here -> Parallel Loops with OpenMP
I am new to OpenMP so this might be very basic.
I have a function:
void do_calc(int input1[], int input2[], int results[]);
Now, the function modifies input1[] during calculations but still can use it for another iteration (it sorts it in various ways), input2[] is different for every iteration and the function stores results in results[].
In one threaded version of the program I just iterate through various input2[]. In parallel version I try this:
#pragma omp parallel for reduction (+:counter) schedule(static) private (i,j)
for (i = 0; i < NUMITER ; i++){
int tempinput1[1000];
int tempresults[1000];
int tempinput2[5] = derive_input_from_i(i, input2[]);
array_copy(input, tempinput);
do_calc(tempinput, tempinput2, tempresults);
for (j = 0; j < 1000; j++)
counter += tempresults[i] //simplified
}
This code works but is very inefficient because I am copying input to tempinput every iteration and I need only one copy per thread. This copy could be then reused in subsequent do_calc invocations. What I would like to do is this:
#do this only once for every thread worker:
array_copy(input, tempinput);
and then tell the thread to store tempinput for iterations it does in the future.
How do I go about it in OpenMP?
Additional performance issues:
a) I would like to have the code which works on dual/quad/octal core processors and let OpenMP determine number of thread workers and for every of them copy input once;
b) My algorithm benefits from input[] being sorted in previous iteration (as then next sort is faster as keys change only slightly for similar i's) so I would like to make sure that number of iterations is divided equally among threads and that thread no 1 gets 0 ... NUMITER/n portion of iterations, thread no 2 gets NUMITER/n ... 2*NUMITER/n etc.
b) Is not that important but it would be very cool to have :)
(I am using Visual Studio 2010 and I have OpenMP 2.0 version)