I am currently working on a tutorial on OpenMP where the following example was given and the author claims that it contains a Bug (The result is not correct). However, I cannot spot it:
#pragma omp parallel for private(temp)
for(i=0;i<N;i++)
{
for (j=0;j<M;j++)
{
temp = b[i]*c[j];
a[i][j] = temp * temp + d[i];
}
}
For me there is no bug as temp, i and j are private and the loop over j is executed sequentially ... Am I missing something ?
As mentioned in the previous comments the variables i and j are not declared in the loop statements. As the compiler takes care of the loop variable i by setting it to private but not j, the inner loop variable is shared thus falsifying the result. One way to correct it would be:
#pragma omp parallel for default (none) private(temp, i, j) shared(a, b, c, d)
for (i = 0; i < N; i++)
{
for (j = 0; j < M; j++)
{
temp = b[i] * c[j];
a[i][j] = temp * temp + d[i];
}
}
Related
I've just started studying parallel programming with OpenMP, and there is a subtle point in the nested loop. I wrote a simple matrix multiplication code, and checked the result that is correct. But actually there are several ways to parallelize this for loop, which may be different in terms of low-level detail, and I wanna ask about it.
At first, I wrote code below, which multiply two matrix A, B and assign the result to C.
for(i = 0; i < N; i++)
{
for(j = 0; j < N; j++)
{
sum = 0;
#pragma omp parallel for reduction(+:sum)
for(k = 0; k < N; k++)
{
sum += A[i][k]*B[k][j];
}
C[i][j] = sum;
}
}
It works, but it takes really long time. And I find out that because of the location of parallel directive, it will construct the parallel region N2 time. I found it by huge increase in user time when I used linux time command.
Next time, I tried code below which also worked.
#pragma omp parallel for private(i, j, k, sum)
for(i = 0; i < N; i++)
{
for(j = 0; j < N; j++)
{
sum = 0;
for(k = 0; k < N; k++)
{
sum += A[i][k]*B[k][j];
}
C[i][j] = sum;
}
}
And the elapsed time is decreased from 72.720s in sequential execution to 5.782s in parallel execution with the code above. And it is the reasonable result because I executed it with 16 cores.
But the flow of the second code is not easily drawn in my mind. I know that if we privatize all loop variables, the program will consider that nested loop as one large loop with size N3. It can be easily checked by executing the code below.
#pragma omp parallel for private(i, j, k)
for(i = 0; i < N; i++)
{
for(j = 0; j < N; j++)
{
for(k = 0; k < N; k++)
{
printf("%d, %d, %d\n", i, j, k);
}
}
}
The printf was executed N3 times.
But in my second matrix multiplication code, there is sum right before and after the innermost loop. And It bothers me to unfold the loop in my mind easily. The third code I wrote is easily unfolded in my mind.
To summarize, I want to know what really happens behind the scene in my second matrix multiplication code, especially with the change of the value of sum. Or I'll really thank you for some recommendation of tools to observe the flow of multithreads program written with OpenMP.
omp for by default only applies to the next direct loop. The inner loops are not affected at all. This means, your can think about your second version like this:
// Example for two threads
with one thread execute
{
// declare private variables "locally"
int i, j, k;
for(i = 0; i < N / 2; i++) // loop range changed
{
for(j = 0; j < N; j++)
{
sum = 0;
for(k = 0; k < N; k++)
{
sum += A[i][k]*B[k][j];
}
C[i][j] = sum;
}
}
}
with the other thread execute
{
// declare private variables "locally"
int i, j, k;
for(i = N / 2; i < N; i++) // loop range changed
{
for(j = 0; j < N; j++)
{
sum = 0;
for(k = 0; k < N; k++)
{
sum += A[i][k]*B[k][j];
}
C[i][j] = sum;
}
}
}
You can simply all reasoning about variables with OpenMP by declaring them as locally as possible. I.e. instead of the explicit declaration use:
#pragma omp parallel for
for(int i = 0; i < N; i++)
{
for(int j = 0; j < N; j++)
{
int sum = 0;
for(int k = 0; k < N; k++)
{
sum += A[i][k]*B[k][j];
}
C[i][j] = sum;
}
}
This way you the private scope of variable more easily.
In some cases it can be beneficial to apply parallelism to multiple loops.
This is done by using collapse, i.e.
#pragma omp parallel for collapse(2)
for(int i = 0; i < N; i++)
{
for(int j = 0; j < N; j++)
You can imagine this works with a transformation like:
#pragma omp parallel for
for (int ij = 0; ij < N * N; ij++)
{
int i = ij / N;
int j = ij % N;
A collapse(3) would not work for this loop because of the sum = 0 in-between.
Now is one more detail:
#pragma omp parallel for
is a shorthand for
#pragma omp parallel
#pragma omp for
The first creates the threads - the second shares the work of a loop among all threads reaching this point. This may not be of importance for the understanding now, but there are use-cases for which it matters. For instance you could write:
#pragma omp parallel
for(int i = 0; i < N; i++)
{
#pragma omp for
for(int j = 0; j < N; j++)
{
I hope this sheds some light on what happens there from a logical point of view.
I'm trying parallelize a for loop but I am not sure how because this loop is using a variable k that is not i (i is incremented by two because I am dealing with two parts of array a at a time) for the index of arrays b and c.
for (int i = 0; i < asize; i +=2)
{
b[k] = a[i];
c[k] = a[i + 1];
k++;
}
#pragma omp parallel for causes a data race with k and produces wrong results
And #pragma omp parallel for private(k) cause each thread to increment k on its own I.E. multiple threads writing to b[0] and c[0] at the same time also producing wrong results.
Something like that?
for (int k = 0; 2*k < asize; k++) // i = 2*k
{
b[k] = a[2*k];
c[k] = a[2*k + 1];
}
I'm trying to learn OpenMP, but the professor moved on to a different subject and I feel like I haven't learned a whole lot (or understood).
After looking at some solved questions here on SO I wrote this bit of code:
Working code now looks like this:
void many_iterations()
{
int it, i, j;
for (it = 0; it < NUM_ITERATIONS; it++)
{
#pragma omp parallel
{
#pragma omp for private(j)
for (i = 0; i < N; i++)
for (j = 0; j < M; j++)
{
if (i == j) B[i][j] = A[i][j] * 2;
else B[i][j] = A[i][j] * 3;
}
}
int **aux = A;
A = B; B = aux;
}
}
I also wrote a serial version (without the #pragma omp bits) and noticed that this version does not actually properly work (outputing A is different between the serial and this version). I then managed to change the two inner for loops to this working bit (correct output as far as I can tell):
for (index = 0; index < N * M; index++)
{
int i = index / M, j = index % M;
// rest of code here
This one does work, but I ran into a problem: running on two threads, it is just as fast as the serial version (with 2 inner fors) and when I tried running this with only one thread the execution time was a lot slower.
Reading online I understood that the parallel section should somehow start before the main for so that it reduces the overhead, but again, my output (A) is wrong.
So my issues are:
How do I set #pragma omp parallel before the first for without ruining the code?
Why is the serial version equal to the 2-thread version of the code with collapsed for loops?
How should I make the code actually more efficient when running on multiple threads?
As a side note, I tried running the serial version with collapsed for loops and I got it to run a lot slower (just like the "parallel" version with 1 thread).
Edit: Trying to use #pragma omp parallel before the it loop:
void many_iterations()
{
int it, i, j;
#pragma omp parallel
{
for (it = 0; it < NUM_ITERATIONS; it++)
{
#pragma omp for private(j)
for (i = 0; i < N; i++)
for (j = 0; j < M; j++)
{
if (i == j) B[i][j] = A[i][j] * 2;
else B[i][j] = A[i][j] * 3;
}
#pragma omp single
{
int **aux = A;
A = B; B = aux;
}
}
}
}
I'm trying to parallelize the code below. It's easy to see that there is a dependency between the values of aux, since they are computed after the inner loop, but they are needed inside that inner loop (note that on the first iteration j = 0, the code inside the inner loop is not executed). On the other hand, there is no dependency between the values of mu because we only update mu[k], but the only values needed for other computations are in mu[j], for 0 <= j < k.
My approach consists in having the elements of aux locked until they are computed. As soon as a given value of aux is computed, the lock of that element is released and every thread can use it. However, with this code a deadlock occurs and I can't figure out why. Does someone have any tips?
Thanks
for (j = 0; j < k; ++j)
locks[j] = 0;
#pragma omp parallel for num_threads(N_THREADS) private(j, i)
for (j = 0; j < k; ++j)
{
vals[j] = (long)0;
for (i = 0; i < j; i++)
{
while(!locks[i]);
vals[j] += mu[j][i] * aux[i];
}
aux[j] = (s[j] - vals[j]);
locks[j] = 1;
mu[k][j] = aux[j] / c[j];
}
Does it also hang when not optimized?
In optimized code, gcc would not bother reading locks[i] more than once, so this:
for (i = 0; i < j; i++) {
while(!locks[i]);
would be like writing:
for (i = 0; i < j; i++) {
if( !locks[i] ) for(;;) {}
Try adding a barrier to force gcc to re-read locks[i]:
#define pause() do { asm volatile("pause;":::"memory"); } while(0)
...
for (i = 0; i < j; i++) {
while(!locks[i]) pause();
HTH
When compiling my source code which does basic matrix-matrix multiplication with auto-vectorization and auto-parallelization enabled, I receive these warnings in console:
C5002: loop not vectorized due to reason '1200'
C5012: loop not parallelized due to reason'1000'
I've read through this resource provided by MSDN which states:
Reason code 1200: Loop contains loop-carried data dependences that prevent vectorization. Different iterations of the loop interfere with each other such that vectorizing the loop would produce wrong answers, and the auto-vectorizer cannot prove to itself that there are no such data dependences.
Reason code 1000: The compiler detected a data dependency in the loop body.
I'm not sure what in my loop is causing problems. Here is the relevant portion of my source code.
// int** A, int** B, int** result, const int dimension
for (int i = 0; i < dimension; ++i) {
for (int j = 0; j < dimension; ++j) {
for (int k = 0; k < dimension; ++k) {
result[i][j] = result[i][j] + A[i][k] * B[k][j];
}
}
}
Any insight would be greatly appreciated.
The loop carried dependence is on result[i][j].
A solution to your problem would be using a temporary variable when summing up the result and do the update outside the inner-most loop like this:
for (int i = 0; i < dimension; ++i) {
for (int j = 0; j < dimension; ++j) {
auto tmp = 0;
for (int k = 0; k < dimension; ++k) {
tmp += A[i][k] * B[k][j];
}
result[i][j] = tmp;
}
}
This is going remove the dependence (since there is more read-after-write of result[i][j] and should help the vectorizer doing a better job.