atomic for tasks

[pojeda]

Hi,

I wonder if there is support for atomic operations, especially in the context of tasks? I have extended a 2d integration from
one of our courses (https://uppmax.github.io/HPC-python/day4/parallel.html) using PyOMP:

import math
from time import perf_counter
from numba.openmp import njit
from numba.openmp import openmp_context as openmp

# grid size
n = 100000

# Task granularity - number of iterations per task
TASK_SIZE = 1000

@njit
def integration2d_omp(n):
 h = math.pi / float(n)
 total_sum = 0.0
 
 with openmp("parallel"):
 with openmp("single"):
 # Create tasks for chunks of the outer loop
 num_tasks = (n + TASK_SIZE - 1) // TASK_SIZE
 
 for task_id in range(num_tasks):
 with openmp("task"):
 i_start = task_id * TASK_SIZE
 i_end = min(i_start + TASK_SIZE, n)
 local_sum = 0.0
 
 for i in range(i_start, i_end):
 x = h * (i + 0.5)
 for j in range(n):
 y = h * (j + 0.5)
 local_sum += math.sin(x + y)
 
 # Atomic update of the shared sum
 with openmp("atomic"):
 total_sum += local_sum
 
 # Wait for all tasks to complete
 with openmp("taskwait"):
 pass
 
 return h**2 * total_sum

if __name__ == "__main__":
 start = perf_counter()
 integral = integration2d_omp(n)
 end = perf_counter()
 print(f"Integral value is {integral:e}, Error is {abs(integral - 0.0):e}")
 print(f"Time spent: {end - start:.2f} sec")

However, the atomic seems not to be supported yet. Do you have any suggestions for using tasks for this problem?

[tgmattso]

Pedro, Support for atomic will need to be addressed by someone else on the team. I am confused. Why do you want to solve your problem with tasks? A much cleaner solution is to use a parallel loop with a reduction. The code would look something like this.

@njit <https://github.com/njit> def integration2d_omp(n): h = math.pi / float(n) total_sum = 0.0 with openmp(“parallel for reduction(+:total_sum)"): for i in range(0,n): x = h * (i + 0.5) for j in range(n): y = h * (j + 0.5) total_sum += math.sin(x + y) return h**2 * total_sum

—Tim

…

On Nov 25, 2025, at 7:41 AM, Pedro Ojeda ***@***.***> wrote: pojeda created an issue (Python-for-HPC/PyOMP#33) <#33> Hi, I wonder if there is support for atomic operations, especially in the context of tasks? I have extended a 2d integration from one of our courses (https://uppmax.github.io/HPC-python/day4/parallel.html) using PyOMP: import math from time import perf_counter from numba.openmp import njit from numba.openmp import openmp_context as openmp grid size n = 100000 Task granularity - number of iterations per task TASK_SIZE = 1000 @njit <https://github.com/njit> def integration2d_omp(n): h = math.pi / float(n) total_sum = 0.0 with openmp("parallel"): with openmp("single"): # Create tasks for chunks of the outer loop num_tasks = (n + TASK_SIZE - 1) // TASK_SIZE for task_id in range(num_tasks): with openmp("task"): i_start = task_id * TASK_SIZE i_end = min(i_start + TASK_SIZE, n) local_sum = 0.0 for i in range(i_start, i_end): x = h * (i + 0.5) for j in range(n): y = h * (j + 0.5) local_sum += math.sin(x + y) # Atomic update of the shared sum with openmp("atomic"): total_sum += local_sum # Wait for all tasks to complete with openmp("taskwait"): pass return h**2 * total_sum if name == "main": start = perf_counter() integral = integration2d_omp(n) end = perf_counter() print(f"Integral value is {integral:e}, Error is {abs(integral - 0.0):e}") print(f"Time spent: {end - start:.2f} sec") However, the atomic seems not to be supported yet. Do you have any suggestions for using tasks for this problem? — Reply to this email directly, view it on GitHub <#33>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AATVME5J4AFT2EDT44HQ6OD36R2BZAVCNFSM6AAAAACNFBXUO2VHI2DSMVQWIX3LMV43ASLTON2WKOZTGY3DGNJZGY2TQMA>. You are receiving this because you are subscribed to this thread.

[pojeda]

Hi,

I wanted to see PyOMP's capabilities for tasking, for teaching purposes. Atomic and taskloop are topics that, I think, can be very useful for our HPC users. I already have an example with the reduction operation; it works well. Thanks for your reply.

/Pedro

[tgmattso]

If you use a critical construct in place of your atomic directive, your program should work. As for taskloop, that construct is covered nicely by a for construct with the “schedule dynamic” clause. I may be telling you what you already know (since I know nothing of your background in parallel computing) but when teaching tasking, I like to focus on patterns that cannot be addressed through versions of the loop construct. The first is basically a version of what you did. A control structure executed by a single thread fills a task queue which the threads waiting at the implied barrier on the single execute. The point is to show students that a vast range of control structures beyond loops can be addressed thereby greatly expanding the range of algorithms that can be addressed with OpenMP The second is a parallel version of the divide and conquer pattern. I often show this with a program to do the numerical integration to estimate pi. I haven’t done so yet, but I need to create a DGEMM algorithm following Leiserson's work on cache oblivious algorithms. Those are by far the most commonly used patterns for task-level parallelism in OpenMP. —Tim

…

On Nov 25, 2025, at 10:38 PM, Pedro Ojeda ***@***.***> wrote: pojeda left a comment (Python-for-HPC/PyOMP#33) <#33 (comment)> Hi, I wanted to see PyOMP's capabilities for tasking, for teaching purposes. Atomic and taskloop are topics that, I think, can be very useful for our HPC users. I already have an example with the reduction operation; it works well. Thanks for your reply. /Pedro — Reply to this email directly, view it on GitHub <#33 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AATVME26XZ5GOSIOERRHW5D36VDF5AVCNFSM6AAAAACNFBXUO2VHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZTKNZZGUYTAMBQHE>. You are receiving this because you commented.

[pojeda]

Hi,

Thanks for your insightful comments. I am curious if the PyOMP project will evolve and include the same capabilities as OpenMP in the near future?

/Pedro

[ggeorgakoudis]

Hi @pojeda !

We plan to support atomic in the next release. The low-level machinery is there, we need to do some minor updates to our frontend. What other constructs are you using in your tutorial? We would love to support them through PyOMP.

Hi,

Thanks for your insightful comments. I am curious if the PyOMP project will evolve and include the same capabilities as OpenMP in the near future?

/Pedro

Yes, we plan to evolve PyOMP to support all (meaningful) features of OpenMP in Python, and we are also working in an OpenMP for Python specification within the OpenMP organization.