def omp_taskyield():
import omp
from time import sleep
NUM_TASKS = 25
count = 0
start_id = [0 for _ in range(NUM_TASKS)]
current_id = [0 for _ in range(NUM_TASKS)]
if 'omp parallel':
use_omp = omp.in_parallel()
if 'omp single':
for i in range(NUM_TASKS):
myi = i
if 'omp task firstprivate(myi) untied':
sleep(0.01)
start_id[myi] = omp.get_thread_num()
'omp taskyield'
if start_id[myi] % 2 == 0:
sleep(0.01)
current_id[myi] = omp.get_thread_num()
for i in range(NUM_TASKS):
if current_id[i] == start_id[i]:
count += 1
return count < NUM_TASKS or not use_omp
def omp_in_parallel():
import omp
serial = 1
isparallel = 0
serial = omp.in_parallel()
if 'omp parallel':
if 'omp single':
isparallel = omp.in_parallel()
if 'omp parallel':
if 'omp single':
pass
return not serial and isparallel
def omp_sections_nowait():
import omp
from time import sleep
result = False
count = 0
if 'omp parallel':
in_parallel = omp.in_parallel()
rank = omp.get_thread_num()
if 'omp sections nowait':
if 'omp section':
sleep(0.01)
count = 1
'omp flush(count)'
if 'omp section':
pass
if 'omp sections':
if 'omp section':
pass
if 'omp section':
if count == 0:
result = True
return result or not in_parallel
def omp_in_parallel():
import omp
serial = 1
isparallel = 0
serial = omp.in_parallel()
if 'omp parallel num_threads(2)':
num_threads = omp.get_num_threads()
if 'omp single':
isparallel = omp.in_parallel()
if 'omp parallel':
if 'omp single':
pass
return (not serial and isparallel) or num_threads == 1
def omp_in_parallel():
import omp
serial = 1
isparallel = 0
serial = omp.in_parallel()
if 'omp parallel num_threads(2)':
num_threads = omp.get_num_threads()
if 'omp single':
isparallel = omp.in_parallel()
if 'omp parallel':
if 'omp single':
pass
return bool(not serial and isparallel) or num_threads == 1
def omp_nested():
import omp
counter = 0
omp.set_nested(1)
if 'omp parallel shared(counter) num_threads(4)':
use_omp = omp.in_parallel()
if 'omp critical':
counter += 1
if 'omp parallel num_threads(4)':
if 'omp critical':
counter -= 1
return counter != 0 or not use_omp
def omp_get_wtime():
import omp
from time import sleep
wait_time = 2
#omp parallel
in_parallel = omp.in_parallel()
start = omp.get_wtime()
sleep(wait_time)
end = omp.get_wtime()
measured_time = end - start
print(measured_time, wait_time)
return (measured_time > 0.9 * wait_time and measured_time < 1.1 * wait_time) or not in_parallel
def omp_flush():
result1 = 0
result2 = 0
if 'omp parallel':
use_omp = omp.in_parallel()
rank = omp.get_thread_num()
'omp barrier'
if rank == 1:
result2 = 3
'omp flush (result2)'
dummy = result2
if rank == 0:
sleep(0.5)
'omp flush(result2)'
result1 = result2
return not use_omp or (result1 == result2 and result2 == dummy and result2 == 3)
def omp_task():
import omp
from time import sleep
NUM_TASKS = 25
tids = range(NUM_TASKS)
if 'omp parallel':
use_parallel = omp.in_parallel()
for i in range(NUM_TASKS):
myi = i
if 'omp task':
sleep(0.01)
tids[myi] = omp.get_thread_num()
for i in range(NUM_TASKS):
if tids[0] != tids[i]:
return True
return not use_parallel
def omp_barrier():
import omp
from time import sleep
result1 = 0
result2 = 0
#omp parallel num_threads(4)
if 1:
use_omp = omp.in_parallel()
rank = omp.get_thread_num()
if rank == 1:
sleep(0.5)
result2 = 3
#omp barrier
if rank == 2:
result1 = result2
if use_omp:
return result1 == 3
else:
return result1 == 0
def omp_task_imp_firstprivate():
i = 5
k = 0
result = False
NUM_TASKS = 25
task_result = True
if 'omp parallel firstprivate(i)':
in_parallel = omp.in_parallel()
if 'omp single':
for k in range(NUM_TASKS):
if 'omp task shared(result, task_result)':
if i != 5:
task_result = False
for j in range(0, NUM_TASKS):
i += 1
'omp taskwait'
result = task_result and i == 5
return result or not in_parallel
def omp_task_imp_firstprivate():
i = 5
k = 0
result = 0
NUM_TASKS = 25
task_result = 1
if 'omp parallel firstprivate(i)':
in_parallel = omp.in_parallel()
if 'omp single':
for k in range(NUM_TASKS):
if 'omp task shared(result, task_result)':
if i != 5:
task_result = 0
for j in range(0, NUM_TASKS):
i += 1
'omp taskwait'
result = task_result and i == 5
return result or not in_parallel
def omp_for_nowait():
LOOPCOUNT = 1000
myarray = [0]*LOOPCOUNT
result = 0
count = 0
import omp
if 'omp parallel num_threads(4)':
use_omp = omp.in_parallel()
rank = omp.get_thread_num()
'omp for nowait'
for i in range(LOOPCOUNT):
if i == 0:
while i < LOOPCOUNT**2: i+=1
count = 1
'omp flush(count)'
for i in range(LOOPCOUNT):
'omp flush(count)'
if count ==0:
result = 1
return result == 1 or not use_omp
def omp_task_firstprivate():
sum = 1234
result = 0
LOOPCOUNT = 1000
NUM_TASKS = 25
known_sum = 1234 + (LOOPCOUNT * (LOOPCOUNT + 1)) / 2
if 'omp parallel':
in_parallel = omp.in_parallel()
if 'omp single':
for i in range(NUM_TASKS):
if 'omp task firstprivate(sum)':
for j in range(LOOPCOUNT + 1):
'omp flush'
sum += j
if sum != known_sum:
if 'omp critical':
result += 1
return result == 0 or not in_parallel
def omp_single_private():
nr_threads_in_single = 0
nr_iterations = 0
result = 0
LOOPCOUNT = 1000
if 'omp parallel':
use_parallel = omp.in_parallel()
myresult = 0
myit = 0
for i in range(LOOPCOUNT):
if 'omp single private(nr_threads_in_single)':
nr_threads_in_single = 0
'omp flush'
nr_threads_in_single += 1
'omp flush'
myit += 1
myresult += nr_threads_in_single
if 'omp critical':
result += nr_threads_in_single
nr_iterations += myit
return not use_parallel or (result == 0 and nr_iterations == LOOPCOUNT)
def omp_single_private():
nr_threads_in_single = 0
nr_iterations = 0
result = 0
LOOPCOUNT = 1000
if 'omp parallel':
use_parallel = omp.in_parallel()
myresult = 0
myit = 0
for i in range(LOOPCOUNT):
if 'omp single private(nr_threads_in_single)':
nr_threads_in_single = 0
'omp flush'
nr_threads_in_single += 1
'omp flush'
myit += 1
myresult += nr_threads_in_single
if 'omp critical':
result += nr_threads_in_single
nr_iterations += myit
return not use_parallel or (result == 0 and nr_iterations == LOOPCOUNT)
def omp_for_schedule_guided():
tids = range(1001)
maxiter = 0
result = True
notout = True
if 'omp parallel num_threads(4)':
in_parallel = omp.in_parallel()
if 'omp single':
threads = omp.get_num_threads()
if threads < 2:
print "This test only works with at least two threads"
result = False
if 'omp parallel shared(tids, maxiter) num_threads(4)':
tid = omp.get_num_threads()
'omp for nowait schedule(guided)'
for j in range(1000):
count = 0
'omp flush(maxiter)'
if j > maxiter:
if 'omp critical':
maxiter = j
'omp flush(notout, maxiter)'
while notout and count < 0.0005 and maxiter == j:
'omp flush(notout, maxiter)'
sleep(0.0001)
count += 0.0001
tids[j] = tid
notout = False
'omp flush(maxiter, notout)'
last_threadnr = tids[0]
global_chunknr = 0
local_chunknr = [0 for i in range(10)]
openwork = 1000
tids[1000] = -1
for i in range(1, 1001):
if last_threadnr == tids[i]:
pass
else:
global_chunknr += 1
local_chunknr[last_threadnr] += 1
last_threadnr = tids[i]
chuncksize = range(global_chunknr)
global_chunknr = 0
determined_chunksize = 1
last_threadnr = tids[0]
for i in range(1, 1001):
if last_threadnr == tids[i]:
determined_chunksize += 1
else:
chuncksize[global_chunknr] = determined_chunksize
global_chunknr += 1
local_chunknr[last_threadnr] += 1
last_threadnr = tids[i]
determined_chunksize = 1
expected_chunk_size = openwork / threads
c = chuncksize[0] / expected_chunk_size
for i in range(global_chunknr):
if expected_chunk_size > 1:
expected_chunk_size = c * openwork / threads
if abs(chuncksize[i] - expected_chunk_size) >= 2:
result = False
openwork -= chuncksize[i]
return result or not in_parallel
def omp_for_schedule_guided():
tids = range(1001)
maxiter = 0
result = True
notout = True
if 'omp parallel num_threads(4)':
in_parallel = omp.in_parallel()
if 'omp single':
threads = omp.get_num_threads()
if threads<2:
print("This test only works with at least two threads")
result = False
if 'omp parallel shared(tids, maxiter) num_threads(4)':
tid = omp.get_num_threads()
'omp for nowait schedule(guided)'
for j in range(1000):
count = 0
'omp flush(maxiter)'
if j > maxiter:
if 'omp critical':
maxiter = j
'omp flush(notout, maxiter)'
while notout and count < 0.0005 and maxiter == j:
'omp flush(notout, maxiter)'
sleep(0.0001)
count += 0.0001
tids[j] = tid
notout = False
'omp flush(maxiter, notout)'
last_threadnr = tids[0]
global_chunknr = 0
local_chunknr = [0 for i in range(10)]
openwork = 1000;
tids[1000] = -1
for i in range(1,1001):
if last_threadnr == tids[i]:
pass
else:
global_chunknr += 1
local_chunknr[last_threadnr] += 1
last_threadnr = tids[i]
chuncksize = range(global_chunknr)
global_chunknr = 0
determined_chunksize = 1
last_threadnr = tids[0]
for i in range(1,1001):
if last_threadnr == tids[i]:
determined_chunksize += 1
else:
chuncksize[global_chunknr] = determined_chunksize
global_chunknr += 1
local_chunknr[last_threadnr] += 1
last_threadnr = tids[i]
determined_chunksize = 1
expected_chunk_size = openwork / threads
c = chuncksize[0] / expected_chunk_size
for i in range(global_chunknr):
if expected_chunk_size > 1:
expected_chunk_size = c * openwork / threads
if abs(chuncksize[i] - expected_chunk_size) >= 2:
result = False
openwork -= chuncksize[i]
return result or not in_parallel
