#!/usr/bin/python
import shmobj, os, sys, time
from shmobj import SHMLST, SHMDBL
shmobj.add_shmem_pages(32000)
print shmobj.freecount()
MAX_PROC = 20
DIM = 10
a = []
b = []
c = SHMLST([])
def mm(rank, numprocs):
i = rank
while i < DIM:
for j in range(DIM):
sum = 0.0
for k in range(DIM):
sum = sum + (a[i][k] * b[k][j])
c[i][j].set(sum)
i = i + numprocs
def checkmatrix():
errs = 0
for i in range(DIM):
for j in range(DIM):
e = 0.0
# by the garbage collector, because some other process may have references to it. # Instead, we are explicitly in charge of deleting shared memory variables # by using delete calls. shared_int = SHMINT(10) shared_int.delete() # So we must define different semantics for local references pointing to # deleted shared memory variables, since when we delete shared_int, the variable # is truly gone. In this case, a reference (stored in shared memory) stored # in a list (also in shared memory) still exists, but now returns the Python # None object. # So the following print statement should show shared_list = [None] shared_int = SHMINT(10) shared_list = SHMLST([shared_int]) shared_int.delete() print 'shared_list = %s' % shared_list # Note that this also means that shared_list still has a length of 1. print 'len(shared_list) = %d' % len(shared_list) # We must still delete the reference from the list to free the shared # memory used for it. We do this with the delitem call, which we must # explicitly call for the same reasons as stated above for an explicit delete. # The delitem call also takes a 'shallow' parameter that acts just as the # one in delete. By default it is not shallow, and will truly delete the item. shared_list.delitem(0) # For lists and dictionaries, the delete call takes an optional named # parameter 'shallow' that will only delete the list and it's references,
#!/usr/bin/python
import shmobj, os, sys, time
from shmobj import SHMLST, SHMDBL
shmobj.add_shmem_pages(32000)
print shmobj.freecount()
MAX_PROC = 20
DIM = 10
a = SHMLST([])
b = SHMLST([])
c = SHMLST([])
def mm(rank, numprocs):
i = rank
while i < DIM:
for j in range(DIM):
sum = 0.0
for k in range(DIM):
sum = sum + (a[i][k] * b[k][j])
c[i][j].set(sum)
i = i + numprocs
def checkmatrix():
errs = 0
for i in range(DIM):
for j in range(DIM):
e = 0.0
