def LoadDataRef(db,table) :
db = MySQLdb.connect("localhost", "root", "", db)
cursor = db.cursor()
sql = "SELECT idstr from %s"%table
idString = []
try:
cursor.execute(sql)
results = cursor.fetchall()
for row in results :
list_ref.append(float64(row[0]))
except :
print "Error"
db.close
'''
Demonstrate the vectorize API with automatical memory transfer and
manual memory transfer.
'''
from timeit import default_timer as timer
import numpy
from numbapro import vectorize, float64, cuda
@vectorize([float64(float64, float64)], target='gpu')
def vector_mul(a, b):
return a * b
a = numpy.random.rand(10000000)
b = numpy.random.rand(10000000)
# Let NumbaPro automatically convert host memory to device memory
ts = timer()
for i in xrange(10):
result = vector_mul(a, b)
te = timer()
print 'auto', te - ts
# Manual conversion between host and device memory
ts = timer()
for i in xrange(10):
# copy host memory to device
da = cuda.to_device(a)
db = cuda.to_device(b)
'''
Demonstrate the vectorize API with automatical memory transfer and
manual memory transfer.
'''
from timeit import default_timer as timer
import numpy
from numbapro import vectorize, float64, cuda
@vectorize([float64(float64, float64)], target='gpu')
def vector_mul(a, b):
return a * b
a = numpy.random.rand(10000000)
b = numpy.random.rand(10000000)
# Let NumbaPro automatically convert host memory to device memory
ts = timer()
for i in xrange(10):
result = vector_mul(a, b)
te = timer()
print 'auto', te - ts
# Manual conversion between host and device memory
ts = timer()
for i in xrange(10):
# copy host memory to device
da = cuda.to_device(a)
db = cuda.to_device(b)
# execute kernel
