def main_ufunc():
ctx = cl.Context(device_type=cl.Device.GPU)
size = 10
a = ca.arange(ctx, size, ctype=c_float)
b = ca.arange(ctx, size, ctype=c_float).reshape([size, 1])
o1 = add(a, b)
with o1.map() as view:
print view
with a.map() as view:
print np.sum(view)
result = add.reduce(a)
result.queue.finish()
with a.map() as view:
print view
print view.sum()
print result.item()
def main_ufunc():
ctx = cl.Context(device_type=cl.Device.GPU)
size = 10
a = ca.arange(ctx, size, ctype=c_float)
b = ca.arange(ctx, size, ctype=c_float).reshape([size, 1])
o1 = add(a, b)
with o1.map() as view:
print view
with a.map() as view:
print np.sum(view)
result = add.reduce(a)
result.queue.finish()
with a.map() as view:
print view
print view.sum()
print result.item()
def main_reduce():
ctx = cl.Context(device_type=cl.Device.GPU)
sum = add.reduce
# for size in range(250, 258):
size = 1027
a = ca.arange(ctx, size, ctype=cl.cl_int)
result = sum(a)
with a.map() as view:
print size, view.sum(), result.item()
def main_reduce():
ctx = cl.Context(device_type=cl.Device.GPU)
sum = add.reduce
# for size in range(250, 258):
size = 1027
a = ca.arange(ctx, size, ctype=cl.cl_int)
result = sum(a)
with a.map() as view:
print size, view.sum(), result.item()
def main():
ctx = cl.Context(device_type=cl.Device.GPU)
queue = cl.Queue(ctx)
npa = np.arange(1.0 * 12.0, dtype=c_float)
a = ca.arange(ctx, 12, ctype=c_float)
out = ca.empty_like(a[:])
output = cl.broadcast(out, a[:].shape)
ca.blitz(queue, lambda: a[:] + a[:] + 1, out=output)
print npa[1:] + npa[:-1]
with out.map() as view:
print view
def main():
ctx = cl.Context(device_type=cl.Device.GPU)
queue = cl.Queue(ctx)
npa = np.arange(1.0 * 12.0, dtype=c_float)
a = ca.arange(ctx, 12, ctype=c_float)
out = ca.empty_like(a[:])
output = cl.broadcast(out, a[:].shape)
ca.blitz(queue, lambda: a[:] + a[:] + 1, out=output)
print npa[1:] + npa[:-1]
with out.map() as view:
print view
@author: sean
'''
import opencl as cl
import clyther as cly
import clyther.array as ca
from ctypes import c_float
import numpy as np
#Always have to create a context.
ctx = cl.Context()
#Create an array
a = ca.arange(ctx, 12)
# Multiply is not defined
try:
c = a * 2
except TypeError as err:
print 'Expected:', err
@ca.binary_ufunc
def multuply(x, y):
return x * y
c = multuply(a, 2)
with c.map() as arr:
Created on Dec 15, 2011
@author: sean
'''
import opencl as cl
import clyther as cly
import clyther.array as ca
from ctypes import c_float
import numpy as np
#Always have to create a context.
ctx = cl.Context()
#Create an array
a = ca.arange(ctx, 12)
# Multiply is not defined
try:
c = a * 2
except TypeError as err:
print 'Expected:', err
@ca.binary_ufunc
def multuply(x, y):
return x * y
c = multuply(a, 2)
