Python/OpenCL: Difference between revisions
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= pyopencl = | = pyopencl = | ||
== To increase an array of float32. == | == To increase an array of float32. == | ||
| Line 62: | Line 38: | ||
print(in_np) | print(in_np) | ||
print(out_np) | print(out_np) | ||
</source> | |||
= numpy = | |||
* https://docs.scipy.org/doc/numpy/user/quickstart.html | |||
== Data Types == | |||
<source lang="python3"> | |||
import numpy as np | |||
# Default int type is int64 | |||
a = np.array([1, 2, 3]) | |||
print(type(a[0]).__name__) | |||
# Default float type is float64 | |||
b = np.array([1.0, 2.0, 3.0]) | |||
print(type(b[0]).__name__) | |||
# Force int32 | |||
c = np.array([1, 2, 3]).astype(np.int32) | |||
print(type(c[0]).__name__) | |||
# Force float32 | |||
d = np.array([1.0, 2.0, 3.0]).astype(np.float32) | |||
print(type(d[0]).__name__) | |||
</source> | </source> | ||
Revision as of 07:37, 6 June 2018
pyopencl
To increase an array of float32.
import os
import numpy as np
import pyopencl as cl
# Kernel code.
CL_INC_F32 = '''
__kernel void inc_f32(__global const float *a_g, __global float *res_g)
{
int gid = get_global_id(0);
res_g[gid] = a_g[gid] + 1;
}
'''
# Context and Queue
os.environ['PYOPENCL_CTX'] = '0:1' # Apple > HD Graphics 4000 of Macbook Air 2012 mid
ctx = cl.create_some_context()
queue = cl.CommandQueue(ctx)
# Memory allocation.
mf = cl.mem_flags
in_np = np.array([1, 2, 3, 4, 5]).astype(np.float32)
in_cl = cl.Buffer(ctx, mf.READ_ONLY | mf.COPY_HOST_PTR, hostbuf=in_np)
out_cl = cl.Buffer(ctx, mf.WRITE_ONLY, in_np.nbytes)
out_np = np.empty_like(in_np)
# Build OpenCL kernel code.
prg = cl.Program(ctx, CL_INC_F32).build()
# Run kernel.
prg.inc_f32(queue, in_np.shape, None, in_cl, out_cl)
# Convert results into numpy format.
cl.enqueue_copy(queue, out_np, out_cl)
print(in_np)
print(out_np)
numpy
Data Types
import numpy as np
# Default int type is int64
a = np.array([1, 2, 3])
print(type(a[0]).__name__)
# Default float type is float64
b = np.array([1.0, 2.0, 3.0])
print(type(b[0]).__name__)
# Force int32
c = np.array([1, 2, 3]).astype(np.int32)
print(type(c[0]).__name__)
# Force float32
d = np.array([1.0, 2.0, 3.0]).astype(np.float32)
print(type(d[0]).__name__)