def setup_opencl(data, cube_size):
import pycl
blocking = True
with timeify("Making context, loading kernel"):
devices = pycl.clGetDeviceIDs()
ctx = pycl.clCreateContext(devices=devices)
queue = pycl.clCreateCommandQueue(ctx)
program = pycl.clCreateProgramWithSource(ctx, SOURCE).build()
score_matrix = program['score_matrix_to_rms']
score_matrix.argtypes = (pycl.cl_mem, pycl.cl_mem, pycl.cl_mem,
pycl.cl_mem, pycl.cl_mem, pycl.cl_int,
pycl.cl_int)
sub_divisions = cube_size**3
with timeify("Creating buffers"):
in_r_buf, in_evt1 = pycl.buffer_from_pyarray(queue,
data['in_r'],
blocking=blocking)
in_g_buf, in_evt2 = pycl.buffer_from_pyarray(queue,
data['in_g'],
blocking=blocking)
in_b_buf, in_evt3 = pycl.buffer_from_pyarray(queue,
data['in_b'],
blocking=blocking)
out_r = data['out_r']
out_r_buf, in_evt4 = pycl.buffer_from_pyarray(queue,
out_r,
blocking=blocking)
score = array.array('f', [0 for x in range(sub_divisions)])
score_buf, in_evt5 = pycl.buffer_from_pyarray(queue,
score,
blocking=blocking)
with timeify("Run kernel r"):
run_evt = score_matrix(
#in_r_buf, in_g_buf, in_b_buf, out_r_buf, score_buf,
in_r_buf,
in_g_buf,
in_b_buf,
in_r_buf,
score_buf,
len(data['in_r']),
cube_size,
wait_for=[in_evt1, in_evt2, in_evt3, in_evt4,
in_evt5]).on(queue, sub_divisions)
with timeify("Retrive data"):
score_from_gpu, evt = pycl.buffer_to_pyarray(queue,
score_buf,
wait_for=run_evt,
like=score)
return score_from_gpu
def run_conv( prog, queue ):
func = prog['conv__num_imgs_20__in_pad_3__in_dim_0_227__in_dim_1_227__conv_has_relu_1__kern_sz_7__stride_2__out_chans_64__in_chans_3']
print func
func.argtypes = (cl.cl_mem, cl.cl_mem, cl.cl_mem, cl.cl_mem)
in_ar = cl.array('f', range(100 * 1000 * 1000))
in_buf, in_evt = cl.buffer_from_pyarray(queue, in_ar, blocking=False)
filt_buf = in_buf.empty_like_this()
bias_buf = in_buf.empty_like_this()
out_buf = in_buf.empty_like_this()
#run_evt = func(filt_buf, bias_buf, in_buf, out_buf).on(queue, gsize=(63,), lsize=(32,), wait_for=in_evt)
func.setarg( 0, filt_buf )
func.setarg( 1, bias_buf )
func.setarg( 2, in_buf )
func.setarg( 3, out_buf )
run_evt = cl.clEnqueueNDRangeKernel( queue, func, gsize=(120*2166,), lsize=(120,), wait_for=in_evt)
out, evt = cl.buffer_to_pyarray(queue, out_buf, wait_for=run_evt, like=in_ar)
print "start wait"
evt.wait()
print "end wait, start loop"
for i in range(1000):
run_evt = cl.clEnqueueNDRangeKernel( queue, func, gsize=(120*2166,), lsize=(120,) )
run_evt.wait()
print "end loop"
print out[0:10]
def run_conv(prog, queue):
func = prog[
"conv__num_imgs_20__in_pad_3__in_dim_0_227__in_dim_1_227__conv_has_relu_1__kern_sz_7__stride_2__out_chans_64__in_chans_3"
]
print func
func.argtypes = (cl.cl_mem, cl.cl_mem, cl.cl_mem, cl.cl_mem)
in_ar = cl.array("f", range(100 * 1000 * 1000))
in_buf, in_evt = cl.buffer_from_pyarray(queue, in_ar, blocking=False)
filt_buf = in_buf.empty_like_this()
bias_buf = in_buf.empty_like_this()
out_buf = in_buf.empty_like_this()
# run_evt = func(filt_buf, bias_buf, in_buf, out_buf).on(queue, gsize=(63,), lsize=(32,), wait_for=in_evt)
func.setarg(0, filt_buf)
func.setarg(1, bias_buf)
func.setarg(2, in_buf)
func.setarg(3, out_buf)
run_evt = cl.clEnqueueNDRangeKernel(queue, func, gsize=(120 * 2166,), lsize=(120,), wait_for=in_evt)
out, evt = cl.buffer_to_pyarray(queue, out_buf, wait_for=run_evt, like=in_ar)
print "start wait"
evt.wait()
print "end wait, start loop"
for i in range(1000):
run_evt = cl.clEnqueueNDRangeKernel(queue, func, gsize=(120 * 2166,), lsize=(120,))
run_evt.wait()
print "end loop"
print out[0:10]
def setup_opencl(data, cube_size):
import pycl
blocking = True
with timeify("Making context, loading kernel"):
devices = pycl.clGetDeviceIDs()
ctx = pycl.clCreateContext(devices = devices)
queue = pycl.clCreateCommandQueue(ctx)
program = pycl.clCreateProgramWithSource(ctx, SOURCE).build()
score_matrix = program['score_matrix_to_rms']
score_matrix.argtypes = (pycl.cl_mem, pycl.cl_mem, pycl.cl_mem,
pycl.cl_mem, pycl.cl_mem, pycl.cl_int, pycl.cl_int)
sub_divisions = cube_size**3
with timeify("Creating buffers"):
in_r_buf, in_evt1 = pycl.buffer_from_pyarray(queue, data['in_r'], blocking = blocking)
in_g_buf, in_evt2 = pycl.buffer_from_pyarray(queue, data['in_g'], blocking = blocking)
in_b_buf, in_evt3 = pycl.buffer_from_pyarray(queue, data['in_b'], blocking = blocking)
out_r = data['out_r']
out_r_buf, in_evt4 = pycl.buffer_from_pyarray(queue, out_r, blocking = blocking)
score = array.array('f', [0 for x in range(sub_divisions)])
score_buf, in_evt5 = pycl.buffer_from_pyarray(queue, score, blocking = blocking)
with timeify("Run kernel r"):
run_evt = score_matrix(
#in_r_buf, in_g_buf, in_b_buf, out_r_buf, score_buf,
in_r_buf, in_g_buf, in_b_buf, in_r_buf, score_buf,
len(data['in_r']), cube_size,
wait_for = [in_evt1, in_evt2, in_evt3, in_evt4, in_evt5]).on(queue,
sub_divisions)
with timeify("Retrive data"):
score_from_gpu, evt = pycl.buffer_to_pyarray(queue, score_buf,
wait_for=run_evt,
like=score)
return score_from_gpu
def run_mxplusb( prog, queue ):
func = prog['mxplusb']
print func
func.argtypes = (cl.cl_float, cl.cl_mem, cl.cl_float, cl.cl_mem)
x = cl.array('f', range(100))
x_buf, in_evt = cl.buffer_from_pyarray(queue, x, blocking=False)
y_buf = x_buf.empty_like_this()
run_evt = func(2, x_buf, 5, y_buf).on(queue, len(x), wait_for=in_evt)
y, evt = cl.buffer_to_pyarray(queue, y_buf, wait_for=run_evt, like=x)
evt.wait()
print y[0:10]
def run_mxplusb(prog, queue):
func = prog["mxplusb"]
print func
func.argtypes = (cl.cl_float, cl.cl_mem, cl.cl_float, cl.cl_mem)
x = cl.array("f", range(100))
x_buf, in_evt = cl.buffer_from_pyarray(queue, x, blocking=False)
y_buf = x_buf.empty_like_this()
run_evt = func(2, x_buf, 5, y_buf).on(queue, len(x), wait_for=in_evt)
y, evt = cl.buffer_to_pyarray(queue, y_buf, wait_for=run_evt, like=x)
evt.wait()
print y[0:10]