def vglClNdCopy(self, img_input, img_output):
print("# Running vglClNdCopy")
if (not img_input.clForceAsBuf == vl.IMAGE_ND_ARRAY()):
print(
"vglClNdCopy: Error: this function supports only OpenCL data as buffer and img_input isn't."
)
exit()
if (not img_output.clForceAsBuf == vl.IMAGE_ND_ARRAY()):
print(
"vglClNdCopy: Error: this function supports only OpenCL data as buffer and img_output isn't."
)
exit()
vl.vglCheckContext(img_input, vl.VGL_CL_CONTEXT())
vl.vglCheckContext(img_output, vl.VGL_CL_CONTEXT())
_program = self.cl_ctx.get_compiled_kernel("../CL_ND/vglClNdCopy.cl",
"vglClNdCopy")
kernel_run = _program.vglClNdCopy
kernel_run.set_arg(0, img_input.get_oclPtr())
kernel_run.set_arg(1, img_output.get_oclPtr())
cl.enqueue_nd_range_kernel(self.ocl.commandQueue, kernel_run,
img_output.get_ipl().shape, None)
vl.vglSetContext(img_output, vl.VGL_CL_CONTEXT())
def max_length_real4(ipt):
out = CLReal(len(ipt))
kern = _lengthkern_real4.kern
kern.set_arg(0, ipt._buffer)
kern.set_arg(1, out._buffer)
cl.enqueue_nd_range_kernel(ipt._ctrl.clqueue, kern, (len(ipt),), None)
return max_reduce(out)
def search(self, midstate):
msg = flipendian32(midstate)
for i in xrange(8):
self.sha512_fill.set_arg(i, msg[i * 4:i * 4 + 4])
self.sha512_fill.set_arg(8, self.hashes_buf)
self.sha512_fill.set_arg(9, self.keyhash_buf)
# t1 = time.time()
cl.enqueue_nd_range_kernel(self.queue, self.sha512_fill,
(HASHES_NUM, ), (self.sha512_fill_ws, ))
self.queue.finish()
# print "fill %f" % (time.time() - t1)
output = bytearray(OUTPUT_SIZE)
cl.enqueue_write_buffer(self.queue, self.output_buf, output)
self.queue.finish()
self.ksearch.set_arg(0, self.hashes_buf)
self.ksearch.set_arg(1, self.keyhash_buf)
self.ksearch.set_arg(2, self.output_buf)
cl.enqueue_nd_range_kernel(self.queue, self.ksearch, (KEYS_NUM, ),
(self.ksearch_ws, ))
self.queue.finish()
cl.enqueue_read_buffer(self.queue, self.output_buf, output)
self.queue.finish()
return str(output)
def vglClBinConway(self, img_input, img_output):
vl.vglCheckContext(img_input, vl.VGL_CL_CONTEXT())
vl.vglCheckContext(img_output, vl.VGL_CL_CONTEXT())
_program = self.cl_ctx.get_compiled_kernel(
"../CL_BIN/vglClBinConway.cl", "vglClBinConway")
kernel_run = _program.vglClBinConway
mobj_img_shape = img_input.getVglShape().get_asVglClShape_buffer()
kernel_run.set_arg(0, img_input.get_oclPtr())
kernel_run.set_arg(1, img_output.get_oclPtr())
kernel_run.set_arg(2, mobj_img_shape)
_worksize_0 = img_input.getWidthIn()
if (img_input.depth == vl.IPL_DEPTH_1U()):
_worksize_0 = img_input.getWidthStep()
if (img_output.depth == vl.IPL_DEPTH_1U()):
_worksize_0 = img_output.getWidthStep()
worksize = (int(_worksize_0), img_input.getHeigthIn(),
img_input.getNFrames())
cl.enqueue_nd_range_kernel(self.ocl.commandQueue, kernel_run, worksize,
None)
#cl.enqueue_nd_range_kernel(self.ocl.commandQueue, kernel_run, img_output.get_oclPtr().shape, None)
vl.vglSetContext(img_output, vl.VGL_CL_CONTEXT())
def do_opencl_pow(hash, target):
global ctx, queue, program, gpus, hash_dt
output = numpy.zeros(1, dtype=[("v", numpy.uint64, 1)])
if ctx == False:
return output[0][0]
data = numpy.zeros(1, dtype=hash_dt, order="C")
data[0]["v"] = ("0000000000000000" + hash).decode("hex")
data[0]["target"] = target
hash_buf = cl.Buffer(ctx, cl.mem_flags.READ_ONLY | cl.mem_flags.COPY_HOST_PTR, hostbuf=data)
dest_buf = cl.Buffer(ctx, cl.mem_flags.WRITE_ONLY, output.nbytes)
kernel = program.kernel_sha512
worksize = kernel.get_work_group_info(cl.kernel_work_group_info.WORK_GROUP_SIZE, gpus[0])
kernel.set_arg(0, hash_buf)
kernel.set_arg(1, dest_buf)
start = time.time()
progress = 0
globamt = worksize * 2000
while output[0][0] == 0:
kernel.set_arg(2, pack("<Q", progress))
cl.enqueue_nd_range_kernel(queue, kernel, (globamt,), (worksize,))
cl.enqueue_read_buffer(queue, dest_buf, output)
queue.finish()
progress += globamt
sofar = time.time() - start
# logger.debug("Working for %.3fs, %.2f Mh/s", sofar, (progress / sofar) / 1000000)
taken = time.time() - start
# logger.debug("Took %d tries.", progress)
return output[0][0]
def do_opencl_pow(hash, target):
output = numpy.zeros(1, dtype=[('v', numpy.uint64, 1)])
if (len(enabledGpus) == 0):
return output[0][0]
data = numpy.zeros(1, dtype=hash_dt, order='C')
data[0]['v'] = ("0000000000000000" + hash).decode("hex")
data[0]['target'] = target
hash_buf = cl.Buffer(ctx, cl.mem_flags.READ_ONLY | cl.mem_flags.COPY_HOST_PTR, hostbuf=data)
dest_buf = cl.Buffer(ctx, cl.mem_flags.WRITE_ONLY, output.nbytes)
kernel = program.kernel_sha512
worksize = kernel.get_work_group_info(cl.kernel_work_group_info.WORK_GROUP_SIZE, enabledGpus[0])
kernel.set_arg(0, hash_buf)
kernel.set_arg(1, dest_buf)
start = time.time()
progress = 0
globamt = worksize*2000
while output[0][0] == 0 and shutdown == 0:
kernel.set_arg(2, pack("<Q", progress))
cl.enqueue_nd_range_kernel(queue, kernel, (globamt,), (worksize,))
cl.enqueue_read_buffer(queue, dest_buf, output)
queue.finish()
progress += globamt
sofar = time.time() - start
# logger.debug("Working for %.3fs, %.2f Mh/s", sofar, (progress / sofar) / 1000000)
if shutdown != 0:
raise Exception ("Interrupted")
taken = time.time() - start
# logger.debug("Took %d tries.", progress)
return output[0][0]
def vglClNdCopy(img_input, img_output):
if (not img_input.clForceAsBuf == vl.IMAGE_ND_ARRAY()):
print(
"vglClNdCopy: Error: this function supports only OpenCL data as buffer and img_input isn't."
)
exit(1)
if (not img_output.clForceAsBuf == vl.IMAGE_ND_ARRAY()):
print(
"vglClNdCopy: Error: this function supports only OpenCL data as buffer and img_output isn't."
)
exit(1)
vl.vglCheckContext(img_input, vl.VGL_CL_CONTEXT())
vl.vglCheckContext(img_output, vl.VGL_CL_CONTEXT())
_program = vl.get_ocl_context().get_compiled_kernel(
"CL_ND/vglClNdCopy.cl", "vglClNdCopy")
_kernel = _program.vglClNdCopy
_kernel.set_arg(0, img_input.get_oclPtr())
_kernel.set_arg(1, img_output.get_oclPtr())
# THIS IS A BLOCKING COMMAND. IT EXECUTES THE KERNEL.
cl.enqueue_nd_range_kernel(vl.get_ocl().commandQueue, _kernel,
img_input.get_ipl().shape, None)
vl.vglSetContext(img_input, vl.VGL_CL_CONTEXT())
vl.vglSetContext(img_output, vl.VGL_CL_CONTEXT())
def do_opencl_pow(hash_, target):
"""Perform PoW using OpenCL"""
output = numpy.zeros(1, dtype=[('v', numpy.uint64, 1)])
if not enabledGpus:
return output[0][0]
data = numpy.zeros(1, dtype=hash_dt, order='C')
data[0]['v'] = ("0000000000000000" + hash_).decode("hex")
data[0]['target'] = target
hash_buf = cl.Buffer(ctx, cl.mem_flags.READ_ONLY | cl.mem_flags.COPY_HOST_PTR, hostbuf=data)
dest_buf = cl.Buffer(ctx, cl.mem_flags.WRITE_ONLY, output.nbytes)
kernel = program.kernel_sha512
worksize = kernel.get_work_group_info(cl.kernel_work_group_info.WORK_GROUP_SIZE, enabledGpus[0])
kernel.set_arg(0, hash_buf)
kernel.set_arg(1, dest_buf)
progress = 0
globamt = worksize * 2000
while output[0][0] == 0 and shutdown == 0:
kernel.set_arg(2, pack("<Q", progress))
cl.enqueue_nd_range_kernel(queue, kernel, (globamt,), (worksize,))
try:
cl.enqueue_read_buffer(queue, dest_buf, output)
except AttributeError:
cl.enqueue_copy(queue, output, dest_buf)
queue.finish()
progress += globamt
if shutdown != 0:
raise Exception("Interrupted")
# logger.debug("Took %d tries.", progress)
return output[0][0]
def runFilter(self):
if self.atts.height == 1 and self.atts.slices == 1:
mid = 1
elif self.atts.slices == 1:
mid = 4
else:
mid = 13
globalSize = [0, 0]
localSize = [0, 0]
self.clattr.computeWorkingGroupSize(
localSize, globalSize, [self.atts.width, self.atts.height, 1])
try:
# set up parameters
self.kernel.set_args(self.clattr.inputBuffer,
self.clattr.outputBuffer,
np.int32(self.atts.width),
np.int32(self.atts.height),
np.int32(self.clattr.maxSliceCount),
np.int32(mid))
# execute kernel
cl.enqueue_nd_range_kernel(self.clattr.queue, self.kernel,
globalSize, localSize)
except Exception as e:
raise e
# write results
cl.enqueue_copy(self.clattr.queue, self.clattr.inputBuffer,
self.clattr.outputBuffer)
self.clattr.queue.finish()
return True
def runFilter(self):
globalSize = [0, 0]
localSize = [0, 0]
self.clattr.computeWorkingGroupSize(
localSize, globalSize, [self.atts.width, self.atts.height, 1])
try:
self.kernel.set_args(
self.clattr.inputBuffer, self.clattr.outputBuffer,
np.int32(self.atts.width), np.int32(self.atts.height),
np.int32(self.clattr.maxSliceCount + self.getInfo().overlapZ),
self.spatialKernel, np.int32((self.spatialRadius + 1) * 2 - 1),
self.rangeKernel, np.int32((self.rangeRadius + 1) * 2 - 1))
cl.enqueue_nd_range_kernel(self.clattr.queue, self.kernel,
globalSize, localSize)
except Exception as e:
raise e
# write results
cl.enqueue_copy(self.clattr.queue, self.clattr.inputBuffer,
self.clattr.outputBuffer)
self.clattr.queue.finish()
return True
def prefixSumUp(self, e, data, ndata, data2, ndata2, events):
import numpy as np
import pyopencl as cl
mf = cl.mem_flags
if not isinstance(data, cl.Buffer):
data_buf = cl.Buffer(self.ctx, mf.READ_WRITE | mf.COPY_HOST_PTR, hostbuf=data)
else:
data_buf = data
if not isinstance(data2, cl.Buffer):
data2_buf = cl.Buffer(self.ctx, mf.READ_WRITE | mf.COPY_HOST_PTR, hostbuf=data2)
else:
data2_buf = data2
kernel = self.prg.prefixSumUp
kernel.set_args(data_buf, np.uint64(ndata), data2_buf, np.uint64(ndata2))
global_dims = self.get_global(self.get_grid_dims(ndata))
print "prefixSumUp"
if e is None:
e = ( cl.enqueue_nd_range_kernel(self.queue, kernel, global_dims, self.localDims, wait_for=e), )
else:
e = ( cl.enqueue_nd_range_kernel(self.queue, kernel, global_dims, self.localDims), )
events += e
return (e, data_buf, data2_buf)
def compute(self, image, num_bins):
width, height = np.shape(image)
numpixels = width * height
image = np.reshape(image, (numpixels, )).astype(np.float32)
result = np.zeros((numpixels * num_bins, ), dtype=np.float32)
mf = cl.mem_flags
self.buf_image = cl.Buffer(self.context,
mf.READ_ONLY | mf.COPY_HOST_PTR,
hostbuf=image)
self.output_buf = cl.Buffer(self.context, mf.READ_WRITE, result.nbytes)
kernel = self.program.iif_binid
kernel.set_scalar_arg_dtypes([np.uintc, np.uintc, np.ubyte] +
[None] * 2)
kernel.set_arg(0, np.uintc(width))
kernel.set_arg(1, np.uintc(height))
kernel.set_arg(2, np.ubyte(num_bins))
kernel.set_arg(3, self.buf_image)
kernel.set_arg(4, self.output_buf)
cl.enqueue_nd_range_kernel(self.queue, kernel, image.shape,
None).wait()
cl.enqueue_read_buffer(self.queue, self.output_buf, result).wait()
return np.reshape(result, (width, height, num_bins)).astype(np.float32)
def compute(self, floatimage, histogram, k):
width, height, nbins = np.shape(histogram)
numpixels = width * height
image_linear = np.reshape(floatimage, (numpixels, )).astype(np.float32)
histogram_linear = np.reshape(
histogram, (np.size(histogram), )).astype(np.float32)
transform = np.zeros_like(image_linear).astype(np.float32)
mf = cl.mem_flags
self.buf_image = cl.Buffer(self.context,
mf.READ_ONLY | mf.COPY_HOST_PTR,
hostbuf=image_linear)
self.buf_histogram = cl.Buffer(self.context,
mf.READ_ONLY | mf.COPY_HOST_PTR,
hostbuf=histogram_linear)
self.output_buf = cl.Buffer(self.context, mf.READ_WRITE,
transform.nbytes)
kernel = self.program.IIF
kernel.set_scalar_arg_dtypes([np.uintc, np.uintc, np.float32] +
[None] * 3)
kernel.set_arg(0, np.uintc(width))
kernel.set_arg(1, np.uintc(height))
kernel.set_arg(2, np.float32(k))
kernel.set_arg(3, self.buf_image)
kernel.set_arg(4, self.buf_histogram)
kernel.set_arg(5, self.output_buf)
cl.enqueue_nd_range_kernel(self.queue, kernel, image_linear.shape,
None).wait()
cl.enqueue_read_buffer(self.queue, self.output_buf, transform).wait()
return np.reshape(transform, (width, height)).astype(np.float)
def __call__(self, thread_count, work_group_size, *args):
fun = self.compile()
for i, arg in enumerate(args):
fun.set_arg(i, arg)
with timed_region("ParLoop kernel"):
cl.enqueue_nd_range_kernel(_queue, fun, (thread_count,),
(work_group_size,), g_times_l=False).wait()
def applyMorphOp(imgIn, op):
"apply morphological operation to image using GPU"
# (1) setup OpenCL
platforms = cl.get_platforms() # a platform corresponds to a driver (e.g. AMD)
platform = platforms[0] # take first platform
devices = platform.get_devices(cl.device_type.GPU) # get GPU devices of selected platform
device = devices[0] # take first GPU
context = cl.Context([device]) # put selected GPU into context object
queue = cl.CommandQueue(context, device) # create command queue for selected GPU and context
# (2) get shape of input image, allocate memory for output to which result can be copied to
shape = imgIn.T.shape
imgOut = np.empty_like(imgIn)
# (2) create image buffers which hold images for OpenCL
imgInBuf = cl.Image(context, cl.mem_flags.READ_ONLY, cl.ImageFormat(cl.channel_order.LUMINANCE, cl.channel_type.UNORM_INT8), shape=shape) # holds a gray-valued image of given shape
imgOutBuf = cl.Image(context, cl.mem_flags.WRITE_ONLY, cl.ImageFormat(cl.channel_order.LUMINANCE, cl.channel_type.UNORM_INT8), shape=shape) # placeholder for gray-valued image of given shape
# (3) load and compile OpenCL program
program = cl.Program(context, open('Erosion_Dilation.cl').read()).build()
# (3) from OpenCL program, get kernel object and set arguments (input image, operation type, output image)
kernel = cl.Kernel(program, 'morphOpKernel') # name of function according to kernel.py
kernel.set_arg(0, imgInBuf) # input image buffer
kernel.set_arg(1, np.uint32(op)) # operation type passed as an integer value (dilate=0, erode=1)
kernel.set_arg(2, imgOutBuf) # output image buffer
# (4) copy image to device, execute kernel, copy data back
cl.enqueue_copy(queue, imgInBuf, imgIn, origin=(0, 0), region=shape, is_blocking=False) # copy image from CPU to GPU
cl.enqueue_nd_range_kernel(queue, kernel, shape, None) # execute kernel, work is distributed across shape[0]*shape[1] work-items (one work-item per pixel of the image)
cl.enqueue_copy(queue, imgOut, imgOutBuf, origin=(0, 0), region=shape, is_blocking=True) # wait until finished copying resulting image back from GPU to CPU
return imgOut
def filterPrepare(self, e, data, keys, ndata, events):
import numpy as np
import pyopencl as cl
mf = cl.mem_flags
ndata = data.size
if keys.size != ndata: raise Exception()
filtbytes = np.bool8(False).nbytes * ndata
if not isinstance(data, cl.Buffer):
data_buf = cl.Buffer(self.ctx, mf.READ_ONLY | mf.COPY_HOST_PTR, hostbuf= data)
else:
data_buf = data
if not isinstance(keys, cl.Buffer):
keys_buf = cl.Buffer(self.ctx, mf.READ_ONLY | mf.COPY_HOST_PTR, hostbuf= keys)
else:
keys_buf = keys
filt_buf = cl.Buffer(self.ctx, mf.READ_WRITE, filtbytes)
kernel = self.prg.filterPrepare
kernel.set_args(data_buf, keys_buf, np.uint64(ndata), np.uint8(33), np.uint8(66), filt_buf)
global_dims = self.get_global(self.get_grid_dims(ndata))
print "filterPrepare"
if e is None:
e = [ cl.enqueue_nd_range_kernel(self.queue, kernel, global_dims, self.localDims), ]
else:
e = [ cl.enqueue_nd_range_kernel(self.queue, kernel, global_dims, self.localDims, wait_for=e), ]
events += e
return (e, data_buf, keys_buf, filt_buf)
def run_kernel(self, kernel, grid_size, stream=None):
global_size = []
for i, dim in enumerate(grid_size):
global_size.append(dim * kernel.block[i])
cl.enqueue_nd_range_kernel(self.default_queue, kernel, global_size,
kernel.block[0:len(global_size)])
def run(self):
cl.enqueue_nd_range_kernel(
self.queue,
self.kernel,
self.global_size,
self.local_size,
).wait()
def __call__(self, thread_count, work_group_size, *args):
fun = self.compile()
for i, arg in enumerate(args):
fun.set_arg(i, arg)
with timed_region("ParLoopCKernel"):
cl.enqueue_nd_range_kernel(_queue, fun, (thread_count,),
(work_group_size,), g_times_l=False).wait()
def do_opencl_pow(hash, target):
output = numpy.zeros(1, dtype=[('v', numpy.uint64, 1)])
if (ctx == False):
return output[0][0]
data = numpy.zeros(1, dtype=hash_dt, order='C')
data[0]['v'] = ("0000000000000000" + hash).decode("hex")
data[0]['target'] = target
hash_buf = cl.Buffer(ctx, cl.mem_flags.READ_ONLY | cl.mem_flags.COPY_HOST_PTR, hostbuf=data)
dest_buf = cl.Buffer(ctx, cl.mem_flags.WRITE_ONLY, output.nbytes)
kernel = program.kernel_sha512
worksize = kernel.get_work_group_info(cl.kernel_work_group_info.WORK_GROUP_SIZE, cl.get_platforms()[0].get_devices()[1])
kernel.set_arg(0, hash_buf)
kernel.set_arg(1, dest_buf)
start = time.time()
progress = 0
globamt = worksize*2000
while output[0][0] == 0:
kernel.set_arg(2, pack("<Q", progress))
cl.enqueue_nd_range_kernel(queue, kernel, (globamt,), (worksize,))
cl.enqueue_read_buffer(queue, dest_buf, output)
queue.finish()
progress += globamt
sofar = time.time() - start
print sofar, progress / sofar, "hashes/sec"
taken = time.time() - start
print progress, taken
return output[0][0]
def do_opencl_pow(hash, target):
output = numpy.zeros(1, dtype=[('v', numpy.uint64, 1)])
if (ctx == False):
return output[0][0]
data = numpy.zeros(1, dtype=hash_dt, order='C')
data[0]['v'] = ("0000000000000000" + hash).decode("hex")
data[0]['target'] = target
hash_buf = cl.Buffer(ctx, cl.mem_flags.READ_ONLY | cl.mem_flags.COPY_HOST_PTR, hostbuf=data)
dest_buf = cl.Buffer(ctx, cl.mem_flags.WRITE_ONLY, output.nbytes)
kernel = program.kernel_sha512
worksize = kernel.get_work_group_info(cl.kernel_work_group_info.WORK_GROUP_SIZE, gpus[0])
kernel.set_arg(0, hash_buf)
kernel.set_arg(1, dest_buf)
start = time.time()
progress = 0
globamt = worksize*2000
while output[0][0] == 0:
kernel.set_arg(2, pack("<Q", progress))
cl.enqueue_nd_range_kernel(queue, kernel, (globamt,), (worksize,))
cl.enqueue_read_buffer(queue, dest_buf, output)
queue.finish()
progress += globamt
sofar = time.time() - start
print sofar, progress / sofar, "hashes/sec"
taken = time.time() - start
print progress, taken
return output[0][0]
def runFilter(self):
mask = self.atts.getMaskImages(self.mask, self.L)[0]
if self.atts.width*self.atts.height*self.atts.slices != np.product(mask.shape):
print("Mask dimensions not equal to original image's")
return False
globalSize = [0]
localSize = [0]
self.clattr.computeWorkingGroupSize(localSize, globalSize, [self.atts.width, self.atts.height,
self.clattr.maxSliceCount + self.atts.overlap[self.index]])
self.maskBuffer = self.atts.getStructElement(self.clattr.context, self.clattr.queue, mask, globalSize[0])
try:
self.kernel.set_args(self.clattr.inputBuffer, self.maskBuffer, self.clattr.outputBuffer,
np.int32(self.atts.width), np.int32(self.atts.height),
np.int32(self.clattr.maxSliceCount + self.atts.overlap[self.index]))
cl.enqueue_nd_range_kernel(self.clattr.queue, self.kernel, globalSize, localSize)
except Exception as e:
raise e
# write results
cl.enqueue_copy(self.clattr.queue, self.clattr.inputBuffer, self.clattr.outputBuffer)
self.clattr.queue.finish()
return True
def vglCl3dThreshold(img_input, img_output, thresh, top=1.0):
print("# Running vglCl3dThreshold")
vl.vglCheckContext(img_input, vl.VGL_CL_CONTEXT())
vl.vglCheckContext(img_output, vl.VGL_CL_CONTEXT())
if( not isinstance(thresh, np.float32) ):
print("vglCl3dThreshold: Warning: thresh not np.float32! Trying to convert...")
try:
thresh = np.float32(thresh)
except Exception as e:
print("vglCl3dThreshold: Error!! Impossible to convert thresh as a np.float32 object.")
print(str(e))
exit()
if( not isinstance(top, np.float32) ):
print("vglCl3dThreshold: Warning: top not np.float32! Trying to convert...")
try:
top = np.float32(top)
except Exception as e:
print("vglCl3dThreshold: Error!! Impossible to convert top as a np.float32 object.")
print(str(e))
exit()
_program = vl.get_ocl_context().get_compiled_kernel("../CL/vglCl3dThreshold.cl", "vglCl3dThreshold")
kernel_run = _program.vglCl3dThreshold
kernel_run.set_arg(0, img_input.get_oclPtr())
kernel_run.set_arg(1, img_output.get_oclPtr())
kernel_run.set_arg(2, thresh)
kernel_run.set_arg(3, top)
cl.enqueue_nd_range_kernel(vl.get_ocl().commandQueue, kernel_run, img_output.get_oclPtr().shape, None)
vl.vglSetContext(img_output, vl.VGL_CL_CONTEXT())
def vglClNdThreshold(img_input, img_output, thresh, top=255):
if (not img_input.clForceAsBuf == vl.IMAGE_ND_ARRAY()):
print(
"vglClNdCopy: Error: this function supports only OpenCL data as buffer and img_input isn't."
)
exit(1)
if (not img_output.clForceAsBuf == vl.IMAGE_ND_ARRAY()):
print(
"vglClNdCopy: Error: this function supports only OpenCL data as buffer and img_output isn't."
)
exit(1)
vl.vglCheckContext(img_input, vl.VGL_CL_CONTEXT())
vl.vglCheckContext(img_output, vl.VGL_CL_CONTEXT())
# EVALUATING IF thresh IS IN CORRECT TYPE
if (not isinstance(thresh, np.uint8)):
print(
"vglClConvolution: Warning: thresh not np.uint8! Trying to convert..."
)
try:
thresh = np.uint8(thresh)
except Exception as e:
print(
"vglClConvolution: Error!! Impossible to convert thresh as a np.uint8 object."
)
print(str(e))
exit()
# EVALUATING IF top IS IN CORRECT TYPE
if (not isinstance(top, np.uint8)):
print(
"vglClConvolution: Warning: top not np.uint8! Trying to convert..."
)
try:
top = np.uint8(top)
except Exception as e:
print(
"vglClConvolution: Error!! Impossible to convert top as a np.uint8 object."
)
print(str(e))
exit()
_program = vl.get_ocl_context().get_compiled_kernel(
"CL_ND/vglClNdThreshold.cl", "vglClNdThreshold")
_kernel = _program.vglClNdThreshold
_kernel.set_arg(0, img_input.get_oclPtr())
_kernel.set_arg(1, img_output.get_oclPtr())
_kernel.set_arg(2, thresh)
_kernel.set_arg(3, top)
# THIS IS A BLOCKING COMMAND. IT EXECUTES THE KERNEL.
cl.enqueue_nd_range_kernel(vl.get_ocl().commandQueue, _kernel,
img_input.get_ipl().shape, None)
vl.vglSetContext(img_input, vl.VGL_CL_CONTEXT())
vl.vglSetContext(img_output, vl.VGL_CL_CONTEXT())
def vglClNdBinThreshold(self, img_input, img_output, thresh):
if (not img_input.clForceAsBuf == vl.IMAGE_ND_ARRAY()):
print(
"vglClNdBinThreshold: Error: this function supports only OpenCL data as buffer and img_input isn't."
)
exit()
if (not img_output.clForceAsBuf == vl.IMAGE_ND_ARRAY()):
print(
"vglClNdBinThreshold: Error: this function supports only OpenCL data as buffer and img_output isn't."
)
exit()
if (not isinstance(thresh, np.uint8)):
print(
"vglClNdBinThreshold: Warning: thresh not np.uint8! Trying to convert..."
)
try:
thresh = np.uint8(thresh)
except Exception as e:
print(
"vglClNdBinThreshold: Error!! Impossible to convert thresh as a np.uint8 object."
)
print(str(e))
exit()
vl.vglCheckContext(img_input, vl.VGL_CL_CONTEXT())
vl.vglCheckContext(img_output, vl.VGL_CL_CONTEXT())
_program = self.cl_ctx.get_compiled_kernel(
"../CL_BIN/vglClNdBinThreshold.cl", "vglClNdBinThreshold")
kernel_run = _program.vglClNdBinThreshold
mobj_img_shape_input = img_input.getVglShape().get_asVglClShape_buffer(
)
mobj_img_shape_output = img_output.getVglShape(
).get_asVglClShape_buffer()
kernel_run.set_arg(0, img_input.get_oclPtr())
kernel_run.set_arg(1, img_output.get_oclPtr())
kernel_run.set_arg(2, thresh)
kernel_run.set_arg(3, mobj_img_shape_input)
kernel_run.set_arg(4, mobj_img_shape_output)
_worksize_0 = img_input.getWidthIn()
if (img_input.depth == vl.IPL_DEPTH_1U()):
_worksize_0 = img_input.getWidthStep()
if (img_output.depth == vl.IPL_DEPTH_1U()):
_worksize_0 = img_output.getWidthStep()
worksize = (int(_worksize_0), img_input.getHeigthIn(),
img_input.getNFrames())
# ENQUEUEING KERNEL EXECUTION
#cl.enqueue_nd_range_kernel(self.ocl.commandQueue, kernel_run, worksize, None)
cl.enqueue_nd_range_kernel(self.ocl.commandQueue, kernel_run,
img_output.ipl.shape, None)
vl.vglSetContext(img_output, vl.VGL_CL_CONTEXT())
def exec_lsz_safe(self, localsize):
"""execute the kernel with specific localsize.
Safe also for lernels with local variables"""
oldloc = int(self._localsize)
self.localsize = localsize
cl.enqueue_nd_range_kernel(self._solverobj.clqueue, self._clkernel, (self.globalsize,), (self.localsize,))
self._solverobj.clqueue.finish()
self.localsize = oldloc
def Difference(img1, img2, threshold):
img1 = np.array(img1).astype('uint8')
img2 = np.array(img2).astype('uint8')
platforms = cl.get_platforms()
platform = platforms[0]
devices = platform.get_devices(cl.device_type.GPU)
device = devices[0]
context = cl.Context([device])
queue = cl.CommandQueue(context, device)
shape = img1.T.shape
result = np.empty_like(img1)
imgInBuf1 = cl.Image(context,
cl.mem_flags.READ_ONLY,
cl.ImageFormat(cl.channel_order.LUMINANCE,
cl.channel_type.UNORM_INT8),
shape=shape)
imgInBuf2 = cl.Image(context,
cl.mem_flags.READ_ONLY,
cl.ImageFormat(cl.channel_order.LUMINANCE,
cl.channel_type.UNORM_INT8),
shape=shape)
imgOutBuf = cl.Image(context,
cl.mem_flags.WRITE_ONLY,
cl.ImageFormat(cl.channel_order.LUMINANCE,
cl.channel_type.UNORM_INT8),
shape=shape)
program = cl.Program(context, open('Difference.cl').read()).build()
kernel = cl.Kernel(program, 'Difference')
kernel.set_arg(0, imgInBuf1)
kernel.set_arg(1, imgInBuf2)
kernel.set_arg(2, imgOutBuf)
kernel.set_arg(3, np.float32(threshold))
cl.enqueue_copy(queue,
imgInBuf1,
img1,
origin=(0, 0),
region=shape,
is_blocking=False)
cl.enqueue_copy(queue,
imgInBuf2,
img2,
origin=(0, 0),
region=shape,
is_blocking=False)
cl.enqueue_nd_range_kernel(queue, kernel, shape, None)
cl.enqueue_copy(queue,
result,
imgOutBuf,
origin=(0, 0),
region=shape,
is_blocking=True)
return result
def futhark_main(self, screenX_700, screenY_701, depth_702, xmin_703,
ymin_704, xmax_705, ymax_706):
res_707 = (xmax_705 - xmin_703)
res_708 = (ymax_706 - ymin_704)
y_711 = sitofp_i32_f32(screenX_700)
y_712 = sitofp_i32_f32(screenY_701)
x_713 = slt32(np.int32(0), depth_702)
bytes_902 = (np.int32(4) * screenY_701)
mem_903 = cl.Buffer(
self.ctx, cl.mem_flags.READ_WRITE,
long(
long(bytes_902) if (bytes_902 > np.int32(0)) else np.int32(1)))
mem_905 = cl.Buffer(
self.ctx, cl.mem_flags.READ_WRITE,
long(
long(bytes_902) if (bytes_902 > np.int32(0)) else np.int32(1)))
group_size_911 = np.int32(512)
num_groups_912 = squot32(
((screenY_701 + group_size_911) - np.int32(1)), group_size_911)
if ((np.int32(1) * (num_groups_912 * group_size_911)) != np.int32(0)):
self.map_kernel_894_var.set_args(np.float32(ymin_704),
np.float32(y_712),
np.float32(res_708),
np.int32(screenY_701), mem_903,
mem_905)
cl.enqueue_nd_range_kernel(
self.queue, self.map_kernel_894_var, (long(
(num_groups_912 * group_size_911)), ),
(long(group_size_911), ))
if synchronous:
self.queue.finish()
nesting_size_844 = (screenX_700 * screenY_701)
bytes_906 = (bytes_902 * screenX_700)
mem_908 = cl.Buffer(
self.ctx, cl.mem_flags.READ_WRITE,
long(
long(bytes_906) if (bytes_906 > np.int32(0)) else np.int32(1)))
group_size_917 = np.int32(512)
num_groups_918 = squot32(
(((screenY_701 * screenX_700) + group_size_917) - np.int32(1)),
group_size_917)
if ((np.int32(1) * (num_groups_918 * group_size_917)) != np.int32(0)):
self.map_kernel_846_var.set_args(np.int32(screenX_700),
np.int32(screenY_701), mem_905,
np.byte(x_713),
np.int32(depth_702),
np.float32(xmin_703), mem_903,
np.float32(y_711),
np.float32(res_707), mem_908)
cl.enqueue_nd_range_kernel(
self.queue, self.map_kernel_846_var, (long(
(num_groups_918 * group_size_917)), ),
(long(group_size_917), ))
if synchronous:
self.queue.finish()
out_mem_909 = mem_908
out_memsize_910 = bytes_906
return (out_memsize_910, out_mem_909)
def dfunKernel(self, state_variables, coupling, local_coupling=0.0):
n_states = state_variables.shape[0]
n_nodes = state_variables.shape[1]
n_mode = state_variables.shape[2]
# allocate data if not yet done so
if not hasattr(self, '_arrays'):
self._alloc_opencl(n_nodes, n_states=n_states, n_mode=n_mode)
# copy if passed host arrays
if isinstance(state_variables, numpy.ndarray):
# state_variables, coupling will be (1, n, 1)
if (DEBUG):
print("state_variables are ndarray", "states:",
state_variables.shape, "coupling:", coupling.shape)
#self._arrays['state'][:] = state_variables.reshape((1, n_states*n_nodes*n_mode)).astype('f')
#self._arrays['coupling'][:] = coupling.reshape((1, n_nodes)).astype('f')
# self._arrays['state'] = state_variables.flatten()
#self._arrays['coupling'] = coupling.reshape((1, n_nodes)).astype('f')
if (DEBUG):
print(
"state_variable shape:",
state_variables.reshape(
(n_states, n_nodes * n_mode, 1)).astype('f').shape)
print("array state shape", self._arrays['state'][:].shape)
self._arrays['state'][:] = state_variables.reshape(
(n_states, n_nodes, n_mode)).astype('f')
self._arrays['coupling'][:] = coupling.reshape(
(1, n_nodes)).astype('f')
# set kernel arg if passed device arrays
elif isinstance(state_variables, pyopencl.array.Array):
self._kernel.set_args(state_variables.data, coupling.data,
self._arrays['param'].data,
self._arrays['deriv'].data)
# otherwise, complain
else:
raise TypeError('unsupported data type %r', type(state_variables))
# run the kernel and wait
print("Run kernel...")
pyopencl.enqueue_nd_range_kernel(self._queue, self._kernel,
(n_nodes, ), None).wait()
# return derivatives following input type
deriv = self._arrays['deriv']
if (DEBUG):
print("derive shape:", deriv.shape)
if isinstance(state_variables, numpy.ndarray):
deriv = deriv.get().reshape(
(n_states, n_nodes, n_mode)).astype('d')
return deriv
def runKernel(self, maskImages, overlapAmount):
globalSize = [0, 0]
localSize = [0, 0]
self.clattr.computeWorkingGroupSize(localSize, globalSize, [self.atts.width, self.atts.height, 1])
for i in range(len(maskImages)):
mask = maskImages[i]
size = [0, 0, 0]
size[2] = mask.shape[0]
size[1] = mask.shape[1]
size[0] = mask.shape[2]
structElem = self.atts.getStructElement(self.clattr.context, self.clattr.queue, mask)
startOffset = 0
endOffset = 0
if self.atts.overlap[self.index] > 0:
startOffset = int(self.atts.overlap[self.index] / 2)
endOffset = int(self.atts.overlap[self.index] / 2)
if self.atts.sliceStart <= 0:
startOffset = 0
if self.atts.sliceEnd >= 0:
endOffset = 0
if i == 0:
self.kernel.set_args(self.clattr.inputBuffer, self.clattr.outputTmpBuffer, np.int32(self.atts.width),
np.int32(self.atts.height),
np.int32(self.clattr.maxSliceCount+self.atts.overlap[self.index]),
structElem, np.int32(size[0]), np.int32(size[1]), np.int32(size[2]),
np.int32(startOffset), np.int32(endOffset))
else:
tmpBuffer1 = self.clattr.outputTmpBuffer if i%2 != 0 else self.clattr.outputBuffer
tmpBuffer2 = self.clattr.outputTmpBuffer if i%2 == 0 else self.clattr.outputBuffer
self.kernel2.set_args(self.clattr.inputBuffer, tmpBuffer1, tmpBuffer2, np.int32(self.atts.width),
np.int32(self.atts.height),
np.int32(self.clattr.maxSliceCount + self.atts.overlap[self.index]),
structElem, np.int32(size[0]), np.int32(size[1]), np.int32(size[2]),
np.int32(startOffset), np.int32(endOffset))
try:
cl.enqueue_nd_range_kernel(self.clattr.queue, self.kernel if i ==0 else self.kernel2,
globalSize, localSize)
except Exception:
return False
structElem.release()
if len(maskImages)%2 != 0:
tmpBuffer = self.clattr.outputTmpBuffer
self.clattr.outputTmpBuffer = self.clattr.outputBuffer
self.clattr.outputBuffer = tmpBuffer
return True
def test_algorithm(self):
print "\n**************************"
print "test_pbrs:"
passed = 0
buffersize_in = 188*8
buffersize_out = 188*8
# opencl buffer uint
self.inputbuffer = cl.Buffer(self.ctx , cl.mem_flags.READ_WRITE, size=buffersize_in*4)
# opencl buffer uint
self.outputbuffer = cl.Buffer(self.ctx , cl.mem_flags.READ_WRITE, size=buffersize_out*4)
for k in self.kernelname:
kernel = self.load_kernel(self.filename, k)
passed = 0
self.fd_input = open('test_bench_pbrs_input.csv', 'r')
self.fd_output = open('test_bench_pbrs_output.csv', 'r')
for j in range(0,6):
encoded_data = numpy.array(numpy.zeros(buffersize_out/4), dtype=numpy.uint32)
data_to_encode = string.replace(self.fd_input.readline(),'\n','')
reference_data = string.replace(self.fd_output.readline(),'\n','')
for i in range(0,7):
data_to_encode = "%s,%s" % (data_to_encode, string.replace(self.fd_input.readline(),'\n',''))
reference_data = "%s,%s" % (reference_data, string.replace(self.fd_output.readline(),'\n',''))
data_to_encode = numpy.fromstring(numpy.fromstring(data_to_encode, dtype=numpy.uint8, sep=",").tostring(), dtype=numpy.uint32)
reference_data = numpy.fromstring(reference_data, dtype=numpy.uint8, sep=",")
cl.enqueue_copy(self.queue, self.inputbuffer, data_to_encode).wait()
kernel.set_args(self.inputbuffer, self.outputbuffer)
cl.enqueue_nd_range_kernel(self.queue,kernel,(8,),(8,),None ).wait()
cl.enqueue_copy(self.queue, encoded_data, self.outputbuffer).wait()
encoded_data = (numpy.fromstring(encoded_data.tostring(), dtype=numpy.uint8))
if encoded_data.tostring() == reference_data.tostring():
passed += 1
print "Test %d PASSED" % (j+1)
else:
print "Test %d FAILED" % (j+1)
print "input data:"
print numpy.fromstring(data_to_encode.tostring(), dtype=numpy.uint8)
print "encoded data:"
print numpy.fromstring(encoded_data.tostring(), dtype=numpy.uint8)
print "reference data:"
print reference_data
print "error data:"
print (reference_data - numpy.fromstring(encoded_data.tostring(), dtype=numpy.uint8))
print "%d pass out of 6" % passed
self.fd_input.close()
self.fd_output.close()
if passed == 6:
print "All pbrs tests PASS\n"
return True
else:
print "at least one pbrs test FAILED\n"
return False
def prefixSumDownInplace(self, e, data, ndata, events):
import numpy as np
import pyopencl as cl
mf = cl.mem_flags
if not isinstance(data, cl.Buffer):
data_buf = cl.Buffer(self.ctx,
mf.READ_WRITE | mf.COPY_HOST_PTR,
hostbuf=data)
else:
data_buf = data
grid_dims = self.get_grid_dims(ndata)
psumbytes = int(np.prod(grid_dims) * np.uint64(0).nbytes)
npsumbytes = np.uint64(0).nbytes
psum_buf = cl.Buffer(self.ctx, mf.READ_WRITE, psumbytes)
npsum_buf = cl.Buffer(self.ctx, mf.READ_WRITE, npsumbytes)
kernel = self.prg.prefixSumDownInplace
kernel.set_args(data_buf, np.uint64(ndata), psum_buf, npsum_buf)
global_dims = self.get_global(grid_dims)
print "prefixSumDownInplace %s %s %d %d" % (
str(global_dims), str(self.localDims), ndata, psumbytes)
if e is None:
e = (cl.enqueue_nd_range_kernel(self.queue,
kernel,
global_dims,
self.localDims,
wait_for=e), )
else:
e = (cl.enqueue_nd_range_kernel(self.queue, kernel, global_dims,
self.localDims), )
events += e
npsum = np.zeros(1, dtype=np.uint64)
events += (cl.enqueue_copy(self.queue, npsum, npsum_buf, wait_for=e), )
if npsum > 1:
(e, psum_buf, psum1_buf, npsum1_buf,
ndata2) = self.prefixSumDownInplace(e, psum_buf, npsum.item(),
events)
else:
ndata2 = np.zeros(1, dtype=np.uint64)
events += (cl.enqueue_copy(self.queue,
ndata2,
psum_buf,
wait_for=e), )
ndata2 = ndata2.item()
print ndata2
self.prefixSumUp(e, data_buf, ndata, psum_buf, npsum, events)
return (e, data_buf, psum_buf, npsum_buf, ndata2)
def vglCl3dDilate(img_input, img_output, convolution_window, window_size_x, window_size_y, window_size_z):
print("# Running vglCl3dDilate")
vl.vglCheckContext(img_input, vl.VGL_CL_CONTEXT())
vl.vglCheckContext(img_output, vl.VGL_CL_CONTEXT())
# TRANSFORMAR EM BUFFER
try:
cl_convolution_window = cl.Buffer(vl.get_ocl().context, cl.mem_flags.READ_ONLY, convolution_window.nbytes)
cl.enqueue_copy(vl.get_ocl().commandQueue, cl_convolution_window, convolution_window.tobytes(), is_blocking=True)
convolution_window = cl_convolution_window
except Exception as e:
print("vglCl3dDilate: Error!! Impossible to convert convolution_window to cl.Buffer object.")
print(str(e))
exit()
if( not isinstance(window_size_x, np.uint32) ):
print("vglCl3dDilate: Warning: window_size_x not np.uint32! Trying to convert...")
try:
window_size_x = np.uint32(window_size_x)
except Exception as e:
print("vglCl3dDilate: Error!! Impossible to convert window_size_x as a np.uint32 object.")
print(str(e))
exit()
if( not isinstance(window_size_y, np.uint32) ):
print("vglCl3dDilate: Warning: window_size_y not np.uint32! Trying to convert...")
try:
window_size_y = np.uint32(window_size_y)
except Exception as e:
print("vglCl3dDilate: Error!! Impossible to convert window_size_y as a np.uint32 object.")
print(str(e))
exit()
if( not isinstance(window_size_z, np.uint32) ):
print("vglCl3dDilate: Warning: window_size_z not np.uint32! Trying to convert...")
try:
window_size_z = np.uint32(window_size_z)
except Exception as e:
print("vglCl3dDilate: Error!! Impossible to convert window_size_z as a np.uint32 object.")
print(str(e))
exit()
_program = vl.get_ocl_context().get_compiled_kernel("../CL/vglCl3dDilate.cl", "vglCl3dDilate")
kernel_run = _program.vglCl3dDilate
kernel_run.set_arg(0, img_input.get_oclPtr())
kernel_run.set_arg(1, img_output.get_oclPtr())
kernel_run.set_arg(2, convolution_window)
kernel_run.set_arg(3, window_size_x)
kernel_run.set_arg(4, window_size_y)
kernel_run.set_arg(5, window_size_z)
cl.enqueue_nd_range_kernel(vl.get_ocl().commandQueue, kernel_run, img_output.get_oclPtr().shape, None)
vl.vglSetContext(img_output, vl.VGL_CL_CONTEXT())
def calc_weights_gradient(self):
"""
Calculate gradient of weights.
This method should be called only for processed layers as it's used
inputs array which is valid only at processing time.
"""
for l in self._next_layers:
if not l[0].processed:
l[0].calc_weights_gradient()
queue = self.opencl.queue
kernel = self.opencl.kernel_calc_layer_gradient
kernel.set_arg(2, self._inputs_offset)
kernel.set_arg(3, self._neurons_offset)
kernel.set_arg(4, self._inputs_per_neuron)
kernel.set_arg(5, self._weights_offset)
kernel.set_arg(7, self._weights_count)
kernel.set_arg(
8,
pyopencl.LocalMemory(
int(4 *
(self._inputs_per_neuron + 1 +
self.opencl.max_local_size[0] // self._inputs_per_neuron))
))
self._calc_gradient_event = pyopencl.enqueue_nd_range_kernel(
queue,
kernel, (int(self._weights_buf_size), ),
(self.opencl.max_local_size[0], ),
wait_for=self._calc_gradient_wait_for)
del self._calc_gradient_wait_for[:]
kernel = self.opencl.kernel_propagate_errors
kernel.set_arg(2, self._neurons_offset)
kernel.set_arg(5, self._neuron_count)
kernel.set_arg(7, self._inputs_per_neuron)
i_s = numpy.int32(1)
for l in self._prev_layers:
kernel.set_arg(3, l[0]._neurons_offset + l[1])
kernel.set_arg(4, l[2])
kernel.set_arg(6, self._weights_offset + i_s)
l[0]._calc_gradient_wait_for.append(
pyopencl.enqueue_nd_range_kernel(
queue,
kernel, (int(l[2] * 64), ), (64, ),
wait_for=(self._calc_gradient_event, )))
i_s += l[2]
self._processed = True
def execute(self):
kernel = self.program.mul
kernel.set_args(self.a_buf, self.b_buf, self.c_buf, numpy.int32(2),
numpy.int32(5), numpy.int32(10))
cl.enqueue_nd_range_kernel(self.queue, kernel, (2, 5), None)
c = numpy.empty_like(self.a.dot(self.b))
cl.enqueue_copy(self.queue, c, self.c_buf).wait()
print("a", self.a)
print("b", self.b)
print("c", c)
def vglClNdThreshold(self, img_input, img_output, thresh, top=255):
print("# Running vglClNdThreshold")
if (not img_input.clForceAsBuf == vl.IMAGE_ND_ARRAY()):
print(
"vglClNdThreshold: Error: this function supports only OpenCL data as buffer and img_input isn't."
)
exit()
if (not img_output.clForceAsBuf == vl.IMAGE_ND_ARRAY()):
print(
"vglClNdThreshold: Error: this function supports only OpenCL data as buffer and img_output isn't."
)
exit()
if (not isinstance(thresh, np.uint8)):
print(
"vglClNdThreshold: Warning: thresh not np.uint8! Trying to convert..."
)
try:
thresh = np.uint8(thresh)
except Exception as e:
print(
"vglClNdThreshold: Error!! Impossible to convert thresh as a np.uint8 object."
)
print(str(e))
exit()
if (not isinstance(top, np.uint8)):
print(
"vglClNdThreshold: Warning: top not np.uint8! Trying to convert..."
)
try:
top = np.uint8(top)
except Exception as e:
print(
"vglClNdThreshold: Error!! Impossible to convert top as a np.uint8 object."
)
print(str(e))
exit()
vl.vglCheckContext(img_input, vl.VGL_CL_CONTEXT())
vl.vglCheckContext(img_output, vl.VGL_CL_CONTEXT())
_program = self.cl_ctx.get_compiled_kernel(
"../CL_ND/vglClNdThreshold.cl", "vglClNdThreshold")
kernel_run = _program.vglClNdThreshold
kernel_run.set_arg(0, img_input.get_oclPtr())
kernel_run.set_arg(1, img_output.get_oclPtr())
kernel_run.set_arg(2, thresh)
kernel_run.set_arg(3, top)
cl.enqueue_nd_range_kernel(self.ocl.commandQueue, kernel_run,
img_output.get_ipl().shape, None)
vl.vglSetContext(img_output, vl.VGL_CL_CONTEXT())
def max_reduce_real4(ipt):
x = CLReal(len(ipt))
y = CLReal(len(ipt))
z = CLReal(len(ipt))
kern = _splitkern_real4.kern
kern.set_arg(0, ipt._buffer)
kern.set_arg(1, x._buffer)
kern.set_arg(2, y._buffer)
kern.set_arg(3, z._buffer)
cl.enqueue_nd_range_kernel(ipt._ctrl.clqueue, kern, (len(ipt),), None)
return max_reduce(x), max_reduce(y), max_reduce(z)
def send(self):
# Set the Kernel Arguments
npSize = np.int32(self.data_size / 4)
self.ocl_krnl_input_stage.set_args(self.buffer_input, npSize)
# Copy input data to device global memory
cl.enqueue_migrate_mem_objects(self.ocl_q, [self.buffer_input],
flags=0)
# Launch the Kernel
cl.enqueue_nd_range_kernel(self.ocl_q, self.ocl_krnl_input_stage, [1],
[1])
def prefixSum(self, e, data, keys, ndata, low, hi, events):
import numpy as np
import pyopencl as cl
mf = cl.mem_flags
if not isinstance(data, cl.Buffer):
data_buf = cl.Buffer(self.ctx, mf.READ_ONLY | mf.COPY_HOST_PTR, hostbuf= data)
else:
data_buf = data
if not isinstance(keys, cl.Buffer):
keys_buf = cl.Buffer(self.ctx, mf.READ_ONLY | mf.COPY_HOST_PTR, hostbuf= keys)
else:
keys_buf = keys
grid_dims = self.get_grid_dims(ndata)
psumbytes = ndata * np.uint64(0).nbytes
bsumbytes = int(np.prod(grid_dims) * np.uint64(0).nbytes)
nbsumbytes = np.uint64(0).nbytes
psum_buf = cl.Buffer(self.ctx, mf.READ_WRITE, psumbytes)
bsum_buf = cl.Buffer(self.ctx, mf.READ_WRITE, bsumbytes)
nbsum_buf = cl.Buffer(self.ctx, mf.READ_WRITE, nbsumbytes)
low = PrefixSum.HOST_TYPE_KEYS(low)
hi = PrefixSum.HOST_TYPE_KEYS(hi)
kernel = self.prg.prefixSumDown
kernel.set_args(data_buf, keys_buf, np.uint64(ndata), low, hi, psum_buf, bsum_buf, nbsum_buf)
global_dims = self.get_global(grid_dims)
print "prefixSumDown %s %s" % (str(global_dims), str(self.localDims))
if e is None:
e = ( cl.enqueue_nd_range_kernel(self.queue, kernel, global_dims, self.localDims, wait_for=e), )
else:
e = ( cl.enqueue_nd_range_kernel(self.queue, kernel, global_dims, self.localDims), )
events += e
nbsum = np.zeros(1, dtype = np.uint64)
events += (cl.enqueue_copy(self.queue, nbsum, nbsum_buf, wait_for=e),)
if nbsum>1:
(e, bsum_buf, bsum1_buf, nbsum1_buf, ndata2) = self.prefixSumDownInplace(e, bsum_buf, nbsum.item(), events)
else:
ndata2 = np.zeros(1, dtype = np.uint64)
events += (cl.enqueue_copy(self.queue, ndata2, bsum_buf, wait_for=e),)
ndata2 = ndata2.item()
print ndata2
self.prefixSumUp(e, psum_buf, ndata, bsum_buf, nbsum, events)
return (e, data_buf, keys_buf, psum_buf, bsum_buf, nbsum_buf, ndata2)
def vglClNdBinMin(self, img_input, img_input2, img_output):
if (not img_input.clForceAsBuf == vl.IMAGE_ND_ARRAY()):
print(
"vglClNdBinMin: Error: this function supports only OpenCL data as buffer and img_input isn't."
)
exit()
if (not img_input2.clForceAsBuf == vl.IMAGE_ND_ARRAY()):
print(
"vglClNdBinMin: Error: this function supports only OpenCL data as buffer and img_input isn't."
)
exit()
if (not img_output.clForceAsBuf == vl.IMAGE_ND_ARRAY()):
print(
"vglClNdBinMin: Error: this function supports only OpenCL data as buffer and img_output isn't."
)
exit()
vl.vglCheckContext(img_input, vl.VGL_CL_CONTEXT())
vl.vglCheckContext(img_input2, vl.VGL_CL_CONTEXT())
vl.vglCheckContext(img_output, vl.VGL_CL_CONTEXT())
if (not isinstance(window, vl.VglStrEl)):
print(
"vglClNdBinMin: Error: window is not a VglStrEl object. aborting execution."
)
exit()
_program = self.cl_ctx.get_compiled_kernel(
"../CL_BIN/vglClNdBinMin.cl", "vglClNdBinMin")
kernel_run = _program.vglClNdBinMin
kernel_run.set_arg(0, img_input.get_oclPtr())
kernel_run.set_arg(1, img_input2.get_oclPtr())
kernel_run.set_arg(2, img_output.get_oclPtr())
_worksize_0 = img_input.getWidthIn()
if (img_input.depth == vl.IPL_DEPTH_1U()):
_worksize_0 = img_input.getWidthStep()
if (img_input2.depth == vl.IPL_DEPTH_1U()):
_worksize_0 = img_input2.getWidthStep()
if (img_output.depth == vl.IPL_DEPTH_1U()):
_worksize_0 = img_output.getWidthStep()
worksize = (int(_worksize_0), img_input.getHeigthIn(),
img_input.getNFrames())
# ENQUEUEING KERNEL EXECUTION
#cl.enqueue_nd_range_kernel(self.ocl.commandQueue, kernel_run, worksize, None)
cl.enqueue_nd_range_kernel(self.ocl.commandQueue, kernel_run,
img_output.get_ipl().shape, None)
vl.vglSetContext(img_output, vl.VGL_CL_CONTEXT())
def filter(self, data, keys, low, hi, events):
import numpy as np
import pyopencl as cl
mf = cl.mem_flags
ndata = data.size
(e, data_buf, keys_buf, indices_buf, bsum_buf, nbsum_buf, ndata2) = self.prefixSum(None, data, keys, ndata, low, hi, events)
filt = np.zeros(ndata, dtype = np.bool8)
indices = np.zeros(ndata, dtype = np.uint64)
data2 = np.zeros(ndata2, dtype = PrefixSum.HOST_TYPE_DATA)
keys2 = np.zeros(ndata2, dtype = PrefixSum.HOST_TYPE_KEYS)
ndata2bytes = np.uint64(0).nbytes
if PrefixSum.RETURN_FILTER == 1:
filt_buf = cl.Buffer(self.ctx, mf.READ_WRITE, filt.nbytes)
print data2.nbytes
data2_buf = cl.Buffer(self.ctx, mf.READ_WRITE, data2.nbytes)
keys2_buf = cl.Buffer(self.ctx, mf.READ_WRITE, keys2.nbytes)
ndata2_buf = cl.Buffer(self.ctx, mf.READ_WRITE, ndata2bytes)
low = PrefixSum.HOST_TYPE_KEYS(low)
hi = PrefixSum.HOST_TYPE_KEYS(hi)
kernel = self.prg.filter
if PrefixSum.RETURN_FILTER == 1:
kernel.set_args(data_buf, keys_buf, indices_buf, np.uint64(ndata), low, hi, filt_buf, data2_buf, keys2_buf, ndata2_buf)
else:
kernel.set_args(data_buf, keys_buf, indices_buf, np.uint64(ndata), low, hi, data2_buf, keys2_buf, ndata2_buf)
global_dims = self.get_global(self.get_grid_dims(ndata))
print "filter"
if e is None:
e = ( cl.enqueue_nd_range_kernel(self.queue, kernel, global_dims, self.localDims, wait_for=e), )
else:
e = ( cl.enqueue_nd_range_kernel(self.queue, kernel, global_dims, self.localDims), )
events += e
if PrefixSum.RETURN_FILTER == 1:
events += ( cl.enqueue_copy(self.queue, filt, filt_buf, wait_for=e),
cl.enqueue_copy(self.queue, indices, indices_buf, wait_for=e),
cl.enqueue_copy(self.queue, data2, data2_buf, wait_for=e),
cl.enqueue_copy(self.queue, keys2, keys2_buf, wait_for=e) )
else:
events += ( cl.enqueue_copy(self.queue, indices, indices_buf, wait_for=e),
cl.enqueue_copy(self.queue, data2, data2_buf, wait_for=e),
cl.enqueue_copy(self.queue, keys2, keys2_buf, wait_for=e) )
return (filt, indices, data2, keys2)
def futhark_render(self, width_743, height_744, time_745, degree_746):
y_749 = sitofp_i32_f32(height_744)
y_750 = sitofp_i32_f32(width_743)
x_751 = fpow32(time_745, np.float32((1.5)))
y_752 = (x_751 * np.float32((5.0e-3)))
res_753 = (np.float32((1.0)) + y_752)
res_754 = (np.float32((3.1415927)) / res_753)
group_size_893 = self.group_size
y_894 = (group_size_893 - np.int32(1))
x_895 = (width_743 + y_894)
num_groups_896 = squot32(x_895, group_size_893)
num_threads_897 = (num_groups_896 * group_size_893)
bytes_911 = (np.int32(4) * width_743)
mem_912 = cl.Buffer(
self.ctx, cl.mem_flags.READ_WRITE,
np.long(
np.long(bytes_911) if (
bytes_911 > np.int32(0)) else np.int32(1)))
if ((np.int32(1) * (num_groups_896 * group_size_893)) != np.int32(0)):
self.map_kernel_898_var.set_args(mem_912, np.float32(y_749),
np.int32(width_743))
cl.enqueue_nd_range_kernel(
self.queue, self.map_kernel_898_var, (np.long(
(num_groups_896 * group_size_893)), ),
(np.long(group_size_893), ))
if synchronous:
self.queue.finish()
nesting_size_833 = (height_744 * width_743)
x_836 = (nesting_size_833 + y_894)
num_groups_837 = squot32(x_836, group_size_893)
num_threads_838 = (num_groups_837 * group_size_893)
bytes_913 = (bytes_911 * height_744)
mem_915 = cl.Buffer(
self.ctx, cl.mem_flags.READ_WRITE,
np.long(
np.long(bytes_913) if (
bytes_913 > np.int32(0)) else np.int32(1)))
if ((np.int32(1) * (num_groups_837 * group_size_893)) != np.int32(0)):
self.map_kernel_839_var.set_args(mem_912, np.int32(height_744),
np.float32(res_754),
np.float32(y_750),
np.int32(degree_746), mem_915,
np.int32(width_743))
cl.enqueue_nd_range_kernel(
self.queue, self.map_kernel_839_var, (np.long(
(num_groups_837 * group_size_893)), ),
(np.long(group_size_893), ))
if synchronous:
self.queue.finish()
out_mem_917 = mem_915
out_memsize_918 = bytes_913
return (out_memsize_918, out_mem_917)
def solve(self,puzzle,simulations = 16384, iterations = 35, workGroupSize = 128):
self.simulations = simulations
self.iterations = iterations
self.workGroupSize = workGroupSize
self.workGroups = int(self.simulations / self.workGroupSize)
self.width = np.int8(puzzle['width'])
self.height = np.int8(puzzle['height'])
#initialise buffers
self.initBuffers(puzzle)
#create kernel
self.kernel = cl.Kernel(self.program,"montecarlo")
self.kernel.set_args(self.lengthsBuffer,self.groupLengthsBuffer,self.puzzlesBuffer,self.solutionsBuffer,self.height,self.width,np.int32(self.iterations))
#execute program for a number of iterations
cl.enqueue_nd_range_kernel(self.queue,self.kernel,(self.simulations,),(self.workGroupSize,))
#unmap group lengths buffer from device
cl.enqueue_map_buffer(self.queue,self.groupLengthsBuffer,cl.map_flags.WRITE,0,self.groupLengths.shape,self.groupLengths.dtype)
self.groupLengths = self.groupLengthsBuffer.get_host_array(self.groupLengths.shape,dtype=self.groupLengths.dtype)
#unmap solutions buffer from device
cl.enqueue_map_buffer(self.queue,self.solutionsBuffer,cl.map_flags.WRITE,0,self.solutionsFlattened.shape,self.solutions.dtype)
self.solutions = self.solutionsBuffer.get_host_array(self.solutions.shape,dtype=self.solutions.dtype)
#release buffers
self.lengthsBuffer.release()
self.groupLengthsBuffer.release()
self.puzzlesBuffer.release()
self.solutionsBuffer.release()
#get the best solution
i = self.groupLengths.argmin()
bestSolution = np.array(self.solutions[i])
#convert solution to list format used by challenge
solution = []
for row in range(0,puzzle['height']):
for col in range(0,puzzle['width']):
if bestSolution[row][col]!=-1:
s = bestSolution[row][col]
#add to solution list
solution.append({'X': int(col),'Y': int(row),'Size':int(s)})
#clear cells in solution
for i in range(0,s):
for j in range(0,s):
bestSolution[row+i][col+j]=-1
return solution
def change_display(image) :
image_buf = cl.Buffer(ctx, mf.READ_ONLY | mf.COPY_HOST_PTR, hostbuf=image)
mem = cl.GLBuffer(ctx, mf.WRITE_ONLY, numpy.float32(buf))
cl.enqueue_acquire_gl_objects(queue, [mem])
add_knl = prog.add
add_knl.set_args(image_buf, mem)
cl.enqueue_nd_range_kernel(queue, add_knl, image.shape, None)
cl.enqueue_release_gl_objects(queue, [mem])
queue.finish()
glFlush()
def _exec_chunked_unsafe(self, chunksize=0):
"""Unsafe for kernels with local variables."""
if chunksize > 0:
self._prep_chunked_exec(chunksize)
lenarr = self.leadingvar.length
ncnk = int(ceil(float(lenarr)/float(self._cnksz)))
cnksz = self._cnksz
for i in range(ncnk):
if (i == (ncnk - 1)) and not(lenarr % cnksz == 0):
cnksz = lenarr % cnksz
self._solverobj.__setattr__(self._cnk_name, i)
cl.enqueue_nd_range_kernel(self._solverobj.clqueue, self._clkernel, (cnksz,), None)
self._solverobj.clqueue.finish()
def updateEt_vanilla(self, algo="SHG"):
root.debug("Updating Et using vanilla algorithm")
t0 = time.clock()
# transform = FFT(self.ctx, self.q, (self.Esig_w_tau_cla,) , (self.Esig_t_tau_p_cla,) , axes = [1])
# events = transform.enqueue(forward = False)
# self.Esig_t_tau_p_cla.set(np.fft.ifft(self.Esig_w_tau_cla.get(), axis=1).astype(self.dtype_c).copy())
if self.useCL == True:
events = self.Esig_t_tau_p_fft.enqueue(forward=False)
for e in events:
e.wait()
if algo == "SD":
krn = self.progs.progs["updateEtVanillaSumSD"].updateEtVanillaSumSD
krn.set_scalar_arg_dtypes((None, None, np.int32))
krn.set_args(self.Esig_t_tau_p_cla.data, self.Et_cla.data, self.N)
ev = cl.enqueue_nd_range_kernel(self.q, krn, self.Et.shape, None)
ev.wait()
Et = self.Et_cla.get()
self.Et_cla.set(-np.conj(Et).astype(self.dtype_c).copy())
# Esig_w_tau = self.Esig_w_tau_cla.get()
# Gm = np.conj(Esig_w_tau.sum(axis=1))[::-1]
# self.Et_cla.set(Gm.copy())
else:
krn = self.progs.progs["updateEtVanillaSumSHG"].updateEtVanillaSumSHG
krn.set_scalar_arg_dtypes((None, None, np.int32))
krn.set_args(self.Esig_t_tau_p_cla.data, self.Et_cla.data, self.N)
ev = cl.enqueue_nd_range_kernel(self.q, krn, self.Et.shape, None)
ev.wait()
krn = self.progs.progs["updateEtVanillaNorm"].updateEtVanillaNorm
krn.set_scalar_arg_dtypes((None, np.int32))
krn.set_args(self.Et_cla.data, self.N)
ev = cl.enqueue_nd_range_kernel(self.q, krn, [1], None)
ev.wait()
else:
self.Esig_t_tau_p_cla.set(np.fft.ifft(self.Esig_w_tau_cla.get(), axis=1).astype(self.dtype_c).copy())
Esig_t_tau_p = self.Esig_t_tau_p_cla.get()
if algo == "SD":
Et = np.sqrt(Esig_t_tau_p.sum(axis=0))
# Et = (Esig_t_tau_p.sum(axis=0))
else:
Et = Esig_t_tau_p.sum(axis=0)
Et = Et / np.abs(Et).max()
self.Et_cla.set(Et)
root.debug("".join(("Time spent: ", str(time.clock() - t0))))
def test_algorithm(self):
print "\n**************************"
print "test_reedsolomon:"
passed = 0
linecnt = 1
# opencl buffer uint
self.inputbuffer = cl.Buffer(self.ctx , cl.mem_flags.READ_WRITE, size=48*4)
# opencl buffer uint
self.outputbuffer = cl.Buffer(self.ctx , cl.mem_flags.READ_WRITE, size=51*4)
for k in self.kernelname:
kernel = self.load_kernel(self.filename, k)
self.fd_input = open('test_bench_rs_input.csv', 'r')
self.fd_output = open('test_bench_rs_output.csv', 'r')
for line in self.fd_input:
data_to_encode = numpy.fromstring(line, dtype=numpy.uint8, sep=",").tostring()
data_to_encode = numpy.fromstring(data_to_encode, dtype=numpy.uint32)
encoded_data = numpy.array(numpy.zeros(51), dtype=numpy.uint32)
reference_data = numpy.fromstring(self.fd_output.readline(), dtype=numpy.uint8, sep=",")
cl.enqueue_copy(self.queue, self.inputbuffer, data_to_encode).wait()
kernel.set_args(self.inputbuffer, self.outputbuffer)
cl.enqueue_nd_range_kernel(self.queue,kernel,(1,),None ).wait()
cl.enqueue_copy(self.queue, encoded_data, self.outputbuffer).wait()
if encoded_data.tostring() == reference_data.tostring():
passed += 1
print "Test %d PASSED" % linecnt
else:
print "Test %d FAILED" % linecnt
print "input data:"
print numpy.fromstring(data_to_encode.tostring(), dtype=numpy.uint8)
print "encoded data:"
print numpy.fromstring(encoded_data.tostring(), dtype=numpy.uint8)
print "reference data:"
print reference_data
print "error data:"
print (reference_data - numpy.fromstring(encoded_data.tostring(), dtype=numpy.uint8))
linecnt += 1
print "%d pass out of %d" % (passed,(linecnt-1))
self.fd_input.close()
self.fd_output.close()
if passed == (linecnt-1):
print "All reedsolomon tests PASS\n"
return True
else:
print "at least one reedsolomon test FAILED\n"
return False
def calc_weights_gradient( self ):
"""
Calculate gradient of weights.
This method should be called only for processed layers as it's used
inputs array which is valid only at processing time.
"""
for l in self._next_layers:
if not l[0].processed:
l[0].calc_weights_gradient()
queue = self.opencl.queue
kernel = self.opencl.kernel_calc_layer_gradient
kernel.set_arg( 2, self._inputs_offset )
kernel.set_arg( 3, self._neurons_offset )
kernel.set_arg( 4, self._inputs_per_neuron )
kernel.set_arg( 5, self._weights_offset )
kernel.set_arg( 7, self._weights_count )
kernel.set_arg( 8, pyopencl.LocalMemory( int(
4 * ( self._inputs_per_neuron + 1 + self.opencl.max_local_size[ 0 ] // self._inputs_per_neuron ) ) ) )
self._calc_gradient_event = pyopencl.enqueue_nd_range_kernel( queue, kernel,
( int( self._weights_buf_size ), ), ( self.opencl.max_local_size[ 0 ], ),
wait_for = self._calc_gradient_wait_for
)
del self._calc_gradient_wait_for[:]
kernel = self.opencl.kernel_propagate_errors
kernel.set_arg( 2, self._neurons_offset )
kernel.set_arg( 5, self._neuron_count )
kernel.set_arg( 7, self._inputs_per_neuron )
i_s = numpy.int32( 1 )
for l in self._prev_layers:
kernel.set_arg( 3, l[0]._neurons_offset + l[1] )
kernel.set_arg( 4, l[2] )
kernel.set_arg( 6, self._weights_offset + i_s )
l[0]._calc_gradient_wait_for.append( pyopencl.enqueue_nd_range_kernel( queue, kernel,
( int( l[2] * 64 ), ), ( 64, ),
wait_for = ( self._calc_gradient_event, )
) )
i_s += l[2]
self._processed = True
def gpu_amend_values(queue, kernels, gpu_params, buffers, amendments):
"""
Transfers requested amendments (after collision detection check) to the GPU,
where a kernel applies them to the data
"""
intermediary_events = []
packet = amendments.get_packet()
if packet[amendments.amount_i] > 0:
events = [
cl.enqueue_copy(queue, buffers["global_amendments_n"],
packet[amendments.amount_i]),
cl.enqueue_copy(queue, buffers["global_amendment_indices"],
packet[amendments.indices_i]),
cl.enqueue_copy(queue, buffers["global_amendment_values"],
packet[amendments.values_i])]
# X groups of 64 items (amendments.amount work items)
intermediary_events.append(
cl.enqueue_nd_range_kernel(
queue, kernels["k_update_values"],
(int(np.ceil(amendments.amount / gpu_params["preferred_multiple"]) *
gpu_params["preferred_multiple"]),),
(gpu_params["preferred_multiple"],), global_work_offset=None,
wait_for=events))
return intermediary_events
def enqueue(self, wait_for=None, profiling=False):
ev = cl.enqueue_nd_range_kernel(
self.queue, self.kern, self.gsize, self.lsize,
wait_for=wait_for)
if profiling:
self._events_to_profile.append(ev)
return ev
def update_map(queue, kernels, intermediary_events):
"""
Updates map. Updating includes:
- "Dissoluting" pheromones: pheromone level is reduced regularly to simulate ageing.
"""
intermediary_events.append(cl.enqueue_nd_range_kernel(
queue, kernels["k_update_map"], [1], [1]))
def applyIntensityData(self, I_w_tau=None):
root.debug("Applying intensity data from experiment")
t0 = time.clock()
krn = self.progs.progs["applyIntensityData"].applyIntensityData
krn.set_scalar_arg_dtypes((None, None, np.int32))
krn.set_args(self.Esig_w_tau_cla.data, self.I_w_tau_cla.data, self.N)
ev = cl.enqueue_nd_range_kernel(self.q, krn, self.Esig_w_tau.shape, None)
ev.wait()
# if self.useCL == True:
# krn = self.progs.progs['applyIntensityData'].applyIntensityData
# krn.set_scalar_arg_dtypes((None, None, np.int32))
# krn.set_args(self.Esig_w_tau_cla.data, self.I_w_tau_cla.data, self.N)
# ev = cl.enqueue_nd_range_kernel(self.q, krn, self.Esig_w_tau.shape, None)
# ev.wait()
# else:
# eps = 0.00
# Esig_w_tau = self.Esig_w_tau_cla.get()
# Esig_mag = np.abs(Esig_w_tau)
#
# Esig_w_tau_p = np.zeros_like(Esig_w_tau)
# good_ind = np.where(Esig_mag > eps)
# Esig_w_tau_p[good_ind[0], good_ind[1]] = np.sqrt(self.I_w_tau_cla.get()[good_ind[0], good_ind[1]])*Esig_w_tau[good_ind[0], good_ind[1]]/Esig_mag[good_ind[0], good_ind[1]]
root.debug("".join(("Time spent: ", str(time.clock() - t0))))
def calc_chi2(self, queue, interspace, q, Iq,
rind, rxyz, lind, lxyz, origin, voxelspacing, fifj, targetIq, sq, chi2):
kernel = self.kernels.calc_chi2
workgroupsize = 16
gws = (queue.device.max_compute_units * workgroupsize * 512,)
lws = (workgroupsize,)
floatsize = 4
tmpIq = cl.LocalMemory(floatsize * q.shape[0] * workgroupsize)
shape = np.zeros(4, dtype=np.int32)
shape[:-1] = interspace.shape
shape[-1] = interspace.size
nq = np.int32(q.shape[0])
nind1 = np.int32(rind.shape[0])
nind2 = np.int32(lind.shape[0])
fifj_shape = np.zeros(4, dtype=np.int32)
fifj_shape[:-1] = fifj.shape
fifj_shape[-1] = fifj.size
kernel.set_args(interspace.data, q.data, Iq.data, tmpIq, rind.data, rxyz.data,
lind.data, lxyz.data, origin, voxelspacing, fifj.data, targetIq.data, sq.data, chi2.data,
shape, nq, nind1, nind2, fifj_shape)
status = cl.enqueue_nd_range_kernel(queue, kernel, gws, lws)
return status
def gradZSD_gpu(self):
root.debug("Calculating dZ for SD using gpu")
krn = self.progs.progs["gradZSD"].gradZSD
krn.set_scalar_arg_dtypes((None, None, None, np.int32))
krn.set_args(self.Esig_t_tau_p_cla.data, self.Et_cla.data, self.dZ_cla.data, self.N)
ev = cl.enqueue_nd_range_kernel(self.q, krn, self.Et.shape, None)
ev.wait()
def __call__(self, *args, **kwargs):
vectors = []
invocation_args = []
for arg, arg_descr in zip(args, self.arguments):
if isinstance(arg_descr, VectorArg):
if not arg.flags.forc:
raise RuntimeError("ElementwiseKernel cannot "
"deal with non-contiguous arrays")
vectors.append(arg)
invocation_args.append(arg.data)
else:
invocation_args.append(arg)
queue = kwargs.pop("queue", None)
wait_for = kwargs.pop("wait_for", None)
if kwargs:
raise TypeError("too many/unknown keyword arguments")
repr_vec = vectors[0]
if queue is None:
queue = repr_vec.queue
invocation_args.append(repr_vec.mem_size)
gs, ls = repr_vec.get_sizes(queue,
self.kernel.get_work_group_info(
cl.kernel_work_group_info.WORK_GROUP_SIZE,
queue.device))
self.kernel.set_args(*invocation_args)
return cl.enqueue_nd_range_kernel(queue, self.kernel,
gs, ls, wait_for=wait_for)
def futhark_main(self, screenX_700, screenY_701, depth_702, xmin_703,
ymin_704, xmax_705, ymax_706):
res_707 = (xmax_705 - xmin_703)
res_708 = (ymax_706 - ymin_704)
y_711 = sitofp_i32_f32(screenX_700)
y_712 = sitofp_i32_f32(screenY_701)
x_713 = slt32(np.int32(0), depth_702)
bytes_902 = (np.int32(4) * screenY_701)
mem_903 = cl.Buffer(self.ctx, cl.mem_flags.READ_WRITE,
long(long(bytes_902) if (bytes_902 > np.int32(0)) else np.int32(1)))
mem_905 = cl.Buffer(self.ctx, cl.mem_flags.READ_WRITE,
long(long(bytes_902) if (bytes_902 > np.int32(0)) else np.int32(1)))
group_size_911 = np.int32(512)
num_groups_912 = squot32(((screenY_701 + group_size_911) - np.int32(1)),
group_size_911)
if ((np.int32(1) * (num_groups_912 * group_size_911)) != np.int32(0)):
self.map_kernel_894_var.set_args(np.float32(ymin_704), np.float32(y_712),
np.float32(res_708),
np.int32(screenY_701), mem_903, mem_905)
cl.enqueue_nd_range_kernel(self.queue, self.map_kernel_894_var,
(long((num_groups_912 * group_size_911)),),
(long(group_size_911),))
if synchronous:
self.queue.finish()
nesting_size_844 = (screenX_700 * screenY_701)
bytes_906 = (bytes_902 * screenX_700)
mem_908 = cl.Buffer(self.ctx, cl.mem_flags.READ_WRITE,
long(long(bytes_906) if (bytes_906 > np.int32(0)) else np.int32(1)))
group_size_917 = np.int32(512)
num_groups_918 = squot32((((screenY_701 * screenX_700) + group_size_917) - np.int32(1)),
group_size_917)
if ((np.int32(1) * (num_groups_918 * group_size_917)) != np.int32(0)):
self.map_kernel_846_var.set_args(np.int32(screenX_700),
np.int32(screenY_701), mem_905,
np.byte(x_713), np.int32(depth_702),
np.float32(xmin_703), mem_903,
np.float32(y_711), np.float32(res_707),
mem_908)
cl.enqueue_nd_range_kernel(self.queue, self.map_kernel_846_var,
(long((num_groups_918 * group_size_917)),),
(long(group_size_917),))
if synchronous:
self.queue.finish()
out_mem_909 = mem_908
out_memsize_910 = bytes_906
return (out_memsize_910, out_mem_909)
def execute(self): global_work_size = [self.outputSignalWidth*self.outputSignalHeight] local_work_size = [1] if (debug==1): print global_work_size print local_work_size kernel = self.program.convolve if (debug==1): print kernel.context print kernel.function_name print kernel.num_args print kernel.program print kernel.reference_count # Vecchia modalita' di creare un evento kernel.set_arg(0,self.inputSignalBuffer) kernel.set_arg(1,self.maskBuffer) kernel.set_arg(2,self.outputSignalBuffer) kernel.set_arg(3,self.inputSignalWidth) kernel.set_arg(4,self.maskWidth) self.event =cl.enqueue_nd_range_kernel(self.queue, kernel, global_work_size, local_work_size, global_work_offset=None, wait_for=None, g_times_l=True) if (debug==1): wgi = cl.kernel_work_group_info for dev in self.ctx.devices: print "-------",dev,"-------" print kernel.get_work_group_info(wgi.WORK_GROUP_SIZE,dev ) print kernel.get_work_group_info(wgi.COMPILE_WORK_GROUP_SIZE,dev ) print kernel.get_work_group_info(wgi.LOCAL_MEM_SIZE,dev ) print kernel.get_work_group_info(wgi.PRIVATE_MEM_SIZE,dev ) print kernel.get_work_group_info(wgi.PREFERRED_WORK_GROUP_SIZE_MULTIPLE,dev ) # Nuova modalita' di creare un evento self.event = kernel(self.queue,global_work_size,None, self.inputSignalBuffer,self.maskBuffer,self.outputSignalBuffer ,self.inputSignalWidth ,self.maskWidth) if (debug==1): print "context",self.event.context print "command_execution_status",self.event.command_execution_status print "command_queue",self.event.command_queue print "command_type",self.event.command_type print "reference_count",self.event.reference_count #print self.event.profile.end #print self.event.profile.queued #print self.event.profile.start #print self.event.profile.submit cl.enqueue_copy(self.queue, self.outputSignal, self.outputSignalBuffer) print self.inputSignal print self.mask print self.outputSignal
def minZerrKernSHG_gpu(self):
krn = self.progs.progs["minZerrSHG"].minZerrSHG
krn.set_scalar_arg_dtypes((None, None, None, None, None, None, None, None, np.int32))
krn.set_args(
self.Esig_t_tau_p_cla.data,
self.Et_cla.data,
self.dZ_cla.data,
self.X0_cla.data,
self.X1_cla.data,
self.X2_cla.data,
self.X3_cla.data,
self.X4_cla.data,
self.N,
)
ev = cl.enqueue_nd_range_kernel(self.q, krn, self.Et.shape, None)
ev.wait()
krn = self.progs.progs["normEsig"].normEsig
krn.set_scalar_arg_dtypes((None, None, np.int32))
krn.set_args(self.Esig_t_tau_p_cla.data, self.Esig_t_tau_norm_cla.data, self.N)
ev = cl.enqueue_nd_range_kernel(self.q, krn, self.Esig_t_tau_p.shape, None)
ev.wait()
mx = cla.max(self.Esig_t_tau_norm_cla).get() * self.N * self.N
# Esig_t_tau = self.Esig_t_tau_p_cla.get()
# mx = ((Esig_t_tau*Esig_t_tau.conj()).real).max() * self.N*self.N
X0 = cla.sum(self.X0_cla, queue=self.q).get() / mx
X1 = cla.sum(self.X1_cla, queue=self.q).get() / mx
X2 = cla.sum(self.X2_cla, queue=self.q).get() / mx
X3 = cla.sum(self.X3_cla, queue=self.q).get() / mx
X4 = cla.sum(self.X4_cla, queue=self.q).get() / mx
root.debug("".join(("X0=", str(X0), ", type ", str(type(X0)))))
root.debug(
"".join(("Poly: ", str(X4), " x^4 + ", str(X3), " x^3 + ", str(X2), " x^2 + ", str(X1), " x + ", str(X0)))
)
# Polynomial in dZ (expansion of differential)
X = np.array([X0, X1, X2, X3, X4]).astype(np.double)
root.debug("".join(("Esig_t_tau_p norm max: ", str(mx / (self.N * self.N)))))
return X
def prepare_device_memory(queue, kernels, buffers, flock):
"""
Initializes device memory and transfers
first flocks from host to the device.
"""
print("Initializing the memory and transferring the first flock.")
intermediary_events = [cl.enqueue_nd_range_kernel(
queue, kernels["k_init_memory"], [1], [1]),
cl.enqueue_copy(queue, buffers["global_generated_flocks"], flock.np_arrays)]
return intermediary_events
def prefixSumDownInplace(self, e, data, ndata, events):
import numpy as np
import pyopencl as cl
mf = cl.mem_flags
if not isinstance(data, cl.Buffer):
data_buf = cl.Buffer(self.ctx, mf.READ_WRITE | mf.COPY_HOST_PTR, hostbuf=data)
else:
data_buf = data
grid_dims = self.get_grid_dims(ndata)
psumbytes = int(np.prod(grid_dims) * np.uint64(0).nbytes)
npsumbytes = np.uint64(0).nbytes
psum_buf = cl.Buffer(self.ctx, mf.READ_WRITE, psumbytes)
npsum_buf = cl.Buffer(self.ctx, mf.READ_WRITE, npsumbytes)
kernel = self.prg.prefixSumDownInplace
kernel.set_args(data_buf, np.uint64(ndata), psum_buf, npsum_buf)
global_dims = self.get_global(grid_dims)
print "prefixSumDownInplace %s %s %d %d" % (str(global_dims), str(self.localDims), ndata, psumbytes)
if e is None:
e = ( cl.enqueue_nd_range_kernel(self.queue, kernel, global_dims, self.localDims, wait_for=e), )
else:
e = ( cl.enqueue_nd_range_kernel(self.queue, kernel, global_dims, self.localDims), )
events += e
npsum = np.zeros(1, dtype = np.uint64)
events += (cl.enqueue_copy(self.queue, npsum, npsum_buf, wait_for=e),)
if npsum>1:
(e, psum_buf, psum1_buf, npsum1_buf, ndata2) = self.prefixSumDownInplace(e, psum_buf, npsum.item(), events)
else:
ndata2 = np.zeros(1, dtype = np.uint64)
events += (cl.enqueue_copy(self.queue, ndata2, psum_buf, wait_for=e),)
ndata2 = ndata2.item()
print ndata2
self.prefixSumUp(e, data_buf, ndata, psum_buf, npsum, events)
return (e, data_buf, psum_buf, npsum_buf, ndata2)