synchronizing writes through a host-mapped array?
by Karl Czajkowski
I am experimenting with convolution kernels for 3D arrays. I have a
scalar version that seems to work, but as I went about trying to
reduce extra data copies, I ran into problems. I isolated the change
in my Python code to two version that get the source Numpy array into
a PyOpenCL array, before calling the exact same OpenCL program.
First, the data-loading that seems to work on every driver:
# src is a sliced view on a larger Numpy array
src = src.astype(float32, copy=True)
src_dev = cl_array.to_device(clq, src)
This version makes an explicit host copy to consolidate the source
data into one contiguous buffer. The to_device() call would otherwise
throw an exception if called on the source.
Second, the version that works faster on Intel and AMD CPU drivers but
shows non-determinism on the NVIDIA GPU driver:
# src is a sliced view on a larger Numpy array
src = src.astype(float32, copy=False)
src_dev = cl_array.empty(clq, src.shape, float32)
src_tmp = src_dev.map_to_host()
src_tmp[...] = src[...]
This version avoids the host copy because the original source is
already in float32 format, and then it consolidates the data while it
is copied into the host-mapped buffer.
My tests have suggested that my OpenCL program is racing with the host
to GPU data copy, as it seems to see the leading portion of the
src_dev array filled with proper values while the trailing portion
looks uninitialized. Through repeated tests with varying problem
sizes, I managed to observe this on some rather small test arrays that
I could inspect manually.
Is there some other synchronization call that I am supposed to make
when writing into a host-mapped array as above? Or does this look
like a bug in the interaction between PyOpenCL and the NVIDIA OpenCL
driver?
Thanks,
Karl
5 years
simple downsampling does not work
by Daniel Haehn
Hi all,
I have this simple code to downsample an image using linear interpolation
but I am stuck:
import pyopencl as cl
import numpy as np
import cv2 # OpenCV 2.3.1
# load a 512x512 image
Img = cv2.imread("testsmall.jpg", cv2.CV_LOAD_IMAGE_GRAYSCALE)
OutImg = np.empty(shape=(Img.shape[0]/2,Img.shape[1]/2), dtype=np.uint8) #
create Output-Image
ctx = cl.create_some_context()
queue = cl.CommandQueue(ctx)
mf = cl.mem_flags
dev_Img = cl.Image(ctx,
mf.READ_ONLY | mf.USE_HOST_PTR,
cl.ImageFormat(cl.channel_order.R,
cl.channel_type.UNSIGNED_INT8),
hostbuf=Img)
dev_OutImg = cl.Image(ctx,
mf.WRITE_ONLY,
cl.ImageFormat(cl.channel_order.R,
cl.channel_type.UNSIGNED_INT8),
shape=(Img.shape[0]/2,Img.shape[1]/2))
prg = cl.Program(ctx, """
const sampler_t smp = CLK_NORMALIZED_COORDS_TRUE |
CLK_FILTER_LINEAR | CLK_ADDRESS_CLAMP_TO_EDGE;
__kernel void ImageDS(__read_only image2d_t Img, __write_only
image2d_t Out)
{
const int2 Coords = (int2)(get_global_id(0), get_global_id(1));
write_imagef(Out, Coords, read_imagef(Img, smp, Coords));
}
""").build()
prg.ImageDS(queue, Img.shape, None, dev_Img, dev_OutImg)
cl.enqueue_read_image(queue, dev_OutImg, (0, 0), OutImg.shape,
OutImg).wait()
cv2.imwrite("out.jpg", OutImg)
The error is:
Traceback (most recent call last):
File "pycltesttex.py", line 44, in <module>
cl.enqueue_read_image(queue, dev_OutImg, (0, 0), OutImg.shape,
OutImg).wait()
File "/Library/Python/2.7/site-packages/pyopencl/__init__.py", line 902,
in new_func
return func(*args, **kwargs)
pyopencl.LogicError: clEnqueueReadImage failed: invalid value
Not sure what I am doing wrong with the enqueue_read_image call. Does
anybody know?
Thanks a ton!
Daniel
5 years, 1 month
OpenCL on Nvidia GRID K520
by Ivan I. grushin
Hello!
I've tried to use Amazon's AWS g2.2xlarge instance and found that it
works with half of it's calculating power.
Then i found that DeviceInfo.py shows that platform.version is "OpenCL
1.1 CUDA 6.5.20", when the same script on AMD card shows "OpenCL 1.2"
Does this version difference affect the behaviour of K520 ?
According to GRID K520 parameters, it is 2 GPUs in one product. Does
OpenCL 1.1 see only 1 GPU on K520?
How can i load K520 to its full power?
Thank you.
5 years, 1 month
