How can a large number of assignments to the same array cause a pyopencl.LogicError when run on GPU? - python-3.x

Question

I'm using pyOpenCL to do some complex calculations.
It runs fine on CPU, but I get an error when trying to run it on an NVIDIA GeForce 9400M (256 MB).
I'm working on Mac OS X Lion (10.7.5)
The strange thing is that this error does not always show up. It seems to occur when my calculations use larger numbers (resulting in larger iterations) but only when run on GPU.
I'm not writing to memory locations I'm not supposed to write to. I ruled out possible problems with concurrent modification by running the code as a single work item.
I simplified my OpenCL code as much as possible, and from what was left created some very simple code with extremely weird behavior that causes the pyopencl.LogicError. It consists of 2 nested loops in which a couple of assignments are made to the result array. This assignment need not even depend on the state of the loop.
This is run on a single thread (or work item, shape = (1,)) on the GPU.
__kernel void weirdError(__global unsigned int* result){
unsigned int outer = (1<<30)-1;
for(int i=20; i--; ){
unsigned int inner = 0;
while(inner != outer){
result[0] = 1248;
result[1] = 1337;
inner++;
}
outer++;
}
}
The strange part is that removing either one of the assignments to the result array removes the error. Also, decreasing the initial value for outer (down to (1<<20)-1 for example) also removes the error. In these cases, the code returns normally, with the correct result available in the corresponding buffer.
On CPU, it never raises an error.
The OpenCL code is run from Python using PyOpenCL.
Nothing fancy in the setup:
platform = cl.get_platforms()[0]
device = platform.get_devices(cl.device_type.GPU)[0]
context = cl.Context([device])
program = cl.Program(context, getProgramCode()).build()
queue = cl.CommandQueue(context)
In this Python code I set the result_buf to 0, then I run the calculation in OpenCL that will set its values in a large iteration. Afterwards I try to collect this value from the device memory, but that's where it goes wrong:
result = numpy.zeros(2, numpy.uint32)
result_buf = cl.Buffer(context, mem_flags.READ_WRITE | mem_flags.COPY_HOST_PTR, hostbuf=result)
shape = (1,)
program.weirdError(queue, shape, None, result_buf)
cl.enqueue_copy(queue, result, result_buf)
The last line gives me:
pyopencl.LogicError: clEnqueueReadBuffer failed: invalid command queue
How can this repeated assignment cause an error?
And more importantly: how can it be avoided?
I understand that this problem is probably platform dependent, and thus perhaps hard to reproduce. But this is the only machine I have access to, so the code should work on this machine.
DISCLAIMER: I have never worked with OpenCL (or CUDA) before. I wrote the code on a machine where the GPU did not support OpenCL. I always tested it on CPU. Now that I switched to GPU, I find it frustrating that errors do not occur consistently and I have no idea why.

Answer 1

My advice is to avoid such a long loops inside a kernel. Work Item is making over 1 billion of iterations, and that's a long shot. Probably, driver kills your kernel as it takes too much time to execute. Reduce the number of iterations to the maximal amount, which doesn't lead to error and look at the execution time. If it takes something like seconds - that's too much.
As you said, reducing iterations numbers solves the problem and that's the evidence in my opinion. Reducing the number of assignment operations also makes kernel runs faster as IO operations are usually the slowest.
CPU doesn't face such difficulties for obvious reasons.

Answer 2

This timeout problem can be fixed in Windows and Linux, but apparently not in Mac.
Windows
This answer to a similar question (explaining the symptoms in Windows) tells both what is going on and how to fix it:
This is a known "feature" under Windows (not sure about Linux) - if the video driver stops responding, the OS will reset it. Except that, since OpenCL (and CUDA) is implemented by the driver, a kernel that takes too long will look like a frozen driver. There is a watchdog timer that keeps track of this (5 seconds, I believe).
Your options are:
You need to make sure that your kernels are not too time-consuming
(best).
You can turn-off the watchdog timer: Timeout Detection and Recovery of GPUs.
You can run the kernel on a GPU that is not hooked up to a display.
I suggest you go with 1.
This answer explains how to actually do (2) in Windows 7. But the MSDN-page for these registry keys mentions they should not be manipulated by any applications outside targeted testing or debugging. So it might not be the best option, but it is an option.
Linux
(From Cuda Release Notes, but also applicable to OpenCL)
GPUs without a display attached are not subject to the 5 second run time restriction. For this reason it is recommeded that CUDA is run on a GPU that is NOT attached to an X display.
While X does not need to be running in order to use CUDA, X must have been initialized at least once after booting in order to properly load the NVIDIA kernel module. The NVIDIA kernel module remains loaded even after X shuts down, allowing CUDA to continue to function.
Mac
Apple apparently does not allow fiddling with this watchdog and thus the only option seems to be using a second GPU (without a screen attached to it)