Andreas,
Thanks for the quick reply.I was able to track that problem down to a syntax
error, but I've run into a problem launching kernels on gpu arrays allocated
from the device memory pool, as in the following code:
import numpy
import pycuda.autoinit
import pycuda.gpuarray as gpua
from pycuda.tools import DeviceMemoryPool as DMP
pool=DMP()
test=gpua.GPUArray((1,2),dtype=numpy.float32,allocator=pool.allocate)
print(test)
# [[ nan nan]]
print( int( test.gpudata))
# 30066083328
print( test.allocator)
# <bound method DeviceMemoryPool.allocate of <pycuda._driver.DeviceMemoryPool
object at 0x2909e68>>
# attempting to launch a kernel returns an error
test.fill(3.)
The error I get is as follows (depending on the kernel and the way it is
launched)
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File
"/usr/local/lib/python3.2/dist-packages/pycuda-2013.1.1-py3.2-linux-x86_64.egg/pycuda/gpuarray.py",
line 516, in fill
value, self.gpudata, self.mem_size)
File
"/usr/local/lib/python3.2/dist-packages/pycuda-2013.1.1-py3.2-linux-x86_64.egg/pycuda/driver.py",
line 475, in function_prepared_async_call
arg_buf = pack(func.arg_format, *args)
struct.error: required argument is not an integer
Manually specifying the allocator in the same way with pycuda.driver.mem_alloc
seems to work fine, though. Do you know what it might be?
Graham
________________________________________
From: Andreas Kloeckner [[email protected]]
Sent: Saturday, April 19, 2014 2:54 PM
To: Graham Mills; [email protected]
Subject: Re: [PyCUDA] Device memory pool
Hi Graham,
Graham Mills <[email protected]> writes:
> I am attempting to use a memory pool for some gpu array calculations,
> using PyCUDA 2013.1 with python 3.x and CUDA 5.5. The trouble is I
> can't find an appropriate integer type with which to call .allocate on
> a DeviceMemoryPool object. All numpy integers as well as the native
> python integer return the error:
>
>
> Boost.Python.ArgumentError: Python argument types in
> DeviceMemoryPool.allocate(numpy.uint32)
> did not match C++ signature:
> allocate(boost::shared_ptr<(anonymous
> namespace)::context_dependent_memory_pool<(anonymous
> namespace)::device_allocator> >, unsigned long)
>
> I'm not entirely clear yet on how the device memory pool works; am I
> doing something wrong? Does the associated boost module not work with
> the unified python 3 integer type?
I'm not sure that's it, as there are tests for DeviceMemoryPool in
PyCUDA's test suite [1], and they pass in Python 3. If you could try and
put together a minimal reproducer, I'd be happy to take a look.
Andreas
[1] https://github.com/inducer/pycuda/blob/master/test/test_driver.py#L343
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