Lev,
Thanks for the explanation...that definitely helps. But how does the indexing
work for a 2d case?
z1 = numpy.zeros((1024)).astype(numpy.float32)
kernel1(drv.Out(z1),block=(16,16,1),grid=(2,2))
int idx=??
int idy =??
Thanks!
Mike
________________________________
From: Lev Givon <l...@columbia.edu>
To: Mike Tischler <mikethesoils...@yahoo.com>
Cc: pycuda@tiker.net
Sent: Thu, March 24, 2011 6:13:54 PM
Subject: Re: [PyCUDA] index multiple blocks and grids
Received from Mike Tischler on Thu, Mar 24, 2011 at 03:41:30PM EDT:
> Hi,
> I'm new to CUDA and PyCUDA, and have having a problem indexing multiple
> grids.
>
> I'm using an older CUDA enabled card (Quadro FX 1700) before I begin writing
>for
>
> a larger GPU. I've been trying to understand the relationship between
> threads,
>
> blocks, and grids in the context of my individual card. To do so, I've set
> up
>a
>
> simple script.
(snip)
> However, what if I have an array that's 1024 in length? If I understand the
> documentation correctly, block=(16,16,1) is the max value (256 threads)
> allowed
>
> for my hardware, which means I have to increase the number of grids. If I
> change the parameters of my script to:
>
> z1 = numpy.zeros((1024)).astype(numpy.float32)
> kernel1(drv.Out(z1),block=(16,16,1),grid=(2,2))
>
> How do I correctly index the array locations in my kernel function given
> multiple grids (z1[???]=???) ? There is a gridDim property, but not gridIdx
> property, like with threads and blocks.
>
>
> Thanks!
> Mike
threadIdx identifies the thread in a single block. To access a 1D
array of 1024 elements assuming a maximum of 256 threads per block,
you can combine the values in threadIdx and blockIdx, e.g.,
int idx = blockIdx.x*blockDim.x + threadIdx.x;
and launch the kernel with a thread block with dimensions (256, 1, 1) and a
grid with dimensions (4, 1). See Chapter 2 of the CUDA Programming
Guide for more info.
L.G.
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