Based on the prior comments, I think I understand the problem now. Complex arrays on the GPU take up the same amount of memory as on the GPU, but (especially in R2014a), it can be difficult to see that for various reasons. On my machine using R2014a, the following steps:
d = gpuDevice(1);
f1 = d.FreeMemory;
gcx = repmat(gpuArray(1i), 1000, 1000);
f2 = d.FreeMemory;
bytesPerElement = (f1 - f2) / (1000*1000)
demonstrate that a complex gpuArray uses 16 bytes per element, just like on the CPU.
Now, there are two subtleties here that I think are getting in the way of what you're actually trying to achieve:
- MATLAB has a memory re-use scheme on the GPU that causes the values returned in FreeMemory to be misleading. FreeMemory shows the number of bytes actually free on the GPU, but it doesn't reflect how much memory is actually available to MATLAB to create new gpuArrays. This is why later releases have a property AvailableMemory, which reflects how many bytes are available to make new gpuArrays.
- When transferring an array from the CPU to the GPU, there's a format conversion required. MATLAB on the CPU stores complex data as two separate allocations - the real part, and the imaginary part. On the GPU, the data is stored in a single interleaved allocation. The transformation from split-complex to interleaved-complex is performed on the GPU, and this requires extra space. Therefore, the maximum complex array that can be transferred from the CPU to the GPU is roughly half the total GPU memory size.
You can avoid the problem in (2) if it is possible to construct the array directly on the GPU (as I did in my example) - however I appreciate that's not always possible.
