Hey,
2014/1/25 Karl Rupp <r...@iue.tuwien.ac.at>
> Hey hey hey,
>
>
> > Convergence depends on what is inside generate_execute() ;-) How is
>
>> the problem with alpha and beta residing on the GPU addressed? How
>> will the batch-compilation look like? The important point is that
>> for the default axpy kernels we really don't want to go through the
>> jit-compiler for each of them individually.
>>
>>
>> ;)
>> in this case, generate_execute() will just trigger the compilation - on
>> the first call only - of the kernel
>> x = cpu_alpha*y + cpu_beta*z;
>>
>> __kernel void kernel(unsigned int N, float4* x, float4* y, float4* z,
>> float alpha, float beta)
>> {
>> for(i = get_global_id(0) ; i < N ; i+=get_global_size(0))
>> x[i] = alpha*y[i] + beta*z[i];
>> }
>>
>
> I'm afraid this is not suitable then. A simple conjugate gradient solver
> would then go through ~10 OpenCL compilations, making it awfully slow at
> the first run... With AMD and Intel SDKs, which to my knowledge still do
> not buffer kernels, this would mean that each time a process is started,
> this large overhead will be visible.
>
>
I don't understand why this would go through more than one compilation...
This kernel is compiled only once, the value of flip_sign and reciprocal
only changes the dynamic value of the argument, not the source code.
This would eventually result in:
if(alpha_reciprocal)
kernel(N,x,y,z,1/alpha,beta)
Am I missing something?
Best regards,
Philippe
Best regards,
> Karli
>
>
>
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