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.
Best regards,
Karli
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