Hi,
> I prefer option 3. This would allow for something like :
>
> if(size(x)>1e5 && stride==1 && start==0){
Here we also need to check the internal_size to fit the vector width
>
> //The following steps are costly for small vectors
> NumericT cpu_alpha = alpha //copy back to host when the scalar is on
> global device memory)
> if(alpha_flip) cpu_alpha*=-1;
> if(reciprocal) cpu_alpha = 1/cpu_alpha;
> //... same for beta
>
> //Optimized routines
> if(external_blas)
> call_axpy_twice(x,cpu_alpha,y,cpu_beta,z)
> else{
> generate_execute(x = cpu_alpha*y + cpu_beta*z);
> }
> else{
> //fallback
> }
>
> This way, we at most generate two kernels, one for small vectors,
> designed to optimize latency, and one for big vectors, designed to
> optimize bandwidth. Are we converging? :)
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.
Note to self: Collect some numbers on the costs of jit-compilation for
different OpenCL SDKs.
Best regards,
Karli
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