> On Mar 31, 2017, at 4:38 PM, Tony Whyman <tony.why...@mccallumwhyman.com>
> wrote:
>
> For example, this distinction is very important in matrix algorithms. When
> operating on two matrices to produce another, the operations on each cell can
> be identified as n x m parallel actions at design time. At deployment time,
> it is often desirable to have a scalable implementation that can use anything
> from 1 to n x m processors to do the job. Thus you can have a design that
> identifies parallelism leading to an implementation that can non-parallel,
> partly or wholly parallel (in real time) depending on the size of the
> matrices and the number of processors available.
That’s a good candidate for parallelism but you need an API like OpenCL to
implement it properly so you can access the actual hardware required. From the
little time I spent with OpenCL you really don’t want to (or shouldn’t) be
intentionally designing your programs like this unless you have a real need for
“true” parallelism with multiple compute units.
Regards,
Ryan Joseph
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