
I'm personally pessimistic about the STI Cell, but it's a reasonable example wrt the difficulty of writing the backends. Barring the data and message orchestration, which also aflicts GPUs, there's the decision function as to when to migrate a computation to the accelerator (GPU, SPU, ...) Not so much of a problem in chip symmetric mcore, but gets pretty dicey for hybrid mcore.
Been trying to wrap my head around that problem for a couple of years.
-scooter
Sent from my Verizon Wireless BlackBerry
-----Original Message-----
From: Jeff Heard
On Wed, Feb 3, 2010 at 12:34 PM, Jeff Heard
wrote: [...] One thing though, is that CUDA is being supplanted by OpenCL in the next few years, and OpenCL can handle data parallelism on multicore CPUs as well as GPUs with the same code. It's a little more flexible overall than CUDA, and will be portable across ATI/nVidia/Intell/AMD/Sony Cell in the end, and is well supported on Linux, Mac, and Windows systems. [...]
I have been told by NVIDIA folks that CUDA is not going to disappear. Its C++ API (as opposed to its ``driver interface'') is quite a bit higher level than OpenCL, suitable for (painful) application development, whereas OpenCL seems more targeted for compiler and framework writers.
Sincerely, Brad
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