I think you could draw somewhat different lessons from this, though: - Cell failed because it required too much work and was too incompatible with any other pl

I think you could draw somewhat different lessons from this, though: - Cell failed because it required too much work and was too incompatible with any other platform (save for DSPs). - Larrabee failed because KNC wasn't binary compatible and the toolchain was too much of a pain to work with, and KNL/KNM "failed" because they were too hard to get (although, I think "failed" is perhaps too strong here, the "lots of general-purpose cores with vector units" is exactly where Intel, AMD, and the aarch64 vendors have gone since). KNC's lack of binary compatibility is really what got things off on the wrong foot, the whole advantage it had over AMD/Nvidia GPUs was it amenability to GPU-unfriendly code, but I think the barrier of having to rebuild everything was prohibitive for many of the smaller users who would have been well-positioned to use it. - NVidia "won" in the GPGPU space because CUDA and its libraries have provided a stable-enough basis for complex applications to use, and the underlying hardware has evolved to be ever more performant and capable. - AMD "failed" in the GPGPU space because their toolchain abstraction is too weak and compatibility is poor. Unlike CUDA, there's no capable API that works on the whole range (even with OpenCL AMD's support is questionable at times, with two totally different driver stacks). I'd say the biggest lessons are: (1) compatibility with existing code is king, and (2) toolchain and hardware accessibility is critical. You can survive breaking (1) if your tools are good enough, but you can't win by breaking both of them.