Since returning to MIT, I've been getting some friends together to build computer systems on heterogeneous fabrics. There really isn't a good framework for load balancing on anything but symmetric multiprocessor environments, and we really need a good way to profile and partition applications across multicore, Cell, GPU and FPGA environments.
Of course the question of which accelerator to use is extremely dependent on profiling tools and compilers, but more importantly, scalability and "future proof"-ness. Here is where I think FPGAs will win since they will continue to be technology drivers and will lead the way to 45nm and below. One other nice facet to having a data center full of FPGAs, is that you can identify useful hardware structures and provide feedback to manufacturers on which features to "harden" into the next fabric.
More important than developing the framework for accelerating applications is actually delivering on the applications even using existing tools. I see a convergence between utility computing (data center outsourcing) and the accelerator co-processor markets: accelerators will benefit from utility computing (where higher utilization will ammortize hardware investment) and data centers will benefit from accelerators (increased GOPS/$).
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Most, if not all, of today's profiling wisdom is based on the premise that the target code will be tweaked in-place and run on the same CPU. Are there accleration-aware profiling techniques and profiling metrics other than just execution time, e.g., amount of data touched or written. What are other profiling metrics matter for offload to reconfigurable fabrics?
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