but more efficient computational model exists. if cpu consists from
huge amount of execution engines which synchronizes their operations
only when one unit uses results produces by another then we got
processor with huge level of natural parallelism and friendlier for FP
programs. it seems that now we move right into this direction with GPUs
GPU's are pretty normal processors. They are different in that they
usually write into a single predetermined memory location and fetch from
texture memory with floating point indexes, so called "gather" operation
mode.
The dataflow approach ("scatter" approach) splits into static dataflow
with limited parallelism and dynamic dataflow with huge potential
parallelism.
Dynamic dataflow approach (currently investigated by our research team I
am proud member of) requires substantial hardware support in terms of
associative memory for matching computation contexts.
Also, "huge amount of execution engines" should be connected somehow.
Connection network is also a non-trivial task.
Problems, problems, problems, problems and no easy solution. ;)
BTW, second of our modeling engines was written in Haskell. ;)
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