Time to Embrace New Data Parallel Programming Models?

In "The rise and fall of High Performance Fortran: An Historical Object Lesson" there's a several lessons to be learned but of significant relevance to the development and adoption of Functional Programming Languages in finance was the promise offered by data parallel architectures.

Data parallelism is the ability to run sequences of instructions which are broadcast to processing units in lock-step, on different elements of data in parallel. This was first implemented in hardware in the 1960's and was classifed as SIMD - Single Instruction Multiple Data

This is the mechanism is now in implemented in software and part of the Glasgow Haskell Compiler and is explored in detail in the presentation "Nested Data Parallelism in Haskell" where they state that it's the only way to take advantage of 100's of cores and that it can even be run on GPUs. Like Handel-C, Haskell has par and seq statements allowing the programmer to parallelise and sequentialise instructions. They also introduce a new concept called stream fusion which is non-recursive allowing data to be "bused"from one stream to another inline - now I'm not sure I fully understand the implications of this but I'm sure going to find out.