Devectorize.jl

en you have a chain of expressions, and the operators in those expressions have implied loops. The naive thing to do is to myopically execute each one of those loops, creating and passing around temporary vectors. The Devectorize framework is given an entire expression, and is able to analyze where the implied loops are, and figures out how to express that computation in a single loop. We can tell these two concepts are independent because the ideal situation is to first use Devectorize on an expression, and then vectorize the result! For the record, the approach taken by Devectorize.jl is similar to the problem that expression templates (http://en.wikipedia.org/wiki/Expression_templates) in C++ try to solve. Through template trickery and operator overloading, we can put off evaluating an expression, avoiding the temporaries and unnecessary memory traversal that a naive execution implies. The Boost library uBLAS (micro Base Linear Algebra Subprograms, http://www.boost.org/doc/libs/1_55_0/libs/numeric/ublas/doc/...) uses this technique.