There seems to be a myth going around that vectorized code in Julia is slow. That's not really the case. Often times it's just that devectorized code is faster because one can manually perform operations such as loop fusion, which the compiler cannot currently reason about (and most C compilers can't either). In some other languages those benefits get drowned out by language overhead, but in julia those kinds of constructs are generally fast. The cases where julia can be slower is when there is excessive memory allocation in a tight inner loop, but those cases can usually be rewritten fairly easily without losing the vectorized look of the code.
On Thu, May 12, 2016 at 1:35 AM, Kristoffer Carlsson <kcarlsso...@gmail.com> wrote: > It is always easier to discuss if there is a piece of code to look at. Could > you perhaps post a few code examples that does not run as fast as you want? > > Also, make sure to look at : > https://github.com/IntelLabs/ParallelAccelerator.jl. They have a quite > sophisticated compiler that does loop fusions and parallelization and other > cool stuff. > > > > On Thursday, May 12, 2016 at 7:22:24 AM UTC+2, Anonymous wrote: >> >> This remains one of the main drawbacks of Julia, and the devectorize >> package is basically useless as it doesn't support some really crucial >> vectorized operations. I'd really prefer not to rewrite all my vectorized >> code into nested loops if at all possible, but I really need more speed, can >> anyone tell me the timeline and future plans for making vectorized code run >> at C speed?
