Thank you for this work! I am particularly interested in working with it for the Xeon Phi. I haven't actually gotten to do extensive tests of the work from https://github.com/IntelLabs/CompilerTools.jl/issues/1 yet. Will be doing this over the summer.
I am trying to incorporate it into DifferentialEquations.jl to speed up some routines. Also will probably use it in VectorizedRoutines.jl. One issue I am having is dealing with ParallelAccelerator as a conditional dependency: I want to add the @acc macro only when the user has the package installed (and working?). This is crucial since the package does work for Windows as well. Conditionally applying macros and packages is difficult. On Tuesday, July 12, 2016 at 1:23:05 PM UTC-7, Todd Anderson wrote: > > Hello, > > I'm one of the developers of the Intel ParallelAccelerator package for > Julia. https://github.com/IntelLabs/ParallelAccelerator.jl > > Now that the package has been out for a while, I'd like to poll the user > community. > > 1) Who has used the package to accelerate some real application that they > are working on? If you fall into this category, please drop us a note. > 2) If you tried the package but it didn't work for some reason or you need > support for some feature also please let us know. Soon after Julia 0.5 is > released we will be releasing an updated version of ParallelAccelerator > with support for parallelization via threading through regular Julia > codegen. By going through Julia codegen, code coverage will be greatly > improved. Our current path through C++ with openmp has several > restrictions about what Julia features can be converted to C and most of > these restrictions are therefore lifted by going through native Julia > codegen. > 3) If you haven't heard about ParallelAccelerator before and you have an > application that is array or stencil oriented and you would like to see if > it can be automatically parallelized then please check out our package. > > thanks, > > Todd > > >
