This is fantastic work! Thanks and congratulations to the team at Intel.
On Tuesday, October 20, 2015 at 8:57:17 PM UTC-4, Lindsey Kuper wrote: > > The High Performance Scripting team at Intel Labs is pleased to announce > the release of version 0.1 of ParallelAccelerator.jl, a package for > high-performance parallel computing in Julia. > > ParallelAccelerator provides an @acc (short for "accelerate") macro for > annotating Julia functions. Together with a system C compiler (ICC or > GCC), it compiles @acc-annotated functions to optimized native code. > > Under the hood, ParallelAccelerator is essentially a domain-specific > compiler written in Julia. It performs additional analysis and optimization > on top of the Julia compiler. ParallelAccelerator discovers and exploits > the implicit parallelism in source programs that use parallel programming > patterns such as map, reduce, comprehension, and stencil. For example, > Julia array operators such as .+, .-, .*, ./ are translated by > ParallelAccelerator internally into data-parallel map operations over all > elements of input arrays. For the most part, these patterns are already > present in standard Julia, so programmers can use ParallelAccelerator to > run the same Julia program without (significantly) modifying the source > code. > > Version 0.1 should be considered an alpha release, suitable for early > adopters and Julia enthusiasts. Please file bugs at > https://travis-ci.org/IntelLabs/ParallelAccelerator.jl/issues . > > ParallelAccelerator requires Julia v0.4.0. See our GitHub repository at > https://github.com/IntelLabs/ParallelAccelerator.jl for a complete list > of prerequisites, supported platforms, example programs, and documentation. > > Thanks to our colleagues at Intel and Intel Labs, the Julia team, and the > broader Julia community for their support of our efforts! > > Best regards, > The High Performance Scripting team > (Programming Systems Lab, Intel Labs) > >
