Great news! Apart from the focus on parallelism, the architecture seems similar to numba <http://numba.pydata.org/>. Good to finally have shared memory parallelism in Julia (not counting the shared memory array under Linux via shm). I wonder how this is going to interact with the upcoming multithreading in julia in the future. Will they play nice together or fight each other?
Sincerely, Steven Sagaert On Wednesday, October 21, 2015 at 2:57:17 AM UTC+2, 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) > >
