After Julia's multithreading is ready for release, we will have a native path to interface with it. This will hopefully enable multiple shm execution paths for users to experiment with.
On Wednesday, October 21, 2015 at 9:12:50 AM UTC-5, Steven Sagaert wrote: > > 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) >> >>
