This is the Linux app named ParallelStencil.jl whose latest release can be downloaded as ParallelStencil.jl0.14.3sourcecode.tar.gz. It can be run online in the free hosting provider OnWorks for workstations.
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ParallelStencil.jl
DESCRIPTION
ParallelStencil empowers domain scientists to write architecture-agnostic high-level code for parallel high-performance stencil computations on GPUs and CPUs. Performance similar to CUDA C / HIP can be achieved, which is typically a large improvement over the performance reached when using only CUDA.jl or AMDGPU.jl GPU Array programming. For example, a 2-D shallow ice solver presented at JuliaCon 2020 [1] achieved a nearly 20 times better performance than a corresponding GPU Array programming implementation; in absolute terms, it reached 70% of the theoretical upper performance bound of the used Nvidia P100 GPU, as defined by the effective throughput metric, T_eff. ParallelStencil relies on the native kernel programming capabilities of CUDA.jl and AMDGPU.jl and on Base.Threads for high-performance computations on GPUs and CPUs, respectively. It is seamlessly interoperable with ImplicitGlobalGrid.jl, which renders the distributed parallelization of stencil-based GPU and CPU apps.
Features
- Parallelization and optimization with one macro call
- Stencil computations with math-close notation
- Seamless interoperability with communication packages and hiding communication
- Support for architecture-agnostic low level kernel programming
- Module documentation callable from the Julia REPL / IJulia
- Concise single/multi-xPU miniapps
Programming Language
Julia
Categories
This is an application that can also be fetched from https://sourceforge.net/projects/parallelstencil-jl.mirror/. It has been hosted in OnWorks in order to be run online in an easiest way from one of our free Operative Systems.
