Author Topic: High-Performance GPU Computing in the Julia Programming Language  (Read 1955 times)

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JeGX

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Julia is a high-level programming language for mathematical computing that is as easy to use as Python, but as fast as C. The language has been created with performance in mind, and combines careful language design with a sophisticated LLVM-based compiler [Bezanson et al. 2017].

Julia is already well regarded for programming multicore CPUs and large parallel computing systems, but recent developments make the language suited for GPU computing as well. The performance possibilities of GPUs can be democratized by providing more high-level tools that are easy to use by a large community of applied mathematicians and machine learning programmers. In this blog post, I will focus on native GPU programming with a Julia package that enhances the Julia compiler with native PTX code generation capabilities: CUDAnative.jl.

Link:
https://devblogs.nvidia.com/parallelforall/gpu-computing-julia-programming-language/