Julia Computing and GPU Acceleration

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Julia is a high-level programming language for mathematical computing that’s as straightforward to make use of as Python, however as quick as C. The language has been created with efficiency in thoughts, and combines cautious language design with a complicated LLVM-based compiler [Bezanson et al. 2017].

Julia is already properly regarded for programming multicore CPUs and enormous parallel computing methods, however latest developments make the language fitted to GPU computing as properly. The efficiency prospects of GPUs will be democratized by offering extra high-level instruments which can be straightforward to make use of by a big neighborhood of utilized mathematicians and machine studying programmers. On this weblog publish, I’ll give attention to native GPU programming with a Julia bundle that enhances the Julia compiler with native PTX code technology capabilities: CUDAnative.jl.


The Julia bundle ecosystem already accommodates fairly just a few GPU-related packages, concentrating on completely different ranges of abstraction. On the highest abstraction stage, domain-specific packages like MXNet.jl and TensorFlow.jl can transparently use the GPUs in your system. Extra generic growth is feasible with ArrayFire.jl, and for those who want a specialised CUDA implementation of a linear algebra or deep neural community algorithm you should use vendor-specific packages like cuBLAS.jl or cuDNN.jl. All these packages are basically wrappers round native libraries, making use of Julia’s international operate interfaces (FFI) to name into the library’s API with minimal overhead. For extra info, take a look at the JuliaGPU GitHub group which hosts many of those packages.

About Julia and Julia Computing

Julia is the quickest high-performance open supply computing language for knowledge, analytics, algorithmic buying and selling, machine studying, synthetic intelligence, and different scientific and numeric computing purposes. Julia solves the 2 language drawback by combining the convenience of use of Python and R with the pace of C++. Julia offers parallel computing capabilities out of the field and limitless scalability with minimal effort. Julia has been downloaded greater than 11 million instances and is used at greater than 1,500 universities. Julia co-creators are the winners of the 2019 James H. Wilkinson Prize for Numerical Software program and the 2019 Sidney Fernbach Award. Julia has run at petascale on 650,000 cores with 1.three million threads to research over 56 terabytes of information utilizing Cori, one of many ten largest and strongest supercomputers on the planet.

Julia Computing was based in 2015 by all of the creators of Julia to supply merchandise together with JuliaTeam, JuliaSure and JuliaRun to companies and researchers utilizing Julia.

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