above. A mention must be made of Julia's GPU programming ecosystem, which includes: 1. CUDA.jl wraps the various CUDA libraries and supports compiling Julia kernels for Nvidia GPUs. 2. oneAPI.jl wraps the jl, Tullio.jl and ArrayFire.jl. In the following example we will use both DistributedArrays.jl and CUDA.jl to distribute an array across multiple processes by first casting it through distribute() and @everywhere $ ./julia -p 4 julia> addprocs() julia> @everywhere using DistributedArrays julia> using CUDA julia> B = ones(10_000) ./ 2; julia> A = ones(10_000) .* π; julia> C = 2 .* A ./ B; julia> all(C

above. A mention must be made of Julia's GPU programming ecosystem, which includes: 1. CUDA.jl wraps the various CUDA libraries and supports compiling Julia kernels for Nvidia GPUs. 2. oneAPI.jl wraps the jl, Tullio.jl and ArrayFire.jl. In the following example we will use both DistributedArrays.jl and CUDA.jl to distribute an array across multiple processes by first casting it through distribute() and @everywhere $ ./julia -p 4 julia> addprocs() julia> @everywhere using DistributedArrays julia> using CUDA julia> B = ones(10_000) ./ 2; julia> A = ones(10_000) .* π; julia> C = 2 .* A ./ B; julia> all(C

above. A mention must be made of Julia's GPU programming ecosystem, which includes: 1. CUDA.jl wraps the various CUDA libraries and supports compiling Julia kernels for Nvidia GPUs. 2. oneAPI.jl wraps the jl, Tullio.jl and ArrayFire.jl. In the following example we will use both DistributedArrays.jl and CUDA.jl to distribute an array across multiple processes by first casting it through distribute() and @everywhere $ ./julia -p 4 julia> addprocs() julia> @everywhere using DistributedArrays julia> using CUDA julia> B = ones(10_000) ./ 2; julia> A = ones(10_000) .* π; julia> C = 2 .* A ./ B; julia> all(C

above. A mention must be made of Julia's GPU programming ecosystem, which includes: 1. CUDA.jl wraps the various CUDA libraries and supports compiling Julia kernels for Nvidia GPUs. 2. oneAPI.jl wraps the jl, Tullio.jl and ArrayFire.jl. In the following example we will use both DistributedArrays.jl and CUDA.jl to distribute an array across multiple processes by first casting it through distribute() and @everywhere $ ./julia -p 4 julia> addprocs() julia> @everywhere using DistributedArrays julia> using CUDA julia> B = ones(10_000) ./ 2; julia> A = ones(10_000) .* π; julia> C = 2 .* A ./ B; julia> all(C

above. A mention must be made of Julia's GPU programming ecosystem, which includes: 1. CUDA.jl wraps the various CUDA libraries and supports compiling Julia kernels for Nvidia GPUs. 2. oneAPI.jl wraps the jl, Tullio.jl and ArrayFire.jl. In the following example we will use both DistributedArrays.jl and CUDA.jl to distribute an array across multiple processes by first casting it through distribute() and @everywhere $ ./julia -p 4 julia> addprocs() julia> @everywhere using DistributedArrays julia> using CUDA julia> B = ones(10_000) ./ 2; julia> A = ones(10_000) .* π; julia> C = 2 .* A ./ B; julia> all(C

above. A mention must be made of Julia's GPU programming ecosystem, which includes: 1. CUDA.jl wraps the various CUDA libraries and supports compiling Julia kernels for Nvidia GPUs. 2. oneAPI.jl wraps the jl, Tullio.jl and ArrayFire.jl. In the following example we will use both DistributedArrays.jl and CUDA.jl to distribute an array across multiple processes by first casting it through distribute() and @everywhere $ ./julia -p 4 julia> addprocs() julia> @everywhere using DistributedArrays julia> using CUDA julia> B = ones(10_000) ./ 2; julia> A = ones(10_000) .* π; julia> C = 2 .* A ./ B; julia> all(C

above. A mention must be made of Julia's GPU programming ecosystem, which includes: 1. CUDA.jl wraps the various CUDA libraries and supports compiling Julia kernels for Nvidia GPUs. 2. oneAPI.jl wraps the jl, Tullio.jl and ArrayFire.jl. In the following example we will use both DistributedArrays.jl and CUDA.jl to distribute an array across multiple processes by first casting it through distribute() and @everywhere $ ./julia -p 4 julia> addprocs() julia> @everywhere using DistributedArrays julia> using CUDA julia> B = ones(10_000) ./ 2; julia> A = ones(10_000) .* π; julia> C = 2 .* A ./ B; julia> all(C

above. A mention must be made of Julia's GPU programming ecosystem, which includes: 1. CUDA.jl wraps the various CUDA libraries and supports compiling Julia kernels for Nvidia GPUs. 2. oneAPI.jl wraps the jl, Tullio.jl and ArrayFire.jl. In the following example we will use both DistributedArrays.jl and CUDA.jl to distribute an array across multiple processes by first casting it through distribute() and @everywhere $ ./julia -p 4 julia> addprocs() julia> @everywhere using DistributedArrays julia> using CUDA julia> B = ones(10_000) ./ 2; julia> A = ones(10_000) .* π; julia> C = 2 .* A ./ B; julia> all(C

above. A mention must be made of Julia's GPU programming ecosystem, which includes: 1. CUDA.jl wraps the various CUDA libraries and supports compiling Julia kernels for Nvidia GPUs. 2. oneAPI.jl wraps the jl, Tullio.jl and ArrayFire.jl. In the following example we will use both DistributedArrays.jl and CUDA.jl to distribute an array across multiple processes by first casting it through distribute() and @everywhere $ ./julia -p 4 julia> addprocs() julia> @everywhere using DistributedArrays julia> using CUDA julia> B = ones(10_000) ./ 2; julia> A = ones(10_000) .* π; julia> C = 2 .* A ./ B; julia> all(C

above. A mention must be made of Julia's GPU programming ecosystem, which includes: 1. CUDA.jl wraps the various CUDA libraries and supports compiling Julia kernels for Nvidia GPUs. 2. oneAPI.jl wraps the jl, Tullio.jl and ArrayFire.jl. In the following example we will use both DistributedArrays.jl and CUDA.jl to distribute an array across multiple processes by first casting it through distribute() and @everywhere $ ./julia -p 4 julia> addprocs() julia> @everywhere using DistributedArrays julia> using CUDA julia> B = ones(10_000) ./ 2; julia> A = ones(10_000) .* π; julia> C = 2 .* A ./ B; julia> all(C