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  • Array programming · CUDA. jl - JuliaGPU
    The easiest way to use the GPU's massive parallelism, is by expressing operations in terms of arrays: CUDA jl provides an array type, CuArray, and many specialized array operations that execute efficiently on the GPU hardware In this section, we will briefly demonstrate use of the CuArray type
  • How to convert an array to a CuArray efficiently - GPU . . .
    cuImgGray = CuArray(imgGray) warns that this is a scalar operation and is slow If I set allowscalar() to false then the operation fails Now there must be a way to do this efficiently otherwise converting my images to the GPU compatible type would be absurdly long and would be no use at all
  • What does CuArray only supports element types that are . . .
    it's probably trying to make the outer container CuArray instead of Vector, if you look at this variable's type: the outer type is just Vector, it's a Vector of CuArray And more importantly, you cannot have a CuArray of CuArray because of the same reason outlined in the error message
  • CUDA. jl 3. 3 - juliabloggers. com
    With CUDA jl 3 3, the CuArray GPU array type now supports this optimization too That means you can safely allocate CuArrays with isbits union element types and perform GPU-accelerated operations on then: julia> a = CuArray([1, nothing, 3]) 3-element CuArray{Union{Nothing, Int64}, 1}: 1 nothing 3julia> findfirst(isnothing, a) 2
  • Overview · CUDA. jl - JuliaGPU
    The CUDA jl package provides three distinct, but related, interfaces for CUDA programming: the CuArray type: for programming with arrays; native kernel programming capabilities: for writing CUDA kernels in Julia; CUDA API wrappers: for low-level interactions with the CUDA libraries
  • CUDA. jl 2. 1 - JuliaGPU
    It is therefore recommended to use the DenseCuArray type alias for methods that need a CuArray backed by contiguous GPU memory For strided CuArray s, i e non-contiguous views, you should use the StridedCuArray alias
  • Dreaded CuArray only supports element types that are stored . . .
    I thought initially that every vector needed to be a CuArray, but don’t really only the ones that are going to be distributed need to be CuArray The database vector just needs to be looked up for each element of the CuArray time vector How do I represent it? Does that make sense?





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