CUDA.jl 3.3 ⋅ JuliaGPU

There have been several releases of CUDA.jl in the past couple of months, with many bugfixes and many exciting new features to improve GPU programming in Julia: CuArray now supports isbits Unions, CUDA.jl can emit debug info for use with NVIDIA tools, and changes to the compiler make it even easier to use the latest version of the CUDA toolkit.

Unions are a way to represent values of one type or another, e.g., a value that can be an integer or a floating point. If all possible element types of a Union are so-called bitstypes, which can be stored contiguously in memory, the Union of these types can be stored contiguously too. This kind of optimization is implemented by the Array type, which can store such "isbits Unions" inline, as opposed to storing a pointer to a heap-allocated box. For more details, refer to the Julia documentation.

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:

Leave a Comment