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CUDAnative v1.0 release notes

This document describes major features and user-facing changes to CUDAnative.

New features

  • @device_code_... macros make it easy to inspect generated device code even if the outermost function call isn't a @cuda invocation. This is especially useful in combination with, e.g., CuArrays. The @device_code macro dumps all forms of intermediate code to a directory, for easy inspection ([#147]).

  • Fast versions of CUDA math intrinsics are now wrapped ([#152]).

  • Support for loading values through the texture cache, aka. __ldg, has been added. No getindex-based interfaced is available yet, manually use unsafe_cached_load instead ([#158]).

  • Multiple devices are supported, by calling device! to switch to another device. The CUDA API is now also initialized lazily, so be sure to call device! before performing any work to avoid allocating a context on device 0 ([#175]).

  • Support for object and closure kernel functions has been added ([#176]).

  • IR transformation passes have been introduced to rewrite exceptions, where possible, to generate user-friendly messages as well as prevent hitting issues in ptxas ([#241]).

  • Code generated by @cuda can now be recreated manually using a low-level kernel launch API. The kernel objects used in that API are useful for reflecting on hardware resource usage ([#266]).

  • A GPU runtime library has been introduced ([#303]), implementing certain functionality from the Julia runtime library that would previously have prevented GPU execution ([#314], [#318], [#321]).

Changes

  • Debug info generation now honors the -g flag as passed to the Julia command, and is no longer tied to the DEBUG environment variable.

  • Log messages are implemented using the new Base Julia logging system. Debug logging can be enabled by specifying the JULIA_DEBUG=CUDAnative environment variable.

  • The syntax of @cuda now takes keyword arguments, eg. @cuda threads=1 foo(...), instead of the old tuple syntax. See the documentation of @cuda for a list of supported arguments ([#154]).

  • Non isbits values can be passed to a kernel, as long as they are unused. This makes it easier to implement GPU-versions of existing functions, without requiring a different method signature ([#168]).

  • Indexing intrinsics now return Int, so no need to convert to (U)Int32 anymore. Although this might require more registers, it allows LLVM to simplify code ([#182]).

  • Better error messages, showing backtraces into GPU code (#189) and detecting common pitfalls like recursion or use of Base intrinsics (#210).

  • Debug information is now stripped from LLVM and PTX reflection functions ([#208], [#214]). Use the strip_ir_metadata (cfr. Base) keyword argument to disable this.

  • Error handling and reporting has been improved. This includes GPU-incompatible ccalls which are now detected and decoded by the IR validator ([#248]).

  • A callback mechanism has been introduced to inform downstream users about device switches ([#226]).

  • Adapt.jl is now used for host-device argument conversions ([#269]).

Deprecations and removals

  • CUDAnative.@profile has been removed, use CUDAdrv.@profile with a manual warm-up step instead.

  • The KernelWrapper has been removed since it prevented inferring varargs functions ([#254]).

  • Support for CUDAdrv.CuArray has been removed, the CuArrays.jl package should be used instead ([#284]).