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+title = "Metal.jl 1.4: Improved random numbers"
+author = "Christian Guinard"
+abstract = """
+  Metal.jl 1.4 adds higher-quality random number generators from the Metal Performance
+  Shaders library. Some limitations apply, with a fallback to the current implementation
+  in those situations."""
++++
+{{abstract}}
+
+
+## `Metal.rand` and friends
+
+Using functionality provided by the Metal Performance Shaders (MPS) library, Metal.jl now
+comes with much improved GPU random number generators. Uniform distributions using
+`Metal.rand` (and its in-place variant `Metal.rand!`) are available for all Metal-supported
+integer types and `Float32`. However, due to [Metal API
+limitations](https://developer.apple.com/documentation/metal/mtlblitcommandencoder/1400767-copyfrombuffer?language=objc),
+8-bit and 16-bit integers may fall back to the lower-quality GPUArrays.jl random number
+generator if their size in bytes is not a multiple of 4. Normally distributed `Float32`
+values can be generated for with `Metal.randn` and `Metal.randn!`, while `Float16` is not
+supported by the MPS library and will always fall back to the GPUArrays implementation.
+
+The easiest way to use these is to use the Metal convenience functions `Metal.rand[n][!]` as
+you would the usual functions from the Random.jl standard library:
+
+```julia-repl
+julia> a = Metal.rand(Float32, 2)
+2-element MtlVector{Float32, Metal.PrivateStorage}:
+ 0.95755994
+ 0.7110207
+
+julia> Metal.randn!(a)
+2-element MtlVector{Float32, Metal.PrivateStorage}:
+ 1.7230463
+ 0.55636907
+```
+
+However, the Random.jl methods can also be used by providing the appropriate `RNG` either
+from `MPS.default_rng()` or `MPS.RNG()` to the standard `Random.rand[n][!]` functions:
+
+
+```julia-repl
+julia> using Random
+
+julia> rng = MPS.RNG();
+
+julia> Random.rand(rng, 2)
+2-element MtlVector{Float32, Metal.PrivateStorage}:
+ 0.8941469
+ 0.67628527
+```
+
+Seeding is done by calling `Metal.seed!` for the global RNG, or `Random.seed!` when working
+with an explicit `RNG` object.
+
+
+## Other improvements since the last blog post
+
+- Since v0.5: `MtlArray` storage mode has been parameterized, allowing one to create a
+  shared storage `MtlArray` by calling `MtlArray{eltype, ndims, Metal.SharedStorage}(...)`.
+- Since v0.3: MPS-accelerated decompositions were added.
+- Various performance improvements
+- *Many* bug fixes.
+
+
+## Future work
+
+Although Metal.jl is now in v1, there is still work to be done to make it as fast and
+feature-complete as possible. In particular:
+
+- Metal.jl is now using native ObjectiveC FFI for wrapping Metal APIs. However, these
+  wrappers have to be written manually for every piece of Objective-C code. *We are looking
+  for help with improving Clang.jl and ObjectiveC.jl* to [enable the automatic generation of
+  these wrappers](https://github.com/JuliaInterop/ObjectiveC.jl/issues/41);
+- The MPS wrappers are incomplete, automatic wrapper generation would greatly help with full
+  MPS support;
+- To implement a full-featured KernelAbstractions.jl back-end, Metal atomic operations need
+  to [be hooked up to Atomix](https://github.com/JuliaGPU/Metal.jl/issues/218);
+- [Full support for BFloat16 values](https://github.com/JuliaGPU/Metal.jl/issues/298), which
+  has been supported since Metal 3.1 (macOS 14), is not yet available in Metal.jl. There is,
+  however, a [draft PR](https://github.com/JuliaGPU/Metal.jl/pull/446) in the works. Check
+  it out if you're interested in helping out;
+- Some functionality present in CUDA.jl [could be ported to Metal.jl to improve
+  usability](https://github.com/JuliaGPU/Metal.jl/issues/443).