Add a post on Metal.jl 0.2. (#32)

post/2023-03-03-metal_0.2/index.md

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+title = "Metal.jl 0.2: Metal Performance Shaders"
+author = "Tim Besard"
+abstract = """
+  Metal.jl 0.2 marks a significant milestone in the development of the Metal.jl package.
+  The release comes with initial support for the Metal Perform Shaders (MPS) framework for
+  accelerating common operations like matrix multiplications, as well as various
+  improvements for writing Metal kernels in Julia."""
++++
+
+{{abstract}}
+
+
+## Metal Performance Shaders
+
+Quoting the [Apple
+documentation](https://developer.apple.com/documentation/metalperformanceshaders), The Metal
+Performance Shaders (MPS) framework contains a collection of highly optimized compute and
+graphics shaders for use in Metal applications. With Metal.jl 0.2, we have added initial
+support for this framework, and used it to accelerate the matrix multiplication operation:
+
+```julia-repl
+julia> n = p = m = 2048
+julia> flops = n*m*(2p-1)
+17175674880
+
+julia> a = MtlArray(rand(Float32, n, p));
+julia> b = MtlArray(rand(Float32, p, m));
+julia> c = MtlArray(zeros(Float32, n, m));
+
+julia> bench = @benchmark Metal.@sync mul!(c, a, b)
+BenchmarkTools.Trial: 518 samples with 1 evaluation.
+ Range (min … max):  9.366 ms …  13.354 ms  ┊ GC (min … max): 0.00% … 0.00%
+ Time  (median):     9.629 ms               ┊ GC (median):    0.00%
+ Time  (mean ± σ):   9.646 ms ± 192.169 μs  ┊ GC (mean ± σ):  0.00% ± 0.00%
+
+               ▃▂▅▅▆▆▆▇█▇▇▆▅▄▄▁▁ ▁
+  ▄▁▄▄▄▄▆▆▆▄▄▁▇█████████████████▄█▄▁▆▁▄▁▆▁▇▁▄▄▁▁▄▄▇▁▄▆▄▁▁▁▁▁▄ █
+  9.37 ms      Histogram: log(frequency) by time      10.1 ms <
+
+ Memory estimate: 352 bytes, allocs estimate: 12.
+
+julia> flops / (minimum(bench.times)/1e9)
+1.83e12
+```
+
+The benchmark above shows that on an 8-core M1 Pro matrix multiplication now reaches 1.8
+TFLOPS (out of the 2.6TFLOPS of theoretical performance). The accelerated matrix
+multiplication is available for a variety of input types, incuding mixed-mode operations,
+and as shown above is integrated with the LinearAlgebra.jl `mul!` interface.
+
+Of course, the MPS framework offers more than just matrix multiplication, and we expect to
+support more of it in the future. If you have a specific operation you would like to use
+from Julia, please let us know by opening an issue on the Metal.jl repository.
+
+
+## GPU profiling support
+
+To support the development of Metal kernels,
+[Max Hawkins](https://github.com/max-Hawkins) has added support for GPU profiling.
+Similar to how this works in CUDA.jl, you can run code under the `Metal.@profile` macro to
+record its execution. However, this does first require setting the `METAL_CAPTURE_ENABLED`
+environment flag *before* import Metal.jl:
+
+```julia-repl
+julia> ENV["METAL_CAPTURE_ENABLED"] = 1
+
+julia> using Metal
+
+julia> a = mtl(rand(1024, 1024))
+julia> Metal.@profile sum(a)
+[ Info: GPU frame capture saved to jl_metal.gputrace/
+```
+
+The resulting capture can be opened with Xcode, presenting a timeline that's similar to
+other profilers:
+
+{{img "xcode.png" "XCode viewing a Metal.jl capture trace" }}
+
+
+## Other improvements
+
+- Julia 1.9 is supported, but requires an up-to-date macOS version (issues have been
+  encountered on macOS 12.4);
+- An `mtl` function has been added for converting Julia arrays to Metal arrays, similar to the
+  `cu` function in CUDA.jl;
+- Multiple GPUs are supported, and the `device!` function can be used to select one;
+- Coverage for SIMD Group functions has been improved, so it's is now possible to use
+  `simdgroup_load`, `simdgroup_store`, `simdgroup_multiply`, and
+  `simdgroup_multiply_accumulate` in kernels functions.
+
+
+## Future work
+
+Although Metal.jl is now usable for a variety of applications, there is still work to be
+done before it can be considered production-ready. In particular:
+
+- there are known performance issues with `mapreduce`, and other operations that realy on
+  `CartesianIndices`;
+- the `libcmt` wrapper library for interfacing with the Metal APIs is cumbersome to use
+  and improve, and we are looking into native ObjectiveC FFI instead;
+- the MPS wrappers are incomplete, and similar to the Metal APIs requires a replacement
+  to `libcmt` to be improved;
+- support for atomic operations is missing, which is required to implement a full-featured
+  KernelAbstractions.jl back-end.
+
+Once (most of) these issues are addressed, we should be able to release Metal.jl 1.0.