Merge pull request #229 from JuliaGPU/tb/cleanup

Large clean-up

Author: maleadt

.appveyor.yml

@@ -38,7 +38,9 @@ julia:nightly:
 #       that means we have to manually install CuArrays' test dependencies though.
 
 cuarrays:
-  extends: .julia:1.2
+  extends:
+    - .julia:1.3
+    - .test
   tags:
     - nvidia
   image: nvidia/cuda:10.1-devel
@@ -51,13 +53,13 @@ cuarrays:
     - julia --project=$CUARRAYS -e 'using Pkg;
                                     Pkg.instantiate();
                                     Pkg.add(["FFTW", "ForwardDiff", "FillArrays"])'
-    - JULIA_LOAD_PATH=".:$CUARRAYS::" julia $CUARRAYS/test/runtests.jl
+    - JULIA_LOAD_PATH=".:$CUARRAYS::" julia --code-coverage $CUARRAYS/test/runtests.jl
   allow_failure: true
 
 
 # other tasks
 
 coverage:
   extends:
-    - .julia:1.2
+    - .julia:1.3
     - .coverage

.gitlab-ci.yml

@@ -3,11 +3,15 @@ language: julia
 os:
   - linux
   - osx
+  - windows
 
 dist: trusty
 
 julia:
   - 1.0
+  - 1.1
+  - 1.2
+  - 1.3
   - nightly
 
 matrix:
@@ -20,7 +24,7 @@ notifications:
 jobs:
   include:
     - stage: "Documentation"
-      julia: 1.0
+      julia: 1.3
       os: linux
       script:
         - julia --project=docs/ -e 'using Pkg; Pkg.develop(PackageSpec(path=pwd()));

.travis.yml

@@ -1,104 +1,27 @@
 # GPUArrays
 
-*Abstract GPU Array package for Julia's various GPU backends.*
+*Reusable GPU array functionality for Julia's various GPU backends.*
 
-[![][docs-stable-img]][docs-stable-url] [![][docs-dev-img]][docs-dev-url] [![codecov](https://codecov.io/gh/JuliaGPU/GPUArrays.jl/branch/master/graph/badge.svg)](https://codecov.io/gh/JuliaGPU/GPUArrays.jl)
+| **Documentation**                                                         | **Build Status**                                                                            |
+|:-------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------:|
+| [![][docs-stable-img]][docs-stable-url] [![][docs-dev-img]][docs-dev-url] | [![][gitlab-img]][gitlab-url] [![][travis-img]][travis-url] [![][codecov-img]][codecov-url] |
 
-[docs-stable-img]: https://img.shields.io/badge/docs-stable-blue.svg
-[docs-stable-url]: http://JuliaGPU.github.io/GPUArrays.jl/stable/
-[docs-dev-img]: https://img.shields.io/badge/docs-dev-blue.svg
-[docs-dev-url]: http://JuliaGPU.github.io/GPUArrays.jl/dev/
-
-
-[Benchmarks](https://github.com/JuliaGPU/GPUBenchmarks.jl/blob/master/results/results.md)
-
-This package is the counterpart of Julia's `Base.AbstractArray` interface, but
-for GPU array types. Currently, you either need to install
-[CLArrays](https://github.com/JuliaGPU/CLArrays.jl) or
-[CuArrays](https://github.com/JuliaGPU/CuArrays.jl) for a concrete
-implementation.
-
-
-# Why another GPU array package in yet another language?
-
-Julia offers great advantages for programming the GPU.
-This [blog post](http://mikeinnes.github.io/2017/08/24/cudanative.html) outlines a few of those.
-
-E.g., we can use Julia's JIT to generate optimized kernels for map/broadcast operations.
-
-This works even for things like complex arithmetic, since we can compile what's already in Julia Base.
-This isn't restricted to Julia Base, GPUArrays works with all kind of user defined types and functions!
-
-GPUArrays relies heavily on Julia's dot broadcasting.
-The great thing about dot broadcasting in Julia is, that it
-[actually fuses operations syntactically](http://julialang.org/blog/2017/01/moredots), which is vital for performance on the GPU.
-E.g.:
-
-```Julia
-out .= a .+ b ./ c .+ 1
-#turns into this one broadcast (map):
-broadcast!(out, a, b, c) do a, b, c
-    a + b / c + 1
-end
-```
-
-Will result in one GPU kernel call to a function that combines the operations without any extra allocations.
-This allows GPUArrays to offer a lot of functionality with minimal code.
-
-Also, when compiling Julia for the GPU, we can use all the cool features from Julia, e.g.
-higher order functions, multiple dispatch, meta programming and generated functions.
-Checkout the examples, to see how this can be used to emit specialized code while not losing flexibility:
-
-[<img src="https://raw.githubusercontent.com/JuliaGPU/GPUBenchmarks.jl/master/results/plots/juliaset_result.png" height="150">](https://github.com/JuliaGPU/GPUBenchmarks.jl/blob/master/results/results.md)
-[<img src="https://user-images.githubusercontent.com/1010467/40832645-12ca1f50-658c-11e8-9fb4-170871db2499.png" height="150">](https://juliagpu.github.io/GPUShowcases.jl/latest/)
-
-In theory, we could go as far as inspecting user defined callbacks (we can get the complete AST), count operations and estimate register usage and use those numbers to optimize our kernels!
+[gitlab-img]: https://gitlab.com/JuliaGPU/CuArrays.jl/badges/master/pipeline.svg
+[gitlab-url]: https://gitlab.com/JuliaGPU/CuArrays.jl/commits/master
 
+[travis-img]: https://api.travis-ci.org/JuliaGPU/GPUArrays.jl.svg?branch=master
+[travis-url]: https://travis-ci.org/JuliaGPU/GPUArrays.jl
 
-# Scope
-
-Interface offered for all backends:
-
-```Julia
-map(f, ::GPUArray...)
-map!(f, dest::GPUArray, ::GPUArray...)
-
-broadcast(f, ::GPUArray...)
-broadcast!(f, dest::GPUArray, ::GPUArray...)
-
-mapreduce(f, op, ::GPUArray...) # so support for sum/mean/minimum etc comes for free
-
-getindex, setindex!, push!, append!, splice!, append!, copy!, reinterpret, convert
-
-From (CL/CU)FFT
-fft!/fft/ifft/ifft! and the matching plan_fft functions.
-From (CL/CU)BLAS
-gemm!, scal!, gemv! and the high level functions that are implemented with these, like A * B, A_mul_B!, etc.
-```
-
-# Currently supported subset of Julia Code
-
-Working with immutable isbits (not containing pointers) type should be completely supported
-with non-allocating code (so no constructs like `x = [1, 2, 3]`). Note that tuples are isbits, so this works x = (1, 2, 3).
-Transpiler/OpenCL has problems with putting GPU arrays on the gpu into a struct - so no views and actually no multidimensional indexing. For that `size` is needed which would need to be part of the array struct. A fix for that is in sight, though.
-
-# JLArray
-
-The `JLArray` is a `GPUArray` which doesn't run on the GPU and rather uses Julia's async constructs as its backend. It serves as a fallback for testing compatibility with `GPUArray`s in cases where a GPU does not exist and as a reference implementation. It is constructed as follows:
-
-```julia
-gA = JLArray(A)
-```
-
-# TODO / up for grabs
-
-* stencil operations, convolutions
-* more tests and benchmarks
-* tests, that only switch the backend but use the same code
-* performance improvements!!
-* interop between OpenCL, CUDA and OpenGL is there as a protype, but needs proper hooking up via `Base.copy!` / `convert`
+[codecov-img]: https://codecov.io/gh/JuliaGPU/CuArrays.jl/branch/master/graph/badge.svg
+[codecov-url]: https://codecov.io/gh/JuliaGPU/CuArrays.jl
 
+[docs-stable-img]: https://img.shields.io/badge/docs-stable-blue.svg
+[docs-stable-url]: http://JuliaGPU.github.io/GPUArrays.jl/stable/
 
-# Installation
+[docs-dev-img]: https://img.shields.io/badge/docs-dev-blue.svg
+[docs-dev-url]: http://JuliaGPU.github.io/GPUArrays.jl/dev/
 
-See CuArrays or CLArrays for installation instructions.
+This package is the counterpart of Julia's `AbstractArray` interface, but for GPU array
+types: It provides functionality and tooling to speed-up development of new GPU array types.
+**This package is not intended for end users!** Instead, you should use one of the packages
+that builds on GPUArrays.jl, such as [CuArrays](https://github.com/JuliaGPU/CuArrays.jl).

README.md

@@ -0,0 +1,92 @@
+# This file is machine-generated - editing it directly is not advised
+
+[[Base64]]
+uuid = "2a0f44e3-6c83-55bd-87e4-b1978d98bd5f"
+
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+
+[[Distributed]]
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+
+[[DocStringExtensions]]
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+version = "0.8.1"
+
+[[Documenter]]
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+uuid = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
+version = "0.24.4"
+
+[[InteractiveUtils]]
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+
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+
+[[LibGit2]]
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+
+[[Libdl]]
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+uuid = "69de0a69-1ddd-5017-9359-2bf0b02dc9f0"
+version = "0.3.10"
+
+[[Pkg]]
+deps = ["Dates", "LibGit2", "Libdl", "Logging", "Markdown", "Printf", "REPL", "Random", "SHA", "Test", "UUIDs"]
+uuid = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
+
+[[Printf]]
+deps = ["Unicode"]
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+
+[[SHA]]
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+
+[[Serialization]]
+uuid = "9e88b42a-f829-5b0c-bbe9-9e923198166b"
+
+[[Sockets]]
+uuid = "6462fe0b-24de-5631-8697-dd941f90decc"
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+[[Test]]
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+
+[[UUIDs]]
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+
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docs/Manifest.toml

@@ -1,15 +1,30 @@
 using Documenter, GPUArrays
 
-makedocs(
-    modules = [GPUArrays],
-    format = Documenter.HTML(prettyurls = get(ENV, "CI", nothing) == "true"),
-    sitename = "GPUArrays.jl",
-    pages = [
-        "Home" => "index.md",
-    ],
-    doctest = true
-)
+function main()
+    makedocs(
+        modules = [GPUArrays],
+        format = Documenter.HTML(
+            # Use clean URLs on CI
+            prettyurls = get(ENV, "CI", nothing) == "true",
+            assets = ["assets/favicon.ico"],
+            analytics = "UA-154489943-6",
+        ),
+        sitename = "GPUArrays.jl",
+        pages = [
+            "Home"          => "index.md",
+            "Interface"     => "interface.md",
+            "Functionality" => [
+                "functionality/host.md",
+                "functionality/device.md",
+            ],
+            "Test suite"    => "testsuite.md",
+        ],
+        doctest = true,
+    )
 
-deploydocs(
-    repo   = "github.com/JuliaGPU/GPUArrays.jl.git"
-)
+    deploydocs(
+        repo = "github.com/JuliaGPU/GPUArrays.jl.git"
+    )
+end
+
+isinteractive() || main()

docs/make.jl

@@ -0,0 +1,3 @@
+# `AbstractDeviceArray`
+
+TODO: describe functionality

docs/src/assets/favicon.ico

@@ -0,0 +1,3 @@
+# `AbstractGPUArray`
+
+TODO: describe functionality