Add a pointer to GPUs in parallel computing (#36043)

* Add a pointer to GPUs * Update parallel-computing.md * Update parallel-computing.md * Update parallel-computing.md * Update doc/src/manual/parallel-computing.md Co-authored-by: Dilum Aluthge <[email protected]> * whitespace fix * Update parallel-computing.md * whitespace fix * whitespace fix * Update doc/src/manual/parallel-computing.md Co-authored-by: Jonas Schulze <[email protected]> Co-authored-by: Dilum Aluthge <[email protected]> Co-authored-by: Jonas Schulze <[email protected]>
JuliaLang · May 28, 2020 · 0ad6dcf · 0ad6dcf
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diff --git a/doc/src/manual/parallel-computing.md b/doc/src/manual/parallel-computing.md
@@ -1,20 +1,34 @@
 # Parallel Computing
 
-Julia supports three main categories of features for concurrent and parallel programming:
+Julia supports these four categories of concurrent and parallel programming:
 
-1. Asynchronous "tasks", or coroutines
-2. Multi-threading
-3. Distributed computing
+1. **Asynchronous "tasks", or coroutines**:
 
-Julia Tasks allow suspending and resuming computations
-for I/O, event handling, producer-consumer processes, and similar patterns.
-Tasks can synchronize through operations like [`wait`](@ref) and [`fetch`](@ref), and
-communicate via [`Channel`](@ref)s.
+    Julia Tasks allow suspending and resuming computations
+    for I/O, event handling, producer-consumer processes, and similar patterns.
+    Tasks can synchronize through operations like [`wait`](@ref) and [`fetch`](@ref), and
+    communicate via [`Channel`](@ref)s. While strictly not parallel computing by themselves,
+    Julia lets you schedule `Task`s on several threads.
 
-Multi-threading functionality builds on tasks by allowing them to run simultaneously
-on more than one thread or CPU core, sharing memory.
+2. **Multi-threading**:
 
-Finally, distributed computing runs multiple processes with separate memory spaces,
-potentially on different machines.
-This functionality is provided by the `Distributed` standard library as well as
-external packages like `MPI.jl` and `DistributedArrays.jl`.
+    Julia's [multi-threading](@ref man-multithreading) provides the ability to schedule Tasks
+    simultaneously on more than one thread or CPU core, sharing memory. This is usually the easiest way
+    to get parallelism on one's PC or on a single large multi-core server. Julia's multi-threading
+    is composable. When one multi-threaded function calls another multi-threaded function, Julia
+    will schedule all the threads globally on available resources, without oversubscribing.
+
+3. **Distributed computing**:
+
+    Distributed computing runs multiple Julia processes with separate memory spaces. These can be on the same
+    computer or multiple computers. The `Distributed` standard library provides the capability for remote execution
+    of a Julia function. With this basic building block, it is possible to build many different kinds of
+    distributed computing abstractions. Packages like [`DistributedArrays.jl`](https://github.com/JuliaParallel/DistributedArrays.jl)
+    are an example of such an abstraction. On the other hand, packages like [`MPI.jl`](https://github.com/JuliaParallel/MPI.jl) and
+    [`Elemental.jl`](https://github.com/JuliaParallel/Elemental.jl) provide access to the existing MPI ecosystem of libraries.
+
+4. **GPU computing**:
+
+    The Julia GPU compiler provides the ability to run Julia code natively on GPUs. There
+    is a rich ecosystem of Julia packages that target GPUs. The [JuliaGPU.org](https://juliagpu.org)
+    website provides a list of capabilities, supported GPUs, related packages and documentation.