diff --git a/doc/src/manual/parallel-computing.md b/doc/src/manual/parallel-computing.md
index 415def3ca8e55..050e342986de9 100644
--- a/doc/src/manual/parallel-computing.md
+++ b/doc/src/manual/parallel-computing.md
@@ -16,14 +16,14 @@ Julia also supports experimental multi-threading, where execution is forked and
 threads.
 Known as the fork-join approach, parallel threads execute independently, and must ultimately be joined in Julia's main thread to allow serial execution to continue.
 Multi-threading is supported using the `Base.Threads` module that is still considered experimental, as Julia is
-not yet fully thread-safe. In particular segfaults seem to occur during I/O operations and task switching.
+not yet fully thread-safe. In particular,segfaults seem to occur during I/O operations and task switching.
 As an up-to-date reference, keep an eye on [the issue tracker](https://github.com/JuliaLang/julia/issues?q=is%3Aopen+is%3Aissue+label%3Amultithreading).
 Multi-Threading should only be used if you take into consideration global variables, locks and
 atomics, all of which are explained later.
 
-In the end we will present Julia's approach to distributed and parallel computing. With scientific computing
+In the end,we will present Julia's approach to distributed and parallel computing. With scientific computing
 in mind, Julia natively implements interfaces to distribute a process across multiple cores or machines.
-Also we will mention useful external packages for distributed programming like `MPI.jl` and `DistributedArrays.jl`.
+Also, we will mention useful external packages for distributed programming like `MPI.jl` and `DistributedArrays.jl`.
 
 # Coroutines