Changeset 9f10d1f2 for doc/proposals/concurrency/text/future.tex

Timestamp:

Nov 23, 2017, 1:31:43 PM (7 years ago)

Author:

Thierry Delisle <tdelisle@…>

Branches:

ADT, aaron-thesis, arm-eh, ast-experimental, cleanup-dtors, deferred_resn, demangler, enum, forall-pointer-decay, jacob/cs343-translation, jenkins-sandbox, master, new-ast, new-ast-unique-expr, new-env, no_list, persistent-indexer, pthread-emulation, qualifiedEnum, resolv-new, with_gc

Children:

88ef2af

Parents:

07c1e595

Message:

Revised up to chapter three

File:

• doc/proposals/concurrency/text/future.tex (modified) (1 diff)

doc/proposals/concurrency/text/future.tex

@@ -10 +10 @@
 \section{Non-Blocking IO} \label{futur:nbio}
 While most of the parallelism tools
-However, many modern workloads are not bound on computation but on IO operations, an common case being web-servers and XaaS (anything as a service). These type of workloads often require significant engineering around amortizing costs of blocking IO operations. While improving throughput of these operations is outside what \CFA can do as a language, it can help users to make better use of the CPU time otherwise spent waiting on IO operations. The current trend is to use asynchronous programming using tools like callbacks and/or futures and promises\cite. However, while these are valid solutions, they lead to code that is harder to read and maintain because it is much less linear
+However, many modern workloads are not bound on computation but on IO operations, an common case being web-servers and XaaS (anything as a service). These type of workloads often require significant engineering around amortizing costs of blocking IO operations. While improving throughput of these operations is outside what \CFA can do as a language, it can help users to make better use of the CPU time otherwise spent waiting on IO operations. The current trend is to use asynchronous programming using tools like callbacks and/or futures and promises~\cit. However, while these are valid solutions, they lead to code that is harder to read and maintain because it is much less linear
 
 \section{Other concurrency tools} \label{futur:tools}
-While monitors offer a flexible and powerful concurrent core for \CFA, other concurrency tools are also necessary for a complete multi-paradigm concurrency package. Example of such tools can include simple locks and condition variables, futures and promises\cite{promises}, and executors. These additional features are useful when monitors offer a level of abstraction which is inadequate for certain tasks.
+While monitors offer a flexible and powerful concurrent core for \CFA, other concurrency tools are also necessary for a complete multi-paradigm concurrency package. Example of such tools can include simple locks and condition variables, futures and promises~\cite{promises}, and executors. These additional features are useful when monitors offer a level of abstraction which is inadequate for certain tasks.
 
 \section{Implicit threading} \label{futur:implcit}
-Simpler applications can benefit greatly from having implicit parallelism. That is, parallelism that does not rely on the user to write concurrency. This type of parallelism can be achieved both at the language level and at the library level. The canonical example of implicit parallelism is parallel for loops, which are the simplest example of a divide and conquer algorithm\cite{uC++book}. Listing \ref{lst:parfor} shows three different code examples that accomplish point-wise sums of large arrays. Note that none of these example explicitly declare any concurrency or parallelism objects.
+Simpler applications can benefit greatly from having implicit parallelism. That is, parallelism that does not rely on the user to write concurrency. This type of parallelism can be achieved both at the language level and at the library level. The canonical example of implicit parallelism is parallel for loops, which are the simplest example of a divide and conquer algorithm~\cite{uC++book}. Listing \ref{lst:parfor} shows three different code examples that accomplish point-wise sums of large arrays. Note that none of these example explicitly declare any concurrency or parallelism objects.
 
 \begin{figure}