Index: doc/proposals/concurrency/concurrency.tex
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--- doc/proposals/concurrency/concurrency.tex	(revision fe84230d44afd91fb519712a68f209d0452629d3)
+++ doc/proposals/concurrency/concurrency.tex	(revision 5f5083ecd08695483327d194d9c4c0c2df735fb7)
@@ -673,21 +673,23 @@
 % #       #     # #     # #     # ####### ####### ####### ####### ###  #####  #     #
 \section{Parallelism}
-Historically, computer performance was about processor speeds and instructions count. However, with heat dissipation being an ever growing challenge, parallelism has become the new source of greatest performance~\cite{Sutter05, Sutter05b}. In this decade, it is not longer reasonnable to create high-performance application without caring about parallelism. Indeed, parallelism is an important aspect of performance and more specifically throughput and hardware utilization. The lowest level approach of parallelism is to use \glspl{kthread}. However since these have significant costs and limitations \glspl{kthread} are now mostly used as an implementation tool rather than a user oriented one. There are several alternatives to solve these issues which all have strengths and weaknesses.
+Historically, computer performance was about processor speeds and instructions count. However, with heat dissipation being a direct consequence of speed increase, parallelism has become the new source for increased performance~\cite{Sutter05, Sutter05b}. In this decade, it is not longer reasonnable to create high-performance application without caring about parallelism. Indeed, parallelism is an important aspect of performance and more specifically throughput and hardware utilization. The lowest level approach of parallelism is to use \glspl{kthread} in combination with semantics like \code{fork}, \code{join}, etc. However, since these have significant costs and limitations \glspl{kthread} are now mostly used as an implementation tool rather than a user oriented one. There are several alternatives to solve these issues that all have strengths and weaknesses. While there are many variations of the presented paradigms, most of these variations do not actually change the guarantees or the semantics, they simply move costs in order to achieve better performance for certain workloads.
 
 \subsection{User-level threads}
-A direct improvement on the \gls{kthread} approach is to use \glspl{uthread}. These threads offer most of the same features that the operating system already provide but can be used on a much larger scale. This is the most powerfull solution as it allows all the features of multi-threading while removing several of the more expensives costs of using kernel threads. The down side is that almost none of the low-level threading complexities are hidden, users still have to think about data races, deadlocks and synchronization issues. This can be somewhat alleviated by a concurrency toolkit with strong garantees but the parallelism toolkit offers very little to reduce complexity in itself.
-
-Examples of languages that support are Java~\cite{Java}, Haskell~\cite{Haskell} and \uC~\cite{uC++book}.
+A direct improvement on the \gls{kthread} approach is to use \glspl{uthread}. These threads offer most of the same features that the operating system already provide but can be used on a much larger scale. This approach is the most powerfull solution as it allows all the features of multi-threading while removing several of the more expensives costs of using kernel threads. The down side is that almost none of the low-level threading problems are hidden, users still have to think about data races, deadlocks and synchronization issues. These issues can be somewhat alleviated by a concurrency toolkit with strong garantees but the parallelism toolkit offers very little to reduce complexity in itself.
+
+Examples of languages that support are Erlang~\cite{Erlang} and \uC~\cite{uC++book}.
+
+\subsection{Fibers : user-level threads without preemption}
+In the middle of the flexibility versus complexity spectrum lay \glspl{fiber} which offer \glspl{uthread} without the complexity of preemption by using cooperative scheduling. On a single core machine this means users need not worry about concurrency. On multi-core machines, while concurrency is still a concern, it is only a problem for fibers across cores but not while on the same core. This extra guarantee plus the fact that creating and destroying fibers are implicit synchronizing points means preventing mutable shared ressources still leaves many control flow options. However, multi-core processors can still execute fibers in parallel. This means users either need to worry about mutual exclusion, deadlocks and race conditions, or limit themselves to subset of concurrency primitives, raising the complexity in both cases. In this aspect, fibers can be seen as a more powerfull alternative to \glspl{job}.
+
+An example of a language that uses fibers is Go~\cite{Go}
 
 \subsection{Jobs and thread pools}
-The approach on the opposite end of the spectrum is to base parallelism on \glspl{job}. Indeed, \glspl{job} offer limited flexibility but at the benefit of a simpler user interface. In \gls{job} based systems users express parallelism as units of work and the dependency graph (either explicit or implicit) that tie them together. This means users need not to worry about concurrency but significantly limits the interaction that can occur between different jobs. Indeed, any \gls{job} that blocks also blocks the underlying \gls{kthread}, this effectively mean the CPU utilization, and therefore throughput, will suffer noticeably.
-The golden standard of this implementation is Intel's TBB library~\cite{TBB}.
-
-\subsection{Fibers : user-level threads without preemption}
-Finally, in the middle of the flexibility versus complexity spectrum lay \glspl{fiber} which offer \glspl{uthread} without the complexity of preemption. This means users don't have to worry about other \glspl{fiber} suddenly executing between two instructions which signficantly reduces complexity. However, any call to IO or other concurrency primitives can lead to context switches. Furthermore, users can also block \glspl{fiber} in the middle of their execution without blocking a full processor core. This means users still have to worry about mutual exclusion, deadlocks and race conditions in their code, raising the complexity significantly.
-An example of a language that uses fibers is Go~\cite{Go}
+The approach on the opposite end of the spectrum is to base parallelism on \glspl{pool}. Indeed, \glspl{pool} offer limited flexibility but at the benefit of a simpler user interface. In \gls{pool} based systems, users express parallelism as units of work and the dependency graph (either explicit or implicit) that tie them together. This approach means users need not worry about concurrency but significantly limits the interaction that can occur between different jobs. Indeed, any \gls{job} that blocks also blocks the underlying worker, which effectively means the CPU utilization, and therefore throughput, suffers noticeably. It can be argued that a solution to this problem is to use more workers than available cores. However, unless the number of jobs and the number of workers are comparable, having a significant amount of blocked jobs will always results in idles cores.
+
+The gold standard of this implementation is Intel's TBB library~\cite{TBB}.
 
 \subsection{Paradigm performance}
-While the choice between the three paradigms listed above may have significant performance implication, it is difficult to pin the performance implications of chosing a model at the language level. Indeed, in many situations one of these paradigms will show better performance but it all strongly depends on the usage. Having mostly indepent units of work to execute almost guarantess that the \gls{job} based system will have the best performance. However, add interactions between jobs and the processor utilisation might suffer. User-level threads may allow maximum ressource utilisation but context switches will be more expansive and it is also harder for users to get perfect tunning. As with every example, fibers sit somewhat in the middle of the spectrum. Furthermore, if the units of uninterrupted work are large enough the paradigm choice will be largely amorticised by the actual work done.
+While the choice between the three paradigms listed above may have significant performance implication, it is difficult to pindown the performance implications of chosing a model at the language level. Indeed, in many situations one of these paradigms may show better performance but it all strongly depends on the workload. Having a large amount of mostly independent units of work to execute almost guarantess that the \gls{pool} based system has the best performance thanks to the lower memory overhead. However, interactions between jobs can easily exacerbate contention. User-level threads may allows fine grain context switching which may result in better resource utilisation but context switches will be more expansive and it is also harder for users to get perfect tunning. As with every example, fibers sit somewhat in the middle of the spectrum. Furthermore, if the units of uninterrupted work are large enough the paradigm choice will be largely amorticised by the actual work done.
 
 %  #####  #######    #          ####### ######  ######
@@ -706,5 +708,5 @@
 \multicolumn{1}{ c| }{} & Has a stack & Preemptive \\
 \hline
-\Glspl{job} & X & X \\
+\Glspl{pool} & X & X \\
 \hline
 \Glspl{fiber} & \checkmark & X \\
@@ -715,4 +717,6 @@
 \end{center}
 
+This table is missing several variations (for example jobs on \glspl{uthread} or \glspl{fiber}), but these variation affect mostly performance and do not effect the guarantees as the presented paradigm do.
+
 As shown in section \ref{cfaparadigms} these different blocks being available in \CFA it is trivial to reproduce any of these paradigm.
 
@@ -726,5 +730,5 @@
 
 \subsection{Thread Interface}
-The basic building blocks of \CFA are \glspl{cfathread}. By default these are implemented as \glspl{uthread} and as such offer a flexible and lightweight threading interface (lightweight comparatievely to \glspl{kthread}). A thread can be declared using a struct declaration prefix with the \code{thread} as follows :
+The basic building blocks of \CFA are \glspl{cfathread}. By default these are implemented as \glspl{uthread} and as such offer a flexible and lightweight threading interface (lightweight comparatievely to \glspl{kthread}). A thread can be declared using a struct declaration with prefix \code{thread} as follows :
 
 \begin{lstlisting}
@@ -732,5 +736,5 @@
 \end{lstlisting}
 
-Obviously, for this thread implementation to be usefull it must run some user code. Several other threading interfaces use some function pointer representation as the interface of threads (for example : \Csharp~\cite{Csharp} and Scala~\cite{Scala}). However, we consider that statically tying a \code{main} routine to a thread superseeds this approach. Since the \code{main} routine is definetely a special routine in \CFA, we can reuse the existing syntax for declaring routines with unordinary name, i.e. operator overloading. As such the \code{main} routine of a thread can be defined as such :
+Obviously, for this thread implementation to be usefull it must run some user code. Several other threading interfaces use some function pointer representation as the interface of threads (for example : \Csharp~\cite{Csharp} and Scala~\cite{Scala}). However, this proposal considers that statically tying a \code{main} routine to a thread superseeds this approach. Since the \code{main} routine is definitely a special routine in \CFA, the existing syntax for declaring routines with unordinary name can be extended, i.e. operator overloading. As such the \code{main} routine of a thread can be defined as :
 \begin{lstlisting}
 	thread struct foo {};
@@ -780,5 +784,5 @@
 }
 \end{lstlisting}
-This semantic has several advantages over explicit semantics : typesafety is guaranteed, any thread will always be started and stopped exaclty once and users can't make any progamming errors. Furthermore it naturally follows the memory allocation semantics which means users don't need to learn multiple semantics.
+This semantic has several advantages over explicit semantics : typesafety is guaranteed, a thread is always started and stopped exaclty once and users cannot make any progamming errors. Furthermore it naturally follows the memory allocation semantics, which means users do not need to learn multiple semantics.
 
 These semantics also naturally scale to multiple threads meaning basic synchronisation is very simple :
@@ -803,4 +807,14 @@
 
 		//Wait for the 10 threads to finish
+	}
+
+	void bar() {
+		MyThread* thrds = new MyThread[10];
+		//Start 10 threads at the beginning of the scope
+
+		DoStuff();
+
+		//Wait for the 10 threads to finish
+		delete MyThread;
 	}
 \end{lstlisting}
Index: doc/proposals/concurrency/glossary.tex
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--- doc/proposals/concurrency/glossary.tex	(revision fe84230d44afd91fb519712a68f209d0452629d3)
+++ doc/proposals/concurrency/glossary.tex	(revision 5f5083ecd08695483327d194d9c4c0c2df735fb7)
@@ -52,4 +52,12 @@
 }
 
+\longnewglossaryentry{pool}
+{name={thread-pool}}
+{
+Group of homogeneuous threads that loop executing units of works after another.
+
+\textit{Synonyms : }
+}
+
 \longnewglossaryentry{cfacluster}
 {name={cluster}}
Index: doc/proposals/concurrency/version
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--- doc/proposals/concurrency/version	(revision fe84230d44afd91fb519712a68f209d0452629d3)
+++ doc/proposals/concurrency/version	(revision 5f5083ecd08695483327d194d9c4c0c2df735fb7)
@@ -1,1 +1,1 @@
-0.5.146
+0.5.150
