source: doc/theses/thierry_delisle_PhD/thesis/text/runtime.tex @ 6a531ab

\chapter{\CFA Runtime}
This chapter offers an overview of the capabilities of the \CFA runtime prior to this work.

Threading in \CFA offers is based on \Gls{uthrding}, where \glspl{thrd} are the representation of a unit of work. As such, \CFA programmers should expect these units to be fairly inexpensive, that is: programmers should be able to create a large number of \glspl{thrd} and switch between \glspl{thrd} liberally without many concerns for performance.

\section{M:N Threading}\label{prev:model}

C traditionnally uses a 1:1 threading model. This model uses \glspl{kthrd} to achive parallelism and concurrency. In this model, every thread of computation maps to an object in the kernel. The kernel then has the responsibility of managing these threads, \eg creating, scheduling, blocking. This also entails that the kernel has a perfect view of every thread executing in the system\footnote{This is not completly true due to primitives like \texttt{futex}es, which have a significant portion of their logic in user space.}.

By contrast \CFA uses an M:N threading models, where concurrency is achieved using many user-level threads mapped onto fewer \glspl{kthrd}. The user-level threads have the same semantic meaning as a \glspl{kthrd} in the 1:1 model, they represent an independant thread of execution with it's on stack. The difference is that user-level threads do not have a corresponding object in the kernel, they are handled by the runtime in user space and scheduled onto \glspl{kthrd}, referred to as \glspl{proc} in this document. \Glspl{proc} run a \gls{thrd} until it context switches out, it then choses a different \gls{thrd} to run.

\section{Clusters}
\begin{figure}
        \begin{center}
                \input{system.pstex_t}
        \end{center}
        \caption{Overview of the \CFA runtime}
        \label{fig:system}
        \Glspl{thrd} are scheduled inside a particular cluster, where it only runs on the \glspl{proc} which belong to the cluster. The discrete-event manager, which handles preemption and timeout, is a \gls{kthrd} which lives outside any cluster and does not run \glspl{thrd}.
\end{figure}
\CFA allows the option to group user-level threading, in the form of clusters. Both \glspl{thrd} and \glspl{proc} belong to a specific cluster. \Glspl{thrd} will only be scheduled onto \glspl{proc} in the same cluster and scheduling is done independantly of other clusters. Figure~\ref{fig:system} shows an overview if this system. This allows programmers to control more tightly parallelism. It also opens the door to handling effects like NUMA, by pining clusters to specific NUMA node\footnote{This is not currently implemented in \CFA, but the only hurdle left is creating a generic interface for cpu masks.}.

\section{Scheduling}
The \CFA runtime was previously using a strictly \glsxtrshort{fifo} ready queue with a single lock. This setup offers perfect fairness in terms of opportunities to run/ However, it offers poor scalability, since the performance of the ready queue can never be improved by adding more \glspl{hthrd}, but the contention can cause significant performance degradation.

\section{\glsxtrshort{io}}\label{prev:io}
Prior to this work, the \CFA runtime did not add any particular support for \glsxtrshort{io} operations. \CFA being built on C, this means that, while all the operations available in C are available in \CFA, \glsxtrshort{io} operations are designed for the POSIX threading model\cit{pthreads}. Using these operations in a M:N threading model, when they are built for 1:1 threading, means that operations block \glspl{proc} instead of \glspl{thrd}. While this can work in certain cases, it limits the number of concurrent operations to the number of \glspl{proc} rather than \glspl{thrd}. This also means that deadlocks can occur because all \glspl{proc} are blocked even if at least one \gls{thrd} is ready to run. A simple example of this type of deadlock would be as follows:

Given a simple network program with 2 \glspl{thrd} and a single \gls{proc}, one \gls{thrd} sends network requests to a server and the other \gls{thrd} waits for response from the server. If the second \gls{thrd} races ahead, it may wait for responses to requests that have not been sent yet. In theory, this should not be a problem, even if the second \gls{thrd} waits, the first \gls{thrd} is still ready to run and should just be able to get CPU time and send the request. In practice with M:N threading, while the first \gls{thrd} is ready, the lone \gls{proc} in this example will \emph{not} try to run the first \gls{thrd} if it is blocked in the \glsxtrshort{io} operation of the second \gls{thrd}. If this happen, the system is effectively deadlocked\footnote{In this example, the deadlocked could be resolved if the server sends unprompted messages to the client. However, this solution is not general and may not be appropriate even in this simple case.}.

One of the objective of this work, is to introduce \emph{User-Level \glsxtrshort{io}} which, as a parallel to \glslink{uthrding}{User-Level \emph{Threading}}, blocks \glspl{thrd} rather than \glspl{proc} when doing \glsxtrshort{io} operations. This entails multiplexing the \glsxtrshort{io} operations of many \glspl{thrd} onto fewer \glspl{proc}. This multiplexing requires that a single \gls{proc} be able to execute multiple operations in parallel. This cannot be done with operations that block \glspl{proc}, \ie \glspl{kthrd}, since the first operation would prevent starting new operations for its duration. Executing operations in parallel requires \emph{asynchronous} \glsxtrshort{io}, sometimes referred to as \emph{non-blocking}, since the \gls{kthrd} is not blocked.

\section{Interoperating with \texttt{C}}
While \glsxtrshort{io} operations are the classical example of operations that block \glspl{kthrd}, the challenges mentioned in the previous section do not require \glsxtrshort{io} to be involved. These challenges are a product of blocking system calls rather than \glsxtrshort{io}. C offers no tools to identify whether or not a librairy function will lead to a blocking system call. This fact means interoperatability with C becomes a challenge in a M:N threading model.

Languages like Go and Java, which have strict interoperatability with C\cit{JNI, GoLang with C}, can control operations in C by ``sandboxing'' them. They can, for example, delegate C operations to \glspl{kthrd} that are not \glspl{proc}. Sandboxing may help towards guaranteeing that the deadlocks mentioned in the previous section do not occur.

As mentioned in Section~\cit{\CFA intro}, \CFA is binary compatible with C and, as such, trivially supports calls to and from C librairies. Furthermore, interoperatability can happen within a single library, through inline code or simply C and \CFA translation units archived together. The fine-grained interoperatability between C and \CFA has two consequences:
\begin{enumerate}
        \item Precisely identifying C calls that could block is difficult.
        \item Introducing code where interoperatability occurs could have a significant impact on general performance.
\end{enumerate}

Because of these consequences, this work does not attempt to ``sandbox'' calls to C. It is possible that conflicting calls to C could lead to deadlocks on \CFA's M:N threading model where they would not in the traditionnal 1:1 threading model. However, I judge that solving this problem in general, in a way that is composable and flexible, is too complex in itself and would add too much work to this thesis. Therefore it is outside the scope of this thesis.