\chapter{User Level \io}
As mentionned in Section~\ref{prev:io}, User-Level \io requires multiplexing the \io operations of many \glspl{thrd} onto fewer \glspl{proc} using asynchronous \io operations. Various operating systems offer various forms of asynchronous operations and as mentioned in Chapter~\ref{intro}, this work is exclusively focuesd on Linux.

\section{Kernel Interface}
Since this work fundamentally depends on operating system support, the first step of any design is to discuss the available interfaces and pick one (or more) as the foundations of the \io subsystem.

\subsection{\lstinline|O_NONBLOCK|}
In Linux, files can be opened with the flag @O_NONBLOCK@~\cite{MAN:open} (or @SO_NONBLOCK@~\cite{MAN:accept}, the equivalent for sockets) to use the file descriptors in ``nonblocking mode''. In this mode, ``Neither the open() nor any subsequent \io operations on the [opened file descriptor] will cause the calling
process to wait.'' This feature can be used as the foundation for the \io subsystem. However, for the subsystem to be able to block \glspl{thrd} until an operation completes, @O_NONBLOCK@ must be use in conjunction with a system call that monitors when a file descriptor becomes ready, \ie, the next \io operation on it will not cause the process to wait\footnote{In this context, ready means to \emph{some} operation can be performed without blocking. It does not mean that the last operation that return \lstinline|EAGAIN| will succeed on the next try. A file that is ready to read but has only 1 byte available would be an example of this distinction.}.

There are three options to monitor file descriptors in Linux\footnote{For simplicity, this section omits to mention \lstinline|pselect| and \lstinline|ppoll|. The difference between these system calls and \lstinline|select| and \lstinline|poll| respectively is not relevant for this discussion.}, @select@~\cite{MAN:select}, @poll@~\cite{MAN:poll} and @epoll@~\cite{MAN:epoll}. All three of these options offer a system call that blocks a \gls{kthrd} until at least one of many file descriptor becomes ready. The group of file descriptors being waited on is often referred to as the \newterm{interest set}.

\paragraph{\lstinline|select|} is the oldest of these options, it takes as an input a contiguous array of bits, where each bits represent a file descriptor of interest. On return, it modifies the set in place to identify which of the file descriptors changed status. This means that calling select in a loop requires re-initializing the array each time and the number of file descriptors supported has a hard limit. Another limit of @select@ is that once the call is started, the interest set can no longer be modified. Monitoring a new file descriptor generally requires aborting any in progress call to @select@\footnote{Starting a new call to \lstinline|select| in this case is possible but requires a distinct kernel thread, and as a result is not a acceptable multiplexing solution when the interest set is large and highly dynamic unless the number of parallel calls to select can be strictly bounded.}.

\paragraph{\lstinline|poll|} is an improvement over select, which removes the hard limit on the number of file descriptors and the need to re-initialize the input on every call. It works using an array of structures as an input rather than an array of bits, thus allowing a more compact input for small interest sets. Like @select@, @poll@ suffers from the limitation that the interest set cannot be changed while the call is blocked.

\paragraph{\lstinline|epoll|} further improves on these two functions, by allowing the interest set to be dynamically added to and removed from while a \gls{kthrd} is blocked on a call to @epoll@. This is done by creating an \emph{epoll instance} with a persistent intereset set and that is used across multiple calls. This advantage significantly reduces synchronization overhead on the part of the caller (in this case the \io subsystem) since the interest set can be modified when adding or removing file descriptors without having to synchronize with other \glspl{kthrd} potentially calling @epoll@.

However, all three of these system calls suffer from generality problems to some extent. The man page for @O_NONBLOCK@ mentions that ``[@O_NONBLOCK@] has no effect for regular files and block devices'', which means none of these three system calls are viable multiplexing strategies for these types of \io operations. Furthermore, @epoll@ has been shown to have some problems with pipes and ttys\cit{Peter's examples in some fashion}. Finally, none of these are useful solutions for multiplexing \io operations that do not have a corresponding file descriptor and can be awkward for operations using multiple file descriptors.

\subsection{The POSIX asynchronous I/O (AIO)}
An alternative to using @O_NONBLOCK@ is to use the AIO interface. Its interface lets programmers enqueue operations to be performed asynchronously by the kernel. Completions of these operations can be communicated in various ways, either by sending a Linux signal, spawning a new \gls{kthrd} or by polling for completion of one or more operation. For the purpose multiplexing operations, spawning a new \gls{kthrd} is counter-productive but a related solution is discussed in Section~\ref{io:morethreads}. Since using interrupts handlers can also lead to fairly complicated interactions between subsystems, I will concentrate on the different polling methods. AIO only supports read and write operations to file descriptors and those do not have the same limitation as @O_NONBLOCK@, \ie, the file descriptors can be regular files and blocked devices. It also supports batching more than one of these operations in a single system call.

AIO offers two different approach to polling. @aio_error@ can be used as a spinning form of polling, returning @EINPROGRESS@ until the operation is completed, and @aio_suspend@ can be used similarly to @select@, @poll@ or @epoll@, to wait until one or more requests have completed. For the purpose of \io multiplexing, @aio_suspend@ is the intended interface. Even if AIO requests can be submitted concurrently, @aio_suspend@ suffers from the same limitation as @select@ and @poll@, \ie, the interest set cannot be dynamically changed while a call to @aio_suspend@ is in progress. Unlike @select@ and @poll@ however, it also suffers from the limitation that it does not specify which requests have completed, meaning programmers then have to poll each request in the interest set using @aio_error@ to identify which requests have completed. This means that, like @select@ and @poll@ but not @epoll@, the time needed to examine polling results increases based in the total number of requests monitored, not the number of completed requests.

AIO does not seem to be a particularly popular interface, which I believe is in part due to this less than ideal polling interface. Linus Torvalds talks about this interface as follows :

\begin{displayquote}
	AIO is a horrible ad-hoc design, with the main excuse being "other,
	less gifted people, made that design, and we are implementing it for
	compatibility because database people - who seldom have any shred of
	taste - actually use it".

	But AIO was always really really ugly.

	\begin{flushright}
		-- Linus Torvalds\cit{https://lwn.net/Articles/671657/}
	\end{flushright}
\end{displayquote}

Interestingly, in this e-mail answer, Linus goes on to describe
``a true \textit{asynchronous system call} interface''
that does
``[an] arbitrary system call X with arguments A, B, C, D asynchronously using a kernel thread''
in
``some kind of arbitrary \textit{queue up asynchronous system call} model''.
This description is actually quite close to the interface described in the next section.

\subsection{\lstinline|io_uring|}
A very recent addition to Linux, @io_uring@\cite{MAN:io_uring} is a framework that aims to solve many of the problems listed with the above mentioned interfaces. Like AIO, it represents \io operations as entries added on a queue. But like @epoll@, new requests can be submitted while a blocking call waiting for requests to complete is already in progress. The @io_uring@ interface uses two ring buffers (referred to simply as rings) as its core, a submit ring to which programmers push \io requests and a completion buffer which programmers poll for completion.

One of the big advantages over the interfaces listed above is that it also supports a much wider range of operations. In addition to supporting reads and writes to any file descriptor like AIO, it supports other operations like @open@, @close@, @fsync@, @accept@, @connect@, @send@, @recv@, @splice@, \etc.

On top of these, @io_uring@ adds many ``bells and whistles'' like avoiding copies between the kernel and user-space with shared memory, allowing different mechanisms to communicate with device drivers and supporting chains of requests, \ie, requests that automatically trigger followup requests on completion.

\subsection{Extra Kernel Threads}\label{io:morethreads}
Finally, if the operating system does not offer any satisfying forms of asynchronous \io operations, a solution is to fake it by creating a pool of \glspl{kthrd} and delegating operations to them in order to avoid blocking \glspl{proc}. The is a compromise on multiplexing. In the worst case, where all \glspl{thrd} are consistently blocking on \io, it devolves into 1-to-1 threading. However, regardless of the frequency of \io operations, it achieves the fundamental goal of not blocking \glspl{proc} when \glspl{thrd} are ready to run. This approach is used by languages like Go\cit{Go} and frameworks like libuv\cit{libuv}, since it has the advantage that it can easily be used across multiple operating systems. This advantage is especially relevant for languages like Go, which offer an homogenous \glsxtrshort{api} across all platforms. As opposed to C, which has a very limited standard api for \io, \eg, the C standard library has no networking.

\subsection{Discussion}
These options effectively fall into two broad camps of solutions, waiting for \io to be ready versus waiting for \io to be completed. All operating systems that support asynchronous \io must offer an interface along one of these lines, but the details can vary drastically. For example, Free BSD offers @kqueue@~\cite{MAN:bsd/kqueue} which behaves similarly to @epoll@ but with some small quality of life improvements, while Windows (Win32)~\cit{https://docs.microsoft.com/en-us/windows/win32/fileio/synchronous-and-asynchronous-i-o} offers ``overlapped I/O'' which handles submissions similarly to @O_NONBLOCK@, with extra flags on the synchronous system call, but waits for completion events, similarly to @io_uring@.

For this project, I have chosen to use @io_uring@, in large parts due to its generality. While @epoll@ has been shown to be a good solution to socket \io (\cite{DBLP:journals/pomacs/KarstenB20}), @io_uring@'s transparent support for files, pipes and more complex operations, like @splice@ and @tee@, make it a better choice as the foundation for a general \io subsystem.

\section{Event-Engine}

The event engines reponsibility is to use the kernel interface to multiplex many \io operations onto few \glspl{kthrd}. In concrete terms, this means that \glspl{thrd} enter the engine through an interface, the event engines then starts the operation and parks the calling \glspl{thrd}, returning control to the \gls{proc}. The parked \glspl{thrd} are then rescheduled by the event engine once the desired operation has completed.

\subsection{\lstinline|io_uring| in depth}
Before going into details on the design of the event engine, I will present some more details on the usage of @io_uring@ which are important for the design of the engine.

\begin{figure}
	\centering
	\input{io_uring.pstex_t}
	\caption[Overview of \lstinline|io_uring|]{Overview of \lstinline|io_uring| \smallskip\newline Two ring buffer are used to communicate with the kernel, one for completions~(right) and one for submissions~(left). The completion ring contains entries, \newterm{CQE}s: Completion Queue Entries, that are produced by the kernel when an operation completes and then consumed by the application. On the other hand, the application produces \newterm{SQE}s: Submit Queue Entries, which it appends to the submission ring for the kernel to consume. Unlike the completion ring, the submission ring does not contain the entries directly, it indexes into the SQE array (denoted \emph{S}) instead.}
	\label{fig:iouring}
\end{figure}

Figure~\ref{fig:iouring} shows an overview of an @io_uring@ instance. Multiple @io_uring@ instances can be created, in which case they each have a copy of the data structures in the figure. New \io operations are submitted to the kernel following 4 steps which use the components shown in the figure.

\paragraph{First} an @sqe@ must be allocated from the pre-allocated array (denoted \emph{S} in Figure~\ref{fig:iouring}). This array is created at the same time as the @io_uring@ instance, is in kernel-locked memory, which means it is both visible by the kernel and the application, and has a fixed size determined at creation. How these entries are allocated is not important for the functionning of @io_uring@, the only requirement is that no entry is reused before the kernel has consumed it.

\paragraph{Secondly} the @sqe@ must be filled according to the desired operation. This step is straight forward, the only detail worth mentionning is that @sqe@s have a @user_data@ field that must be filled in order to match submission and completion entries.

\paragraph{Thirdly} the @sqe@ must be submitted to the submission ring, this requires appending the index of the @sqe@ to the ring following regular ring buffer steps: \lstinline|{ buffer[head] = item; head++ }|. Since the head is visible to the kernel, some memory barriers may be required to prevent the compiler from reordering these operations. Since the submission ring is a regular ring buffer, more than one @sqe@ can be added at once and the head can be updated only after the entire batch has been updated.

\paragraph{Finally} the kernel must be notified of the change to the ring using the system call @io_uring_enter@. The number of elements appended to the submission ring is passed as a parameter and the number of elements consumed is returned. The @io_uring@ instance can be constructed so that this step is not required, but this requires elevated privilege and early version of @io_uring@ had additionnal restrictions.

The completion side is simpler, applications call @io_uring_enter@ with the flag @IORING_ENTER_GETEVENTS@ to wait on a desired number of operations to complete. The same call can be used to both submit @sqe@s and wait for operations to complete. When operations do complete the kernel appends a @cqe@ to the completion ring and advances the head of the ring. Each @cqe@ contains the result of the operation as well as a copy of the @user_data@ field of the @sqe@ that triggered the operation. It is not necessary to call @io_uring_enter@ to get new events, the kernel can directly modify the completion ring, the system call is only needed if the application wants to block waiting on operations to complete.

The @io_uring_enter@ system call is protected by a lock inside the kernel. This means that concurrent call to @io_uring_enter@ using the same instance are possible, but there is can be no performance gained from parallel calls to @io_uring_enter@. It is possible to do the first three submission steps in parallel, however, doing so requires careful synchronization.

@io_uring@ also introduces some constraints on what the number of operations that can be ``in flight'' at the same time. Obviously, @sqe@s are allocated from a fixed-size array, meaning that there is a hard limit to how many @sqe@s can be submitted at once. In addition, the @io_uring_enter@ system call can fail because ``The  kernel [...] ran out of resources to handle [a request]'' or ``The application is attempting to overcommit the number of requests it can  have  pending.''. This requirement means that it can be required to handle bursts of \io requests by holding back some of the requests so they can be submitted at a later time.

\subsection{Multiplexing \io: Submission}
The submission side is the most complicated aspect of @io_uring@ and from the design decisions made in the submission side, the completion side effectively follows.

While it is possible to do the first steps of submission in parallel, the duration of the system call scales with number of entries submitted. The consequence of this is that how much parallelism can be used to prepare submissions for the next system call is limited. Beyond this limit, the length of the system call will be the throughput limiting factor. I have concluded from early experiments that preparing submissions seems to take about as long as the system call itself, which means that with a single @io_uring@ instance, there is no benefit in terms of \io throughput to having more than two \glspl{hthrd}. Therefore the design of the submission engine must manage multiple instances of @io_uring@ running in parallel, effectively sharding @io_uring@ instances. Similarly to scheduling, this sharding can be done privately, \ie, one instance per \glspl{proc}, or in decoupled pools, \ie, a pool of \glspl{proc} use a pool of @io_uring@ instances without one-to-one coupling between any given instance and any given \gls{proc}.

\subsubsection{Pool of Instances}
One approach is to have multiple shared instances. \Glspl{thrd} attempting \io operations pick one of the available instances and submits operations to that instance. Since the completion will be sent to the same instance, all instances with pending operations must be polled continously\footnote{As will be described in Chapter~\ref{practice}, this does not translate into constant cpu usage.}. Since there is no coupling between \glspl{proc} and @io_uring@ instances in this approach, \glspl{thrd} running on more than one \gls{proc} can attempt to submit to the same instance concurrently. Since @io_uring@ effectively sets the amount of sharding needed to avoid contention on its internal locks, performance in this approach is based on two aspects: the synchronization needed to submit does not induce more contention than @io_uring@ already does and the scheme to route \io requests to specific @io_uring@ instances does not introduce contention. This second aspect has an oversized importance because it comes into play before the sharding of instances, and as such, all \glspl{hthrd} can contend on the routing algorithm.

Allocation in this scheme can be handled fairly easily. Free @sqe@s, \ie, @sqe@s that aren't currently being used to represent a request, can be written to safely and have a field called @user_data@ which the kernel only reads to copy to @cqe@s. Allocation also requires no ordering guarantee as all free @sqe@s are interchangeable. This requires a simple concurrent bag. The only added complexity is that the number of @sqe@s is fixed, which means allocation can fail. This failure needs to be pushed up to the routing algorithm, \glspl{thrd} attempting \io operations must not be directed to @io_uring@ instances without any available @sqe@s. Ideally, the routing algorithm would block operations up-front if none of the instances have available @sqe@s.

Once an @sqe@ is allocated, \glspl{thrd} can fill them normally, they simply need to keep trac of the @sqe@ index and which instance it belongs to.

Once an @sqe@ is filled in, what needs to happen is that the @sqe@ must be added to the submission ring buffer, an operation that is not thread-safe on itself, and the kernel must be notified using the @io_uring_enter@ system call. The submission ring buffer is the same size as the pre-allocated @sqe@ buffer, therefore pushing to the ring buffer cannot fail\footnote{This is because it is invalid to have the same \lstinline|sqe| multiple times in the ring buffer.}. However, as mentioned, the system call itself can fail with the expectation that it will be retried once some of the already submitted operations complete. Ideally, when multiple \glspl{thrd} attempt to submit operations to the same @io_uring@ instance, all requests would be batched together and one of the \glspl{thrd} would do the system call on behalf of the others, referred to as the \newterm{submitter}. In practice however, it is important that the \io requests are not left pending indefinately and as such, it may be required to have a current submitter and a next submitter. Indeed, as long as there is a ``next'' submitter, \glspl{thrd} submitting new \io requests can move on, knowing that some future system call will include their request. Once the system call is done, the submitter must also free @sqe@s so that the allocator can reused them.

Finally, the completion side is much simpler since the @io_uring@ system call enforces a natural synchronization point. Polling simply needs to regularly do the system call and go through the produced @cqe@s and communicate the result back to the original \gls{thrd}. Since @cqe@s only own a signed 32 bit result, in addition to the copy of the @user_data@ field, all that is needed to communicate the result is a simple future~\cite{wiki:future}. As mentioned in the last paragraph, the polling is also responsible for unlocking submitters that are blocked because the system failed due to being busy. A simple approach to polling is to allocate a \gls{thrd} per @io_uring@ instance and simply let the poller \glspl{thrd} poll their respective instances when scheduled. This design is especially convinient for reasons explained in Chapter~\ref{practice}.


\subsubsection*{Private Instances}
Another approach is to simply create one ring instance per \gls{proc}. This alleviate the need for synchronization on the submissions, requiring only that \glspl{thrd} are not interrupted in between two submission steps. This is effectively the same requirement as using @thread_local@ variables. Since @sqe@s that are allocated must be submitted to the same ring, on the same \gls{proc}, this effectively forces the application to submit @sqe@s in allocation order\footnote{The actual requirement is that \glspl{thrd} cannot context switch between allocation and submission. This requirement means that from the subsystem's point of view, the allocation and submission are sequential. To remove this requirement, a \gls{thrd} would need the ability to ``yield to a specific \gls{proc}'', \ie, park with the promise that it will be run next on a specific \gls{proc}, the \gls{proc} attached to the correct ring. This is not a current or planned feature of \CFA.}, greatly simplifying both allocation and submission. In this design, allocation and submission form a ring partitionned ring buffer as shown in Figure~\ref{fig:pring}.

\begin{figure}
	\centering
	\input{pivot_ring.pstex_t}
	\caption[Partitionned ring buffer]{Partitionned ring buffer \smallskip\newline Allocated sqes are appending to the first partition. When submitting, the partition is simply advanced to include all the sqes that should be submitted. The kernel considers the partition as the head of the ring.}
	\label{fig:pring}
\end{figure}

\subsection{Multiplexing \io: Completion}

\section{Interface}
Finally, the last important part of the \io subsystem is it's interface. There are multiple approaches that can be offered to programmers, each with advantages and disadvantages. The new \io subsystem can replace the C runtime's API or extend it. And in the later case the interface can go from very similar to vastly different. The following sections discuss some useful options using @read@ as an example. The standard Linux interface for C is :

@ssize_t read(int fd, void *buf, size_t count);@.

\subsection{Replacement}
Replacing the C \glsxtrshort{api}

\subsection{Synchronous Extension}

\subsection{Asynchronous Extension}

\subsection{Interface directly to \lstinline|io_uring|}