\chapter{Introduction}\label{intro}
\todo{A proper intro}

Any shared system needs scheduling.
Computer systems share multiple resources across many threads of execution, even on single user computers like a laptop.
Scheduling occurs at discreet points when there are transitions in a system.
For example, a thread cycles through the following transitions during its execution.
\begin{center}
\input{executionStates.pstex_t}
\end{center}
These \newterm{state transition}s are initiated in response to events (\Index{interrupt}s):
\begin{itemize}
\item
entering the system (new $\rightarrow$ ready)
\item
timer alarm for preemption (running $\rightarrow$ ready)
\item
long term delay versus spinning (running $\rightarrow$ blocked)
\item
blocking ends, \ie network or I/O completion (blocked $\rightarrow$ ready)
\item
normal completion or error, \ie segment fault (running $\rightarrow$ halted)
\end{itemize}
Key to scheduling is that a thread cannot bypass the ``ready'' state during a transition so the scheduler maintains complete control of the system.

In detail, in a computer system with multiple processors and work units, there exists the problem of mapping work onto processors in an efficient manner, called \newterm{scheduling}.
These systems are normally \newterm{open}, meaning new work arrives from an external source or spawned from an existing work unit.
Scheduling is a zero-sum game as computer processors normally have a fixed, maximum number of cycles per unit time.
(Frequency scaling and turbot boost add a degree of complexity that can be ignored in this discussion without loss of generality.)

On a computer system, the scheduler takes a sequence of work requests in the form of threads and attempts to complete the work, subject to performance objectives, such as resource utilization.
A general-purpose dynamic-scheduler for an open system cannot anticipate future work requests, so its performance is rarely optimal.
Even with complete knowledge of arrive order and work, an optimal solution is NP (bin packing).
However, schedulers do take advantage of regularities in work patterns to produce excellent solutions.
Nevertheless, schedulers are a series of compromises, occasionally with some static or dynamic tuning parameters to enhance specific patterns.

When the workload exceeds the capacity of the processors, \ie work cannot be executed immediately, it is placed on a queue for subsequent service, called a \newterm{ready queue}.
(In real-life, a work unit (person) can \newterm{balk}, and leave the system rather than wait.)
Ready queues organize threads for scheduling, which indirectly organizes the work to be performed.
The structure of ready queues forms a spectrum.
At one end is the single-queue multi-server (SIMS) and at the other end is the multi-queue multi-server (MOMS).
\begin{center}
\begin{tabular}{l|l}
\multicolumn{1}{c|}{\textbf{SIMS}} & \multicolumn{1}{c}{\textbf{MOMS}} \\
\hline
\raisebox{0.5\totalheight}{\input{SQMS.pstex_t}} & \input{MQMSG.pstex_t}
\end{tabular}
\end{center}
(While \newterm{pipeline} organizations also exist, \ie chaining schedulers together, they do not provide an advantage in this context.)

The three major optimization criteria for a scheduler are:
\begin{enumerate}[leftmargin=*]
\item
\newterm{load balancing}: available work is distributed so no processor is idle when work is available.

\noindent
Eventual progress for each work unit is often an important consideration, \ie no starvation.
\item
\newterm{affinity}: processors access state through a complex memory hierarchy, so it is advantageous to keep a work unit's state on a single or closely bound set of processors.

\noindent
Essentially, all multi-processor computers have non-uniform memory access (NUMB), with one or more quantized steps to access data at different levels (steps) in the memory hierarchy.
When a system has a large number of independently executing threads, affinity is impossible because of \newterm{thread churn}.
That is, threads must be scheduled on multiple processors to obtain high processors utilization because the number of threads $\ggg$ processors.

\item
\newterm{contention}: safe access of shared objects by multiple processors requires mutual exclusion in the form of locking\footnote{
Lock-free data-structures is still locking because all forms of locking invoke delays.}

\noindent
Locking cost and latency increases significantly with the number of processors accessing a shared object.
\end{enumerate}

SIMS has perfect load-balancing but poor affinity and high contention by the processors, because of the single queue.
MOMS has poor load-balancing but perfect affinity and no contention, because each processor has its own queue.
Between these two schedulers are a host of options, \ie adding an optional global, shared ready-queue to MOMS.
Significant research effort has also looked at load sharing/stealing among queues, when a ready queue is too long or short, respectively.
These approaches attempt to perform better load-balancing at the cost of affinity and contention.
Load sharing/stealing schedulers attempt to push/pull work units to/from other ready queues

Note, a computer system is often lightly loaded (or has no load);
any scheduler can handle this situation, \ie all schedulers are equal in this context.
As the workload increases, there is a point where some schedulers begin to perform better than others based on the above criteria.
A poorer scheduler might saturate for some reason and not be able to assign available work to idle processors, \ie processors are spinning when work is available.


\section{\CFA programming language}

\todo{Brief discussion on \CFA features used in the thesis.}

The \CFA programming language~\cite{cfa:frontpage,cfa:typesystem} extends the C programming language by adding modern safety and productivity features, while maintaining backwards compatibility. Among its productivity features, \CFA supports user-level threading~\cite{Delisle21} allowing programmers to write modern concurrent and parallel programs.
My previous master's thesis on concurrent in \CFA focused on features and interfaces.
This Ph.D.\ thesis focuses on performance, introducing \glsxtrshort{api} changes only when required by performance considerations. Specifically, this work concentrates on scheduling and \glsxtrshort{io}. Prior to this work, the \CFA runtime used a strict \glsxtrshort{fifo} \gls{rQ} and  no non-blocking I/O capabilities at the user-thread level.

As a research project, this work builds exclusively on newer versions of the Linux operating-system and gcc/clang compilers. While \CFA is released, supporting older versions of Linux ($<$~Ubuntu 16.04) and gcc/clang compilers ($<$~gcc 6.0) is not a goal of this work.


\section{Contributions}
\label{s:Contributions}

This work provides the following contributions in the area of user-level scheduling in an advanced programming-language runtime-system:
\begin{enumerate}[leftmargin=*]
\item
Guarantee no thread or set of threads can conspire to prevent other threads from executing.
\begin{itemize}
\item
There must exist some form of preemption to prevent CPU-bound threads from gaining exclusive access to one or more processors.
\item
There must be a scheduler guarantee that threads are executed after CPU-bound threads are preempted.
Otherwise, CPU-bound threads can immediately rescheduled, negating the preemption.
\end{itemize}
Hence, the runtime/scheduler provides a notion of fairness that is impossible for a programmer to violate.
\item
Provide comprehensive scheduling across all forms of preemption and blocking, where threads move from the running to ready or blocked states.

Once a thread stops running, the processor executing it must be rescheduled, if possible.
Knowing what threads are in the ready state for scheduling is difficult because blocking operations complete asynchronous and with poor notification.
\item
Efficiently deal with unbalanced workloads among processors.

Simpler to 
\item
Efficiently deal with idle processors when there is less work than the available computing capacity.
\end{enumerate}
