source: doc/theses/mike_brooks_MMath/list.tex@ 6fe4a7f

\chapter{Linked List}

This chapter presents my work on designing and building a linked-list library for \CFA.
Due to time limitations and the needs expressed by the \CFA runtime developers, I focussed on providing a doubly-linked list, and its bidirectionally iterators for traversal. 
Simpler data-structures, like stack and queue, can be built from the doubly-linked mechanism with only a slight storage/performance cost because of the unused link field.
Reducing to data-structures with a single link follows directly from the more complex doubly-links and its iterators.


\section{Plan-9 Inheritance}
\label{s:Plan9Inheritance}

This chapter uses a form of inheritance from the Plan-9 C dialect~\cite[\S~3.3]{Thompson90new}, which is supported by @gcc@ and @clang@ using @-fplan9-extensions@.
\CFA has its own variation of the Plan-9 mechanism, where the nested type is denoted using @inline@.
\begin{cfa}
union U {  int x;  double y;  char z; };
struct W { int i;  double j;  char k; };
struct S {
        @inline@ struct W;  $\C{// extended Plan-9 inheritance}$
        unsigned int tag;
        @inline@ U;                     $\C{// extended Plan-9 inheritance}$
} s;
\end{cfa}
Inline inheritance is containment, where the inlined field is unnamed and the type's internal fields are hoisted into the containing structure.
Hence, the field names must be unique, unlike \CC nested types, but any inlined type-names are at a nested scope level, unlike aggregate nesting in C.
Note, the position of the containment is normally unimportant, unless there is some form of memory or @union@ overlay.
Finally, the inheritance declaration of @U@ is not prefixed with @union@.
Like \CC, \CFA allows optional prefixing of type names with their kind, \eg @struct@, @union@, and @enum@, unless there is ambiguity with variable names in the same scope.

\VRef[Figure]{f:Plan9Polymorphism} shows the key polymorphic feature of Plan-9 inheritance: implicit conversion of values and pointers for nested types.
In the example, there are implicit conversions from @S@ to @U@ and @S@ to @W@, extracting the appropriate value or pointer for the substructure.
\VRef[Figure]{f:DiamondInheritancePattern} shows complex multiple inheritance patterns are possible, like the \newterm{diamond pattern}~\cite[\S~6.1]{Stroustrup89}\cite[\S~4]{Cargill91}.
Currently, \CFA type-system does not support @virtual@ inheritance.

\begin{figure}
\begin{cfa}
void f( U, U * ); $\C{// value, pointer}$
void g( W, W * ); $\C{// value, pointer}$
U u, * up;   W w, * wp;   S s, * sp;
u = s;   up = sp; $\C{// value, pointer}$
w = s;   wp = sp; $\C{// value, pointer}$
f( s, &s ); $\C{// value, pointer}$
g( s, &s ); $\C{// value, pointer}$
\end{cfa}
\caption{Plan-9 Polymorphism}
\label{f:Plan9Polymorphism}
\end{figure}

\begin{figure}
\setlength{\tabcolsep}{10pt}
\begin{tabular}{ll@{}}
\multicolumn{1}{c}{\CC} & \multicolumn{1}{c}{\CFA}      \\
\begin{c++}
struct B { int b; };
struct L : @public B@ { int l, w; };
struct R : @public B@ { int r, w; };
struct T : @public L, R@ { int t; };

T t = { { { 5 }, 3, 4 }, { { 8 }, 6, 7 }, 3 };
t.t = 42;
t.l = 42;
t.r = 42;
((L &)t).b = 42;  // disambiguate
\end{c++}
&
\begin{cfa}
struct B { int b; };
struct L { @inline B;@ int l, w; };
struct R { @inline B;@ int r, w; };
struct T { @inline L; inline R;@ int t; };

T t = { { { 5 }, 3, 4 }, { { 8 }, 6, 7 }, 3 };
t.t = 42;
t.l = 42;
t.r = 42;
((L &)t).b = 42;  // disambiguate, proposed solution t.L.b = 42;
\end{cfa}
\end{tabular}
\caption{Diamond Non-Virtual Inheritance Pattern}
\label{f:DiamondInheritancePattern}
\end{figure}


\section{Features}

The following features directed this project, where the goal is high-performance list operations required by \CFA runtime components, like the threading library.


\subsection{Core Design Issues}

The doubly-linked list attaches links intrusively, supports multiple link axes, integrates with user code via the type system, treats its ends uniformly, and identifies a list using an explicit head.
This design covers system and data management issues stated in \VRef{toc:lst:issue}.

\VRef[Figure]{fig:lst-features-intro} continues the running @req@ example from \VRef[Figure]{fig:lst-issues-attach} using the \CFA list @dlist@.
The \CFA link attachment is intrusive so the resulting memory layout is per user node, as for the LQ version of \VRef[Figure]{f:Intrusive}.
The \CFA framework provides generic type @dlink( T )@ (not to be confused with @dlist@) for the two link fields (front and back).
A user inserts the links into the @req@ structure via \CFA inline-inheritance \see{\VRef{s:Plan9Inheritance}}.
Lists leverage the automatic conversion of a pointer to anonymous inline field for assignments and function calls.
Therefore, a reference to a @req@ is implicitly convertible to @dlink@ in both contexts.
The links in @dlist@ point at another embedded @dlist@ node, know the offsets of all links (data is abstract but accessible), and any field-offset arithmetic or link-value changes are safe and abstract.

\begin{figure}
    \lstinput{20-30}{lst-features-intro.run.cfa}
    \caption[Multiple link axes in \CFA list library]{
        Demonstration of the running \lstinline{req} example, done using the \CFA list library.
        This example is equivalent to the three approaches in \VRef[Figure]{fig:lst-issues-attach}.
    }
    \label{fig:lst-features-intro}
\end{figure}

\VRef[Figure]{f:dlistOutline} shows the outline for types @dlink@ and @dlist@.
Note, the first @forall@ clause is distributed across all the declaration in its containing block, eliminating repetition on each declaration.
The second nested @forall@ on @dlist@ is also distributed and adds an optional second type parameter, @tLinks@, denoting the linking axis \see{\VRef{s:Axis}}, \ie the kind of list this node can appear on.

\begin{figure}
\begin{cfa}
forall( tE & ) {                                        $\C{// distributed}$
        struct @dlink@ { ...  };                $\C{// abstract type}$
        static inline void ?{}( dlink( tE ) & this );  $\C{// constructor}$

        forall( tLinks & = dlink( tE ) ) { $\C{// distributed, default type for axis}$
                struct @dlist@ { ... };         $\C{// abstract type}$
                static inline void ?{}( dlist( tE, tLinks ) & this ); $\C{// constructor}$
        }
}
\end{cfa}
\caption{\lstinline{dlisk} / \lstinline{dlist} Outline}
\label{f:dlistOutline}
\end{figure}

\VRef[Figure]{fig:lst-features-multidir} shows how the \CFA library supports multi-inline links, so a node can be on one or more lists simultaneously (axes).
The declaration of @req@ has two inline-inheriting @dlink@ occurrences.
The first of these gives a type named @req.by_pri@, @req@ inherits from it, and it inherits from @dlink@.
The second line @req.by_rqr@ is similar to @req.by_pri@.
Thus, there is a diamond, non-virtual, inheritance from @req@ to @dlink@, with @by_pri@ and @by_rqr@ being the mid-level types \see{\VRef[Figure]{f:DiamondInheritancePattern}}.

The declarations of the list-head objects, @reqs_pri@, @reqs_rqr_42@, @reqs_rqr_17@, and @reqs_rqr_99@, bind which link nodes in @req@ are used by this list.
Hence, the type of the variable @reqs_pri@, @dlist(req, req.by_pri)@, means operations called on @reqs_pri@ implicitly select (disambiguate) the correct @dlink@s, \eg the calls @insert_first(reqs_pri, ...)@ imply, ``here, we are working by priority.''
As in \VRef[Figure]{fig:lst-issues-multi-static}, three lists are constructed, a priority list containing all nodes, a list with only nodes containing the value 42, and a list with only nodes containing the value 17.

\begin{figure}
\centering
\begin{tabular}{@{}ll@{}}
\begin{tabular}{@{}l@{}}
    \lstinput{20-31}{lst-features-multidir.run.cfa} \\
    \lstinput{43-71}{lst-features-multidir.run.cfa}
    \end{tabular}
        &
        \lstinput[language=C++]{20-60}{lst-issues-multi-static.run.c}
        \end{tabular}

\caption{
        Demonstration of multiple static link axes done in the \CFA list library.
        The right example is from \VRef[Figure]{fig:lst-issues-multi-static}.
                The left \CFA example does the same job.
    }
    \label{fig:lst-features-multidir}
\end{figure}

The list library also supports the common case of single directionality more naturally than LQ.  
Returning to \VRef[Figure]{fig:lst-features-intro}, the single-axis list has no contrived name for the link axis as it uses the default type in the definition of @dlist@;
in contrast, the LQ list in \VRef[Figure]{f:Intrusive} adds the unnecessary field name @d@.
In \CFA, a single axis list sets up a single inheritance with @dlink@, and the default list axis is to itself.

When operating on a list with several axes and operations that do not take the list head, the list axis can be ambiguous.
For example, a call like @insert_after( r1, r2 )@ does not have enough information to know which axes to select implicitly.
Is @r2@ supposed to be the next-priority request after @r1@, or is @r2@ supposed to join the same-requester list of @r1@?
As such, the \CFA type-system gives an ambiguity error for this call.
There are multiple ways to resolve the ambiguity.
The simplest is an explicit cast on each call to select the specific axis, \eg @insert_after( (by_pri)r1, r2 )@.
However, multiple explicit casts are tedious and error-prone.
To mitigate this issue, the list library provides a hook for applying the \CFA language's scoping and priority rules.
\begin{cfa}
with ( DLINK_VIA( req, req.pri ) ) insert_after( r1, r2 );
\end{cfa}
Here, the @with@ statement opens the scope of the object type for the expression;
hence, the @DLINK_VIA@ result causes one of the list axes to become a more attractive candidate to \CFA's overload resolution.
This boost can be applied across multiple statements in a block or an entire function body.
\begin{cquote}
\setlength{\tabcolsep}{15pt}
\begin{tabular}{@{}ll@{}}
\begin{cfa}
@with( DLINK_VIA( req, req.pri ) ) {@
        ...  insert_after( r1, r2 );  ...
@}@
\end{cfa}
&
\begin{cfa}
void f() @with( DLINK_VIA( req, req.pri ) ) {@
        ... insert_after( r1, r2 ); ...
@}@
\end{cfa}
\end{tabular}
\end{cquote}
Within the @with@, the code acts as if there is only one list axis, without explicit casting.

Unlike the \CC template container-types, the \CFA library works completely within the type system;
both @dlink@ and @dlist@ are ordinary types, not language macros.
There is no textual expansion other than header-included static-inline function for performance.
Hence, errors in user code are reported only with mention of the library's declarations, versus long template names in error messages.
Finally, the library is separately compiled from the usage code, modulo inlining.

The \CFA library works in headed and headless modes.
\PAB{TODO: elaborate.}


\section{List API}

\VRef[Figure]{f:ListAPI} shows the API for the doubly-link list operations, where each is explained.
\begin{itemize}[leftmargin=*]
\item
@isListed@ returns true if the node is an element of a list and false otherwise.
\item
@isEmpty@ returns true if the list has no nodes and false otherwise.
\item
@first@ returns a reference to the first node of the list without removing it or @0p@ if the list is empty.
\item
@last@ returns a reference to the last node of the list without removing it or @0p@ if the list is empty.
\item
@insert_before@ adds a node before a specified node \see{\lstinline{insert_last} for insertion at the end}\footnote{
Some list packages allow \lstinline{0p} (\lstinline{nullptr}) for the before/after node implying insert/remove at the start/end of the list, respectively.
However, this inserts an \lstinline{if} statement in the fastpath of a potentially commonly used list operation.}
\item
@insert_after@ adds a node after a specified node \see{\lstinline{insert_first} for insertion at the start}\footnotemark[\value{footnote}].
\item
@remove@ removes a specified node from the list (any location) and returns a reference to the node.
\item
@iter@ create an iterator for the list.
\item
@recede@ returns true if the iterator cursor is advanced to the previous (predecessor, towards first) node before the prior cursor node and false otherwise.
\item
@advance@ returns true if the iterator cursor is advanced to the next (successor, towards last) node after the prior cursor node and false otherwise.
\item
@isFirst@ returns true if the node is the first node in the list and false otherwise.
\item
@isLast@ returns true if the node is the last node in the list and false otherwise.
\item
@pred@ returns a reference to the previous (predecessor, towards first) node before a specified node or @0p@ if a specified node is the first node in the list.
\item
@next@ returns a reference to the next (successor, towards last) node after a specified node or @0p@ if a specified node is the last node in the list.
\item
@insert_first@ adds a node to the start of the list so it becomes the first node and returns a reference to the node for cascading.
\item
@insert_last@ adds a node to the end of the list so it becomes the last node and returns returns a reference to the node for cascading.
\item
@remove_first@ removes the first node and returns a reference to it or @0p@ if the list is empty.
\item
@remove_last@ removes the last node and returns a reference to it or @0p@ if the list is empty.
\item
@transfer@ transfers all nodes from the @from@ list to the end of the @to@ list; the @from@ list is empty after the transfer.
\item
@split@ transfers the @from@ list up to node to the end of the @to@ list; the @from@ list becomes the list after the node.
The node must be in the @from@ list.
\end{itemize}

\begin{figure}
\begin{cfa}
E & isListed( E & node );
E & isEmpty( dlist( E ) & list );
E & first( dlist( E ) & list );
E & last( dlist( E ) & list );
E & insert_before( E & before, E & node );
E & insert_after( E & after, E & node );
E & remove( E & node );
E & iter( dlist( E ) & list );
bool advance( E && refx );
bool recede( E && refx );
bool isFirst( E & node );
bool isLast( E & node );
E & prev( E & node );
E & next( E & node );
E & insert_first( dlist( E ) & list, E & node );
E & insert_last( dlist( E ) & list, E & node );
E & remove_first( dlist( E ) & list );
E & remove_last( dlist( E ) & list );
void transfer( dlist( E ) & to, dlist( E ) & from ) {
void split( dlist( E ) & to, dlist( E ) & from, E & node ) {
\end{cfa}
\caption{\CFA List API}
\label{f:ListAPI}
\end{figure}


\subsection{Iteration}

It is possible to iterate through a list manually or using a set of standard macros.
\VRef[Figure]{f:IteratorTemple} shows the iterator template, managing a list of nodes, used throughout the following iterator examples.
Each example assumes its loop body prints the value in the current node.

\begin{figure}
\begin{cfa}
#include <fstream.hfa>
#include <list.hfa>

struct node {
        int v;
        inline dlink(node);
};
int main() {
        dlist(node) list;
        node n1 = { 1 }, n2 = { 2 }, n3 = { 3 }, n4 = { 4 };
        insert( list, n1 );   insert( list, n2 );   insert( list, n3 );   insert( list, n4 );
        sout | nlOff;
        for ( ... ) @sout | it.v | ",";   sout | nl;@ // iterator examples in text
        remove( n1 );   remove( n2 );   remove( n3 );   remove( n4 );
}
\end{cfa}
\caption{Iterator Temple}
\label{f:IteratorTemple}
\end{figure}

The manual method is low level but allows complete control of the iteration.
The list cursor (index) can be either a pointer or a reference to a node in the list.
The choice depends on how the programmer wants to access the fields: @it->f@ or @it.f@.
The following examples use a reference because the loop body manipulates the node values rather than the list pointers.
The end of iteration is denoted by the loop cursor returning @0p@.

\noindent
Iterating forward and reverse through the entire list.
\begin{cquote}
\setlength{\tabcolsep}{15pt}
\begin{tabular}{@{}l|l@{}}
\begin{cfa}
for ( node & it = @first@( list ); &it /* != 0p */ ; &it = &@next@( it ) ...
for ( node & it = @last@( list ); &it; &it = &@prev@( it ) ) ...
\end{cfa}
&
\begin{cfa}
1, 2, 3, 4,
4, 3, 2, 1,
\end{cfa}
\end{tabular}
\end{cquote}
Iterating forward and reverse from a starting node through the remaining list.
\begin{cquote}
\setlength{\tabcolsep}{15pt}
\begin{tabular}{@{}l|l@{}}
\begin{cfa}
for ( node & it = @n2@; &it; &it = &@next@( it ) ) ...
for ( node & it = @n3@; &it; &it = &@prev@( it ) ) ...
\end{cfa}
&
\begin{cfa}
2, 3, 4,
3, 2, 1,
\end{cfa}
\end{tabular}
\end{cquote}
Iterating forward and reverse from a starting node to an ending node through the contained list.
\begin{cquote}
\setlength{\tabcolsep}{15pt}
\begin{tabular}{@{}l|l@{}}
\begin{cfa}
for ( node & it = @n2@; &it @!= &n4@; &it = &@next@( it ) ) ...
for ( node & it = @n4@; &it @!= &n2@; &it = &@prev@( it ) ) ...
\end{cfa}
&
\begin{cfa}
2, 3,
4, 3,
\end{cfa}
\end{tabular}
\end{cquote}
Iterating forward and reverse through the entire list using the shorthand start at the list head and pick a axis.
In this case, @advance@ and @recede@ return a boolean, like \CC @while ( cin >> i )@.
\begin{cquote}
\setlength{\tabcolsep}{15pt}
\begin{tabular}{@{}l|l@{}}
\begin{cfa}
for ( node & it = @iter@( list ); @advance@( it ); ) ...
for ( node & it = @iter@( list ); @recede@( it ); ) ...
\end{cfa}
&
\begin{cfa}
1, 2, 3, 4,
4, 3, 2, 1,
\end{cfa}
\end{tabular}
\end{cquote}
Finally, there are convenience macros that look like @foreach@ in other programming languages.
Iterating forward and reverse through the entire list.
\begin{cquote}
\setlength{\tabcolsep}{15pt}
\begin{tabular}{@{}l|l@{}}
\begin{cfa}
FOREACH( list, it ) ...
FOREACH_REV( list, it ) ...
\end{cfa}
&
\begin{cfa}
1, 2, 3, 4,
4, 3, 2, 1,
\end{cfa}
\end{tabular}
\end{cquote}
Iterating forward and reverse through the entire list or until a predicate is triggered.
\begin{cquote}
\setlength{\tabcolsep}{15pt}
\begin{tabular}{@{}l|l@{}}
\begin{cfa}
FOREACH_COND( list, it, @it.v == 3@ ) ...
FOREACH_REV_COND( list, it, @it.v == 1@ ) ...
\end{cfa}
&
\begin{cfa}
1, 2,
4, 3, 2,
\end{cfa}
\end{tabular}
\end{cquote}
Macros are not ideal, so future work is to provide a language-level @foreach@ statement, like \CC.
Finally, a predicate can be added to any of the manual iteration loops.
\begin{cquote}
\setlength{\tabcolsep}{15pt}
\begin{tabular}{@{}l|l@{}}
\begin{cfa}
for ( node & it = first( list ); &it @&& !(it.v == 3)@; &it = &next( it ) ) ...
for ( node & it = last( list ); &it @&& !(it.v == 1)@; &it = &prev( it ) ) ...
for ( node & it = iter( list ); advance( it ) @&& !(it.v == 3)@; ) ...
for ( node & it = iter( list ); recede( it ) @&& !(it.v == 1)@; ) ...
\end{cfa}
&
\begin{cfa}
1, 2,
4, 3, 2,
1, 2,
4, 3, 2,
\end{cfa}
\end{tabular}
\end{cquote}

\begin{comment}
Many languages offer an iterator interface for collections, and a corresponding for-each loop syntax for consuming the items through implicit interface calls.
\CFA does not yet have a general-purpose form of such a feature, though it has a form that addresses some use cases.
This section shows why the incumbent \CFA pattern does not work for linked lists and gives the alternative now offered by the linked-list library.
Chapter 5 [TODO: deal with optimism here] presents a design that satisfies both uses and accommodates even more complex collections.

The current \CFA extensible loop syntax is:
\begin{cfa}
for( elem; end )
for( elem; begin ~ end )
for( elem; begin ~ end ~ step )
\end{cfa}
Many derived forms of @begin ~ end@ exist, but are used for defining numeric ranges, so they are excluded from the linked-list discussion.
These three forms are rely on the iterative trait:
\begin{cfa}
forall( T ) trait Iterate {
        void ?{}( T & t, zero_t );
        int ?<?( T t1, T t2 );
        int ?<=?( T t1, T t2 );
        int ?>?( T t1, T t2 );
        int ?>=?( T t1, T t2 );
        T ?+=?( T & t1, T t2 );
        T ?+=?( T & t, one_t );
        T ?-=?( T & t1, T t2 );
        T ?-=?( T & t, one_t );
}
\end{cfa}
where @zero_t@ and @one_t@ are constructors for the constants 0 and 1.
The simple loops above are abbreviates for:
\begin{cfa}
for( typeof(end) elem = @0@; elem @<@ end; elem @+=@ @1@ )
for( typeof(begin) elem = begin; elem @<@ end; elem @+=@ @1@ )
for( typeof(begin) elem = @0@; elem @<@ end; elem @+=@ @step@ )
\end{cfa}
which use a subset of the trait operations.
The shortened loop works well for iterating a number of times or through an array.
\begin{cfa}
for ( 20 ) // 20 iterations
for ( i: 1 ~= 21 ~ 2 ) // odd numbers
for ( i; n ) total += a[i]; // subscripts
\end{cfa}
which is similar to other languages, like JavaScript.
\begin{cfa}
for ( i in a ) total += a[i];
\end{cfa}
Albeit with different mechanisms for expressing the array's length.
It might be possible to take the \CC iterator:
\begin{c++}
for ( list<int>::iterator it=mylist.begin(); it != mylist.end(); ++it )
\end{c++}
and convert it to the \CFA form
\begin{cfa}
for ( it; begin() ~= end() )
\end{cfa}
by having a list operator @<=@ that just looks for equality, and @+=@ that moves to the next node, \etc.

However, the list usage is contrived, because a list does use its data values for relational comparison, only links for equality comparison.
Hence, the focus of a list iterator's stopping condition is fundamentally different.
So, iteration of a linked list via the existing loop syntax is to ask whether this syntax can also do double-duty for iterating values.
That is, to be an analog of JavaScript's @for..of@ syntax:
\begin{cfa}
for ( e of a ) total += e;
\end{cfa}

The \CFA team will likely implement an extension of this functionality that moves the @~@ syntax from being part of the loop, to being a first-class operator (with associated multi-pace operators for the elided derived forms).
With this change, both @begin ~ end@ and @end@ (in context of the latter ``two-place for'' expression) parse as \emph{ranges}, and the loop syntax becomes, simply:
\begin{cfa}
    for( elem; rangeExpr )
\end{cfa}
The expansion and underlying API are under discussion.
TODO: explain pivot from ``is at done?'' to ``has more?''
Advantages of this change include being able to pass ranges to functions, for example, projecting a numerically regular subsequence of array entries, and being able to use the loop syntax to cover more collection types, such as looping over the keys of a hashtable.

When iterating an empty list, the question, ``Is there a further element?'' needs to be posed once, receiving the answer, ``no.''
When iterating an $n$-item list, the same question gets $n$ ``yes'' answers (one for each element), plus one ``no'' answer, once there are no more elements; the question is posed $n+1$ times.

When iterating an empty list, the question, ``What is the value of the current element?'' is never posed, nor is the command, ``Move to the next element,'' issued.  When iterating an $n$-item list, each happens $n$ times.

So, asking about the existence of an element happens once more than retrieving an element's value and advancing the position.

Many iteration APIs deal with this fact by splitting these steps across different functions, and relying on the user's knowledge of iterator state to know when to call each.  In Java, the function @hasNext@ should be called $n+1$ times and @next@ should be called $n$ times (doing the double duty of advancing the iteration and returning a value).  In \CC, the jobs are split among the three actions, @it != end@, @it++@ and @*it@, the latter two being called one time more than the first.

TODO: deal with simultaneous axes: @DLINK_VIA@ just works

TODO: deal with spontaneous simultaneity, like a single-axis req, put into an array: which ``axis'' is @&req++@ navigating: array-adjacency vs link dereference.  It should sick according to how you got it in the first place: navigating dlist(req, req.pri) vs navigating array(req, 42).  (prob. future work)
\end{comment}


\section{Implementation}

\VRef[Figure]{fig:lst-impl-links} continues the running @req@ example, now showing the \CFA list library's internal representation.
The @dlink@ structure contains exactly two pointers: @next@ and @prev@, which are opaque.
Even though the user-facing list model is linear, the CFA library implements all listing as circular.
This choice helps achieve uniform end treatment and \PAB{TODO finish summarizing benefit}.
A link pointer targets a neighbouring @dlink@ structure, rather than a neighbouring @req@.
(Recall, the running example has the user putting a @dlink@ within a @req@.)

\begin{figure}
        \centering
        \includegraphics[width=\textwidth]{lst-impl-links.pdf}
        \caption{
                \CFA list library representations for the cases under discussion.
        }
        \label{fig:lst-impl-links}
\end{figure}

Circular link-pointers (dashed lines) are tagged internally in the pointer to indicate linear endpoints.
Links among neighbour nodes are not tagged.
Iteration reports ``has more elements'' when accessing untagged links, and ``no more elements'' when accessing tagged links.
Hence, the tags are set on the links that a user cannot navigate.

In a headed list, the list head (@dlist(req)@) acts as an extra element in the implementation-level circularly-linked list.
The content of a @dlist@ header is a (private) @dlink@, with the @next@ pointer to the first element, and the @prev@ pointer to the last element.
Since the head wraps a @dlink@, as does @req@, and since a link-pointer targets a @dlink@, the resulting cycle is among @dlink@ structures, situated inside of header/node.
An untagged pointer points within a @req@, while a tagged pointer points within a list head.
In a headless list, the circular backing list is only among @dlink@s within @req@s.

No distinction is made between an unlisted node (top left and middle) under a headed model and a singleton list under a headless model.
Both are represented as an item referring to itself, with both tags set.


\section{Assessment}
\label{toc:lst:assess}

This section examines the performance of the discussed list implementations.
The goal is to show the \CFA lists are competitive with other designs, but the different list designs may not have equivalent functionality, so it is impossible to select a winner encompassing both functionality and execution performance.


\subsection{Add-Remove Performance}

The fundamental job of a linked-list library is to manage the links that connect nodes.
Any link management is an action that causes pair(s) of elements to become, or cease to be, adjacent in the list.
Thus, adding and removing an element are the sole primitive actions.

Repeated adding and removing is necessary to measure timing because these operations can be as simple as a dozen instructions.
These instruction sequences may have cases that proceed (in a modern, deep pipeline) without a stall.

This experiment takes the position that:
\begin{itemize}
    \item The total time to add and remove is relevant \vs individual times for add and remove.
          Adds without removes quickly fill memory;
                  removes without adds is meaningless.
    \item A relevant breakdown ``by operation'' is:
                \begin{description}
                \item[movement]
          Is the add/remove applied to a stack, queue, or something else?
                \item[polarity]
                  In which direction does the action apply?
                  For a queue, do items flow from first to last or last to first?
                  For a stack, is the first or last end used for adding and removing?
                \item[accessor]
          Is an add/remove location given by a list head's ``first''/``last'', or by a reference to an individual element?
                \end{description}
    \item Speed differences caused by the host machine's memory hierarchy need to be identified and explained,
          but do not represent advantages of one linked-list implementation over another.
\end{itemize}

The experiment used to measure insert/remove cost measures the mean duration of a sequence of additions and removals.
Confidence bounds, on this mean, are discussed.
The distribution of speeds experienced by an individual add-remove pair (tail latency) is not discussed.
Space efficiency is shown only indirectly, by way of caches' impact on speed.
The experiment is sensitive enough to show:
\begin{itemize}
    \item intrusive lists performing (majorly) differently than wrapped lists,
    \item a space of (minor) performance differences among the intrusive lists.
\end{itemize}


\subsubsection{Experiment setup}

The experiment driver defines a (intrusive) node type:
\begin{cfa}
struct Node {
        int i, j, k;  // fields
        // possible intrusive links
};
\end{cfa}
and considers the speed of building and tearing down a list of $n$ instances of it.
% A number of experimental rounds per clock check is precalculated to be appropriate to the value of $n$.
\begin{cfa}
// simplified harness: CFA implementation,
// stack movement, insert-first polarity, head-mediated access
size_t totalOpsDone = 0;
dlist( node_t ) lst;
node_t nodes[ n ];                                              $\C{// preallocated list storage}$
startTimer();
while ( CONTINUE ) {                                    $\C{// \(\approx\) 20 second duration}$
        for ( i; n ) insert_first( lst, nodes[i] );  $\C{// build up}$
        for ( i; n ) remove_first( lst );       $\C{// tear down}$
        totalOpsDone += n;
}
stopTimer();
reportedDuration = getTimerDuration() / totalOpsDone; // throughput per insert/remove operation
\end{cfa}
To reduce administrative overhead, the $n$ nodes for each experiment list are preallocated in an array (on the stack), which removes dynamic allocations for this storage.
These nodes contain an intrusive pointer for intrusive lists, or a pointer to it is stored in a dynamically-allocated internal-node for a wrapper list.
Copying the node for wrapped lists skews the results with administration costs.
The list insertion/removal operations are repeated for a typical 20+ second duration.
After each round, a counter is incremented by $n$ (for throughput).
Time is measured outside the loop because a large $n$ can overrun the time duration before the @CONTINUE@ flag is tested.
Hence, there is minimum of one outer (@CONTINUE@) loop iteration for large lists.
The loop duration is divided by the counter and this throughput is reported.
In a scatter-plot, each dot is one throughput, which means insert + remove + harness overhead.
The harness overhead is constant when comparing linked-list implementations and keep as small as possible.
% The remainder of the setup section discusses the choices that affected the harness overhead.

To test list operations, the experiment performs the inserts/removes in different patterns, \eg insert and remove from front, insert from front and remove from back, random insert and remove, \etc.
Unfortunately, the @std::list@ does \emph{not} support direct insert/remove from a node without an iterator, \ie no @erase( node )@, even though the list is doubly-linked.
To eliminate the iterator, a trick is used for random insertions without replacement.
The @i@ fields in each node are initialized from @0..n-1@.
These @i@ values are then shuffled in the nodes, and the @i@ value is used to represent an indirection to that node for insertion.
hence, the nodes are inserted in random order and removed in the same random order.
$\label{p:Shuffle}$
\begin{c++}
        for ( i; n ) @nodes[i].i = i@;          $\C{// indirection}$
        shuffle( nodes, n );                            $\C{// random shuffle indirects within nodes}$

        while ( CONTINUE ) {
                for ( i; n ) {
                        node_t & temp = nodes[ nodes[i].i ];
                        @temp.j = 0;@                           $\C{// touch random node for wrapped nodes}$
                        insert_first( lst, temp );
                }
                insert_first( lst, nodes[ @nodes[i].i@ ] ); $\C{// build up}$
                for ( i; n ) pass( &remove_first( lst ) );      $\C{// tear down}$
                totalOpsDone += n;
        }
\end{c++}
Note, insertion is traversing the list of nodes linearly, @node[i]@.
For intrusive lists, the inserted (random) node is always touched because its link fields are read/written for insertion into the list.
Hence, the array of nodes is being accessed both linearly and randomly during the traversal.
For wrapped lists, the wrapped nodes are traversed linearly but the random node is not accessed, only a pointer to it is inserted into the linearly accessed wrapped node.
Hence, the traversal is the same as the non-random traversal above.
To level the experiments, an explicit access to the random node is inserted after the insertion, @temp.j = 0@, for the wrapped experiment.
Furthermore, it is rare to insert/remove nodes and not access them.

% \emph{Interleaving} allows for movements other than pure stack and queue.
% Note that the earlier example of using the iterators' array is still a pure stack: the item selected for @erase(...)@ is always the first.
% Including a less predictable movement is important because real applications that justify doubly linked lists use them.
% Freedom to remove from arbitrary places (and to insert under more relaxed assumptions) is the characteristic function of a doubly linked list.
% A queue with drop-out is an example of such a movement.
% A list implementation can show unrepresentative speed under a simple movement, for example, by enjoying unchallenged ``Is first element?'' branch predictions.

% Interleaving brings ``at middle of list'' cases into a stream of add or remove invocations, which would otherwise be exclusively ``at end''.
% A chosen split, like half middle and half end, populates a boolean array, which is then shuffled.
% These booleans then direct the action to end-\vs-middle.
% 
% \begin{cfa}
% // harness (bookkeeping and shuffling elided): CFA implementation,
% // stack movement, insert-first polarity, interleaved element-based remove access
% dlist( item_t ) lst;
% item_t items[ n ];
% @bool interl[ n ];@  // elided: populate with weighted, shuffled [0,1]
% while ( CONTINUE ) {
%       item_t * iters[ n ];
%       for ( i; n ) {
%               insert_first( items[i] );
%               iters[i] = & items[i];
%       }
%       @item_t ** crsr[ 2 ]@ = { // two cursors into iters
%               & iters[ @0@ ], // at stack-insert-first's removal end
%               & iters[ @n / interl_frac@ ]  // in middle
%       };
%       for ( i; n ) {
%               item *** crsr_use = & crsr[ interl[ i ] ]@;
%               remove( *** crsr_use );  // removing from either middle or end
%               *crsr_use += 1;  // that item is done
%       }
%       assert( crsr[0] == & iters[ @n / interl_frac@ ] ); // through second's start
%       assert( crsr[1] == & iters[ @n@ ] );  // did the rest
% }
% \end{cfa}
% 
% By using the pair of cursors, the harness avoids branches, which could incur prediction stall times themselves, or prime a branch in the SUT.
% This harness avoids telling the hardware what the SUT is about to do.

The comparator linked-list implementations are:
\begin{description}
\item[std::list]  The @list@ type of g++.
\item[lq-list]  The @list@ type of LQ from glibc of gcc.
\item[lq-tailq] The @tailq@ type of the same.
\item[upp-upp]  \uCpp provided @uSequence@
\item[cfa-cfa]  \CFA's @dlist@
\end{description}


\subsection{Experimental Environment}
\label{s:ExperimentalEnvironment}

The performance experiments are run on:
\begin{description}[leftmargin=*,topsep=3pt,itemsep=2pt,parsep=0pt]
%\item[PC]
%with a 64-bit eight-core AMD FX-8370E, with ``taskset'' pinning to core \#6.  The machine has 16 GB of RAM and 8 MB of last-level cache.
%\item[ARM]
%Gigabyte E252-P31 128-core socket 3.0 GHz, WO memory model
\item[AMD]
Supermicro AS--1125HS--TNR EPYC 9754 128--core socket, hyper-threading $\times$ 2 sockets (512 processing units) 2.25 GHz, TSO memory model, with cache structure 32KB L1i/L1d, 1024KB L2, 16MB L3, where each L3 cache covers 16 processors.
%\item[Intel]
%Supermicro SYS-121H-TNR Xeon Gold 6530 32--core, hyper-threading $\times$ 2 sockets (128 processing units) 2.1 GHz, TSO memory model
\end{description}
The experiments are single threaded and pinned to single core to prevent any OS movement, which might cause cache or NUMA effects perturbing the experiment.

The compiler is gcc/g++-14.2.0 running on the Linux v6.8.0-52-generic OS.
Switching between the default memory allocators @glibc@ and @llheap@ is done with @LD_PRELOAD@.
To prevent eliding certain code patterns, crucial parts of a test are wrapped by the function @pass@
\begin{cfa}
// prevent eliding, cheaper than volatile
static inline void * pass( void * v ) {  __asm__  __volatile__( "" : "+r"(v) );  return v;  }
...
pass( &remove_first( lst ) );                   // wrap call to prevent elision, insert cannot be elided now
\end{cfa}
The call to @pass@ can prevent a small number of compiler optimizations but this cost is the same for all lists.


\subsubsection{Result: Coarse comparison of styles}

This comparison establishes how an intrusive list performs compared with a wrapped-reference list.
\VRef[Figure]{fig:plot-list-zoomout} presents throughput at various list lengths for a linear and random (shuffled) insert/remove test.
Other kinds of scans were made, but the results are similar in many cases, so it is sufficient to discuss these two scans, representing difference ends of the access spectrum.
In the graphs, all four intrusive lists are plotted with the same symbol;
theses symbols clumped on top of each, showing the performance difference among intrusive lists is small in comparison to the wrapped list \see{\VRef{s:ComparingIntrusiveImplementations} for details among intrusive lists}.

The list lengths start at 10 due to the short insert/remove times of 2--4 ns, for intrusive lists, \vs STL's wrapped-reference list of 15--20 ns.
For very short lists, like 4, the experiment time of 4 $\times$ 2.5 ns and experiment overhead (loops) of 2--4 ns, results in an artificial administrative bump at the start of the graph having nothing to do with the insert/remove times.
As the list size grows, the administrative overhead for intrusive lists quickly disappears.

\begin{figure}
  \centering
  \subfloat[Linear List Nodes]{\label{f:Linear}
    \includegraphics{plot-list-zoomout-noshuf.pdf}
  } % subfigure
  \\
  \subfloat[Shuffled List Nodes]{\label{f:Shuffled}
    \includegraphics{plot-list-zoomout-shuf.pdf}
  } % subfigure
  \caption{Insert/remove duration \vs list length.
  Lengths go as large possible without error.
  One example operation is shown: stack movement, insert-first polarity and head-mediated access.}
  \label{fig:plot-list-zoomout}
\end{figure}

The large difference in performance between intrusive and wrapped-reference lists results from the dynamic allocation for the wrapped nodes.
In effect, this experiment is largely measuring the cost of malloc/free rather than the insert/remove, and is affected by the layout of memory by the allocator.
Fundamentally, insert/remove for a doubly linked-list has a basic cost, which is seen by the similar results for the intrusive lists.
Hence, the costs for a wrapped-reference list are: allocating/deallocating a wrapped node, copying a external-node pointer into the wrapped node for insertion, and linking the wrapped node to/from the list;
the dynamic allocation dominates these costs.
For example, the experiment was run with both glibc and llheap memory allocators, where performance from llheap reduced the cost from 20 to 16 ns, which is still far from the 2--4 ns for intrusive lists.
Unfortunately, there is no way to tease apart the linking and allocation costs for wrapped lists, as there is no way to preallocate the list nodes without writing a mini-allocator to manage the storage.
Hence, dynamic allocation cost is fundamental for wrapped lists.

In detail, \VRef[Figure]{f:Linear} shows linear insertion of all the nodes and then linear removal, both in the same direction.
For intrusive lists, the nodes are adjacent because they are preallocated in an array.
For wrapped lists, the wrapped nodes are still adjacent because the memory allocator happens to use bump allocation for the small fixed-sized wrapped nodes.
As a result, these memory layouts result in high spatial and temporal locality for both kinds of lists during the linear array traversal.
With address look-ahead, the hardware does an excellent job of managing the multi-level cache.
Hence, performance is largely constant for both kinds of lists, until L3 cache and NUMA boundaries are crossed for longer lists and the costs increase consistently for both kinds of lists.

In detail, \VRef[Figure]{f:Shuffled} shows shuffle insertion and removal of the nodes.
As for linear, there are issues with the wrapped list and memory allocation.
For intrusive lists, it is possible to link the nodes randomly, so consecutive nodes in memory seldom point at adjacent nodes.
For wrapped lists, the placement of wrapped nodes is dictated by the memory allocator, and results in memory layout virtually the same as for linear, \ie the wrapped nodes are laid out consecutively in memory, but each wrapped node points at a randomly located external node.
As seen on \VPageref{p:Shuffle}, the random access reads data from external node, which are located in random order.
So while the wrapped nodes are accessed linearly, external nodes are touched randomly for both kinds of lists resulting in similar cache events.
The slowdown of shuffled occurs as the experiment's length grows from last-level cache into main memory.
% Insert and remove operations act on both sides of a link.
%Both a next unlisted item to insert (found in the items' array, seen through the shuffling array), and a next listed item to remove (found by traversing list links), introduce a new user-item location.
Each time a next item is processed, the memory access is a hop to a randomly far address.
The target is unavailable in the cache and a slowdown results.
Note, the external-data no-touch assumption is often unrealistic: decisions like,``Should I remove this item?'' need to look at the item.
Therefore, under realistic assumptions, both intrusive and wrapped-reference lists suffer similar caching issues for very large lists.
% In an odd scenario where this intuition is incorrect, and where furthermore the program's total use of the memory allocator is sufficiently limited to yield approximately adjacent allocations for successive list insertions, a non-intrusive list may be preferred for lists of approximately the cache's size.

The takeaway from this experiment is that wrapped-list operations are expensive because memory allocation is expense at this fine-grained level of execution.
Hence, when possible, using intrusive links can produce a significant performance gain, even if nodes must be dynamically allocated, because the wrapping allocations are eliminated.
Even when space is a consideration, intrusive links may not use any more storage if a node is mostly linked.
Unfortunately, many programmers are unaware of intrusive lists for dynamically-sized data-structures.

% Note, linear access may not be realistic unless dynamic size changes may occur;
% if the nodes are known to be adjacent, use an array.

% In a wrapped-reference list, list nodes are allocated separately from the items put into the list.
% Intrusive beats wrapped at the smaller lengths, and when shuffling is avoided, because intrusive avoids dynamic memory allocation for list nodes.

% STL's performance is not affected by element order in memory.
%The field of intrusive lists begins with length-1 operations costing around 10 ns and enjoys a ``sweet spot'' in lengths 10--100 of 5--7-ns operations.
% This much is also unaffected by element order.
% Beyond this point, shuffled-element list performance worsens drastically, losing to STL beyond about half a million elements, and never particularly leveling off.
% In the same range, an unshuffled list sees some degradation, but holds onto a 1--2 $\times$ speedup over STL.

% The apparent intrusive ``sweet spot,'' particularly its better-than-length-1 speed, is not because of list operations truly running faster.
% Rather, the worsening as length decreases reflects the per-operation share of harness overheads incurred at the outer-loop level.
% Disabling the harness's ability to drive interleaving, even though the current scenario is using a ``never work in middle'' interleave, made this rise disappear.
% Subsequent analyses use length-controlled relative performance when comparing intrusive implementations, making this curiosity disappear.

% The remaining big-swing comparison points say more about a computer's memory hierarchy than about linked lists.
% The tests in this chapter are only inserting and removing.
% They are not operating on any user payload data that is being listed.
% The drastic differences at large list lengths reflect differences in link-field storage density and in correlation of link-field order to element order.
% These differences are inherent to the two list models.

% A wrapped-reference list's separate nodes are allocated right beside each other in this experiment, because no other memory allocation action is happening.
% As a result, the interlinked nodes of the STL list are generally referencing their immediate neighbours.
% This pattern occurs regardless of user-item shuffling because this test's ``use'' of the user-items' array is limited to storing element addresses.  
% This experiment, driving an STL list, is simply not touching the memory that holds the user data.
% Because the interlinked nodes, being the only touched memory, are generally adjacent, this case too has high memory locality and stays fast.

% But the comparison of unshuffled intrusive with wrapped-reference gives the performance of these two styles, with their the common impediment of overfilling the cache removed.
% Intrusive consistently beats wrapped-reference by about 20 ns, at all sizes.  
% This difference is appreciable below list length 0.5 M, and enormous below 10 K.


\section{Result: Comparing intrusive implementations}
\label{s:ComparingIntrusiveImplementations}

The preceding result shows that all the intrusive implementations examined have noteworthy performance compared to wrapped lists.
This analysis picks the list region 10-150 and zooms-in to differentiate among the different intrusive implementations.
The data is selected from the start of \VRef[Figure]{f:Linear}, but the start of \VRef[Figure]{f:Shuffled} would be largely the same.

\begin{figure}
\centering
  \subfloat[Absolute Time]{\label{f:AbsoluteTime}
  \includegraphics{plot-list-zoomin-abs.pdf}
  } % subfigure
  \\
  \subfloat[Relative Time]{\label{f:RelativeTime}
  \includegraphics{plot-list-zoomin-rel.pdf}
  } % subfigure
  \caption{Operation duration \vs list length at small-medium lengths.  One example operation is shown: stack movement, insert-first polarity and head-mediated access.  (a) has absolute times.  (b) has times relative to those of LQ-\lstinline{tailq}.}
  \label{fig:plot-list-zoomin}
\end{figure}

\VRef[Figure]{fig:plot-list-zoomin} shows the zone from 10--150 blown up, both in absolute and relative time.
% The same scenario as the coarse comparison is used: a stack, with insertions and removals happening at the end called ``first,'' ``head'' or ``front,'' and all changes occurring through a head-provided insert/remove operation.
The error bars show fastest and slowest time seen on five trials, and the central point is the mean of the remaining three trials.
For readability, the points are slightly staggered at a given horizontal value, where the points might otherwise appear on top of each other.

While preparing experiment results, I first tested on my old office PC, AMD FX-8370E Eight-Core, before switching to the large new server for final testing.
For this experiment, the results flipped in my favour when running on the server.
New CPU architectures are now amazingly good at branch prediction and micro-parallelism in the pipelines.
Specifically, on the PC, my \CFA and companion \uCpp lists are slower than lq-tail and lq-list by 10\% to 20\%.
On the server, \CFA and \uCpp lists are can be fast by up to 100\%.
Overall, LQ-tailq does the best at short lengths but loses out above a dozen elements.

\VRef[Figure]{f:RelativeTime} shows the percentage difference by treating @tailq@ as the control benchmark, and showing the other list results relative to it.
This change does not affect the who-wins statements, it just removes the ``sweet spot'' bend that the earlier discussion dismissed as incidental.
Runs faster than @tailq@'s are below the zero and slower runs are above;
@tailq@'s mean is always zero by definition, but its error bars, representing a single scenario's re-run stability, are still meaningful.
With this bend straightened out, aggregating across lengths is feasible.

\begin{figure}
\centering
  \subfloat[Supersets]{\label{f:Superset}
  \includegraphics{plot-list-cmp-exout.pdf}
  } % subfigure
  \\
  \subfloat[1st Level Slice]{\label{f:1stLevelSlice}
  \includegraphics{plot-list-cmp-survey.pdf}
  } % subfigure
  \caption{Operation duration ranges across operational scenarios.  (a) has the supersets of the running example operation.  (b) has the first-level slices of the full space of operations.}
  \label{fig:plot-list-cmp-overall}
\end{figure}

\VRef[Figure]{fig:plot-list-cmp-overall} introduces alternative views of the data.
Part (a)'s first column summarizes all the data of \VRef{fig:plot-list-zoomin}-(b).
Its x-axis label, ``stack/insfirst/allhead,'' names the concrete scenario that has been discussed until now.
Moving across the columns, the next three each stretch to include more scenarios on each of the operation dimensions, one at a time.
The second column considers the scenarios $\{\mathrm{stack}\} \times \{\mathrm{insfirst}\} \times \{\mathrm{allhead}, \mathrm{inselem}, \mathrm{remelem}\}$,
while the third stretches polarity and the fourth stretches accessor.
Then next three columns each stretch two scenario dimensions and the last column stretches all three.
The \CFA bar in the last column is summarizing 840 test-program runs: 14 list lengths, 2 movements, 2 polarities, 3 accessors and 5 repetitions.

In the earlier plots of one scenario broken down by length, each data point, with its error bars, represents just 5 repetitions.
With a couple exceptions, this reproducibility error was small.
Now, for a \CFA bar, summarizing 70 (first column) to 840 (last column) runs, a bar's height is dominated by the different behaviours of the scenarios and list length that it summarizes.
Accordingly, the first column's bars are short and last one's are tall.
A box represents the inner 68\% of the durations, while its lines extend to cover 95\%.
The symbol on the bar is the mean duration.

The chosen benchmark of LQ-@tailq@ is not shown in this format because it would be trivial here.
With iter-scenario differences dominating the bar size, and @tailq@'s mean performance defined to be zero in all scenarios, a @tailq@ bar on this plot would only show @tailq@'s re-run stability, which is of no comparison value.

The LQ-@list@ implementation does not support all scenarios, only stack movement with insert-first polarity.
So, its 1, 3, 4 and 7\textsuperscript{th} bars all summarize the same set of points (those with accessor constrained to all-head), as do its 2, 5, 6 and 8\textsuperscript{th} (those with accessor unconstrained).

Rather than exploring from one scenario out, \VRef{fig:plot-list-cmp-overall}-(b) gives a more systematic breakdown of the entire experimental space.
Other than the last grand-total column, each breakdown column shows one value from one operation dimension.

LQ-@list@'s partial scenario coverage gives missing bars where it does not support the operation.
And, again, it gives repetition where all data points occur in several columns' intersection, such as stack/*/* and */insfirst/*.

In the grand total, and in all halves by movement or polarity, \CFA and uC++ are equivalent, while LQ-@list@ beats them slightly.
Splitting on accessor, \CFA has a poor result on element removal, LQ-@list@ has a great result on the other accessors, and uC++ is unaffected.
The unseen @tailq@ dominates across every category and beats \CFA and uC++ by 15--20\%.

% \begin{figure}
% \centering
%   \begin{tabular}{c}
%   \includegraphics{plot-list-cmp-intrl-shift.pdf} \\
%   (a) \\
%   \includegraphics{plot-list-cmp-intrl-outcome.pdf} \\
%   (b) \\
%   \end{tabular}
%   \caption{Caption TODO}
%   \label{fig:plot-list-cmp-intrl}
% \end{figure}


\section{Result: CFA cost attribution}

This comparison loosely itemizes the reasons that the \CFA implementation runs 15--20\% slower than LQ.
Each reason provides for safer programming.
For each reason, a version of the \CFA list was measured that forgoes its safety and regains some performance.
These potential sacrifices are:
\newcommand{\mandhead}{\emph{mand-head}}
\newcommand{\nolisted}{\emph{no-listed}}
\newcommand{\noiter}{\emph{no-iter}}
\begin{description}
\item[mand(atory)-head] Removing support for headless lists.
  A specific explanation of why headless support causes a slowdown is not offered.
  But it is reasonable for a cost to result from making one piece of code handle multiple cases; the subset of the \CFA list API that applies to headless lists shares its implementation with headed lists.
  In the \mandhead case, disabling the feature in \CFA means using an older version of the implementation, from before headless support was added.
  In the pre-headless library, trying to form a headless list (instructing, ``Insert loose element B after loose element A,'') is a checked runtime error.
  LQ does not support headless lists\footnote{
        Though its documentation does not mention the headless use case, this fact is due to one of its insert-before or insert-after routines being unusable in every list model.
        For \lstinline{tailq}, the API requires a head.
        For \lstinline{list}, this usage causes an ``uncaught'' runtime crash.}.
\item[no-listed] Removing support for the @is_listed@ API query.
  Along with it goes error checking such as ``When inserting an element, it must not already be listed, \ie be referred to from somewhere else.''
  These abilities have a cost because, in order to support them, a listed element that is being removed must be written to, to record its change in state.
  In \CFA's representation, this cost is two pointer writes.
  To disable the feature, these writes, and the error checking that consumes their result, are put behind an @#ifdef@.
  The result is that a removed element sees itself as still having neighbours (though these quasi-neighbours see it differently).
  This state is how LQ leaves a removed element; LQ does not offer an is-listed query.
\item[no-iter(ation)] Removing support for well-terminating iteration.
  The \CFA list uses bit-manipulation tagging on link poiters (rather than \eg null links) to express, ``No more elements this way.''
  This tagging has the cost of submitting a retrieved value to the ALU, and awaiting this operation's completion, before dereferencing a link pointer.
  In some cases, the is-terminating bit is transferred from one link to another, or has a similar influence on a resulting link value; this logic adds register pressure and more data dependency.
  To disable the feature, the @#ifdef@-controlled tag manipulation logic compiles in answers like, ``No, that link is not a terminator,'' ``The dereferenceable pointer is the value you read from memory,'' and ``The terminator-marked value you need to write is the pointer you started with.''
  Without this termination marking, repeated requests for a next valid item will always provide a positive response; when it should be negative, the indicated next element is garbage data at an address unlikely to trigger a memory error.
  LQ has a well-terminating iteration for listed elements.
  In the \noiter case, the slowdown is not inherent; it represents a \CFA optimization opportunity.
\end{description}
\MLB{Ensure benefits are discussed earlier and cross-reference}  % an LQ programmer must know not to ask, ``Who's next?'' about an unlisted element; an LQ programmer cannot write assertions about an item being listed; LQ requiring a head parameter is an opportunity for the user to provide inconsistent data

\begin{figure}
\centering
  \begin{tabular}{c}
  \includegraphics{plot-list-cfa-attrib.pdf} \\
  (a) \\
  \includegraphics{plot-list-cfa-attrib-remelem.pdf} \\
  (b) \\
  \end{tabular}
  \caption{Operation duration ranges for functionality-reduced \CFA list implementations.  (a) has the top level slices. (b) has the next level of slicing within the slower element-based removal operation.}
  \label{fig:plot-list-cfa-attrib}
\end{figure}

\VRef[Figure]{fig:plot-list-cfa-attrib} shows the \CFA list performance with these features, and their combinations, turned on and off.  When a series name is one of the three sacrifices above, the series is showing this sacrifice in isolation.  These further series names give combinations:
\newcommand{\attribFull}{\emph{full}}
\newcommand{\attribParity}{\emph{parity}}
\newcommand{\attribStrip}{\emph{strip}}
\begin{description}
        \item[full] No sacrifices.  Same as measurements presented earlier.
        \item[parity] \mandhead + \nolisted.  Feature parity with LQ.
        \item[strip] \mandhead + \nolisted + \noiter.  All options set to ``faster.''
\end{description}
All list implementations are \CFA, possibly stripped.
The plot uses the same LQ-relative basis as earlier.
So getting to zero means matching LQ's @tailq@.

\VRef[Figure]{fig:plot-list-cfa-attrib}-(a) summarizes the time attribution across the main operating scenarios.
The \attribFull series is repeated from \VRef[Figure]{fig:plot-list-cmp-overall}, part (b), while the series showing feature sacrifices are new.
Going all the way to \attribStrip at least nearly matches LQ in all operating scenarios, beats LQ often, and slightly beats LQ overall.
Except within the accessor splits, both sacrifices contribute improvements individually, \noiter helps more than \attribParity, and the total \attribStrip benefit depends on both contributions.
When the accessor is not element removal, the \attribParity shift appears to be counterproductive, leaving \noiter to deliver most of the benefit.
For element removals, \attribParity is the heavy hitter, with \noiter contributing modestly.

The counterproductive shift outside of element removals is likely due to optimization done in the \attribFull version after implementing headless support, \ie not present in the \mandhead version.
This work streamlined both head-based operations (head-based removal being half the work of the element-insertion test).
This improvement could be ported to a \mandhead-style implementation, which would bring down the \attribParity time in these cases.

More significantly, missing this optimization affects every \attribParity result because they all use head-based inserts or removes for at least half their operations.
It is likely a reason that \attribParity is not delivering as well overall as \noiter.
It even represents plausible further improvements in \attribStrip.

\VRef[Figure]{fig:plot-list-cfa-attrib}-(b) addresses element removal being the overall \CFA slow spot and element removal having a peculiar shape in the (a) analysis.
Here, the \attribParity sacrifice bundle is broken out into its two constituents.
The result is the same regardless of the operation.
All three individual sacrifices contribute noteworthy improvements (\nolisted slightly less).
The fullest improvement requires all of them.

The \noiter feature sacrifice is unpalatable.
But because it is not an inherent slowdown, there may be room to pursue a \noiter-level speed improvement without the \noiter feature sacrifice.
The performance crux for \noiter is the pointer-bit tagging scheme.
Alternative designs that may offer speedup with acceptable consequences include keeping the tag information in a separate field, and (for 64-bit architectures) keeping it in the high-order byte \ie using byte- rather than bit-oriented instructions to access it.
The \noiter speed improvement would bring \CFA to +5\% of LQ overall, and from high twenties to high teens, in the worst case of element removal.

Ultimately, this analysis provides options for a future effort that needs to get the most speed out of the \CFA list.




\section{Future Work}
\label{toc:lst:futwork}

The \CFA list examples elide the \lstinline{P9_EMBEDDED} annotations that (TODO: xref P9E future work) proposes to obviate.
Thus, these examples illustrate a to-be state, free of what is to be historic clutter.
The elided portions are immaterial to the discussion and the examples work with the annotations provided.
The \CFA test suite (TODO:cite?) includes equivalent demonstrations, with the annotations included.
\begin{cfa}
struct mary {
        float anotherdatum;
        inline dlink(mary);
};
struct fred {
        float adatum;
        inline struct mine { inline dlink(fred); };
        inline struct yours { inline dlink(fred); };
};
\end{cfa}
like in the thesis examples.  You have to say
\begin{cfa}
struct mary {
        float anotherdatum;
        inline dlink(mary);
};
P9_EMBEDDED(mary, dlink(mary))
struct fred {
        float adatum;
        inline struct mine { inline dlink(fred); };
        inline struct yours { inline dlink(fred); };
};
P9_EMBEDDED(fred, fred.mine)
P9_EMBEDDED(fred, fred.yours)
P9_EMBEDDED(fred.mine, dlink(fred))
P9_EMBEDDED(fred.yours, dlink(fred))
\end{cfa}
like in tests/list/dlist-insert-remove.
Future work should autogen those @P9_EMBEDDED@ declarations whenever it sees a plan-9 declaration.
The exact scheme chosen should harmonize with general user-defined conversions.

Today's P9 scheme is:  mary gets a function `inner returning this as dlink(mary).
Fred gets four of them in a diamond.
They're defined so that `inner is transitive; i.e. fred has two further ambiguous overloads mapping fred to dlink(fred).
The scheme allows the dlist functions to give the assertion, "we work on any T that gives a `inner to dlink(T)."


TODO: deal with: A doubly linked list is being designed.

TODO: deal with: Link fields are system-managed.
Links in GDB.