Changeset ed1a637


Ignore:
Timestamp:
Jan 5, 2021, 2:51:58 PM (9 months ago)
Author:
Thierry Delisle <tdelisle@…>
Branches:
arm-eh, jacob/cs343-translation, master, new-ast-unique-expr
Children:
f03209d3
Parents:
c4241b6 (diff), 95eec2c (diff)
Note: this is a merge changeset, the changes displayed below correspond to the merge itself.
Use the (diff) links above to see all the changes relative to each parent.
Message:

Merge branch 'master' of plg.uwaterloo.ca:software/cfa/cfa-cc

Files:
1 added
11 edited

Legend:

Unmodified
Added
Removed
  • doc/theses/thierry_delisle_PhD/thesis/Makefile

    rc4241b6 red1a637  
    2828        base \
    2929        empty \
     30        emptybit \
     31        emptytls \
     32        emptytree \
     33        fairness \
    3034        system \
    3135}
     
    7377        ${LaTeX} $<
    7478
     79build/fairness.svg : fig/fairness.py | ${Build}
     80        python3 $< $@
     81
    7582## Define the default recipes.
    7683
     
    8895        fig2dev -L pstex_t -p ${Build}/$@ $< > ${Build}/$@_t
    8996
     97%.pstex : build/%.svg | ${Build}
     98        inkscape -z -D --file=$< --export-eps=${Build}/$@ --export-latex
     99        mv ${Build}/$@_tex ${Build}/$@_t
     100        echo "sed -i 's/$@/${Build}/$@/g' ${Build}/$@_t"
     101        sed -i 's/$@/${Build}\/$@/g' ${Build}/$@_t
     102
    90103## pstex with inverted colors
    91104%.dark.pstex : fig/%.fig Makefile | ${Build}
  • doc/theses/thierry_delisle_PhD/thesis/glossary.tex

    rc4241b6 red1a637  
    77\newacronym{io}{I/O}{Input and Output}
    88\newacronym{numa}{NUMA}{Non-Uniform Memory Access}
     9\newacronym{prng}{PRNG}{Pseudo Random Number Generator}
    910\newacronym{raii}{RAII}{Resource Acquisition Is Initialization}
    1011\newacronym{tls}{TLS}{Thread Local Storage}
  • doc/theses/thierry_delisle_PhD/thesis/local.bib

    rc4241b6 red1a637  
    609609  note = "[Online; accessed 23-October-2020]"
    610610}
     611
     612@misc{wiki:lcg,
     613  author = "{Wikipedia contributors}",
     614  title = "Linear congruential generator --- {W}ikipedia{,} The Free Encyclopedia",
     615  year = "2020",
     616  url = "https://en.wikipedia.org/wiki/Linear_congruential_generator",
     617  note = "[Online; accessed 2-January-2021]"
     618}
  • doc/theses/thierry_delisle_PhD/thesis/text/core.tex

    rc4241b6 red1a637  
    11\chapter{Scheduling Core}\label{core}
    22
    3 Before discussing scheduling in general, where it is important to address systems that are changing states, this document discusses scheduling in a somewhat ideal scenerio, where the system has reached a steady state. For this purpose, a steady state is loosely defined as a state where there are always \glspl{thrd} ready to run and but the system has the ressources necessary to accomplish the work. In short, the system is neither overloaded or underloaded.
     3Before discussing scheduling in general, where it is important to address systems that are changing states, this document discusses scheduling in a somewhat ideal scenerio, where the system has reached a steady state. For this purpose, a steady state is loosely defined as a state where there are always \glspl{thrd} ready to run and the system has the ressources necessary to accomplish the work, \eg, enough workers. In short, the system is neither overloaded nor underloaded.
    44
    5 I believe it is important to discuss the steady state first because it is the easiest case to handle and, relatedly, the case in which the best performance is to be expected. As such, when the system is either overloaded or underloaded, a common approach is to try to adapt the system to the new load and return to the steady state. Flaws in the scheduling in the steady state tend therefore to be pervasive in all states.
     5I believe it is important to discuss the steady state first because it is the easiest case to handle and, relatedly, the case in which the best performance is to be expected. As such, when the system is either overloaded or underloaded, a common approach is to try to adapt the system to the new load and return to the steady state, \eg, adding or removing workers. Flaws in the scheduling when the system is in the steady state can therefore to be pervasive in all states.
    66
    77\section{Design Goals}
    8 As with most of the design decisions behind \CFA, the main goal is to match the expectation of the programmer, according to their probable mental model. To match these expectations, the design must offer the programmers sufficient guarantees so that, as long as the programmer respects the mental model, the system will also respect this model.
     8As with most of the design decisions behind \CFA, an important goal is to match the expectation of the programmer, according to their probable mental model. To match these expectations, the design must offer the programmers sufficient guarantees so that, as long as they respect the mental model, the system will also respect this model.
    99
    1010For threading, a simple and common mental model is the ``Ideal multi-tasking CPU'' :
     
    1616Applied to threads, this model states that every ready \gls{thrd} immediately runs in parallel with all other ready \glspl{thrd}. While a strict implementation of this model is not feasible, programmers still have expectations about scheduling that come from this model.
    1717
    18 In general, the expectation at the center of this model is that ready \glspl{thrd} do not interfere with eachother but simply share the hardware. This makes it easier to reason about threading because ready \glspl{thrd} can be taken in isolation and the effect of the scheduler can be virtually ignored. This expectation of \gls{thrd} independence means the scheduler is expected to offer 2 guarantees:
     18In general, the expectation at the center of this model is that ready \glspl{thrd} do not interfere with eachother but simply share the hardware. This makes it easier to reason about threading because ready \glspl{thrd} can be taken in isolation and the effect of the scheduler can be virtually ignored. This expectation of \gls{thrd} independence means the scheduler is expected to offer two guarantees:
    1919\begin{enumerate}
    2020        \item A fairness guarantee: a \gls{thrd} that is ready to run will not be prevented to do so by another thread.
     
    2424It is important to note that these guarantees are expected only up to a point. \Glspl{thrd} that are ready to run should not be prevented to do so, but they still need to share a limited amount of hardware. Therefore, the guarantee is considered respected if a \gls{thrd} gets access to a \emph{fair share} of the hardware, even if that share is very small.
    2525
    26 Similarly the performance guarantee, the lack of interferance between threads is only relevant op to a point. Ideally the cost of running and blocking would be constant regardless of contention, but the guarantee is considered satisfied if the cost is not \emph{too high} with or without contention. How much is an acceptable cost is obviously highly variable. For this document the performance experimentation will attempt to show that the cost of scheduling is not a major factor in application performance. This demonstration can be made by comparing application built in \CFA to applications built with other languages or other models. If the performance of an application built in \CFA is not meaningfully different than one built with a different runtime, then the scheduler has a negigeable impact on performance, \ie its impact can be ignored. Recall from a few paragraphs ago that the expectation of programmers is that the impact of the scheduler can be ignored. Therefore, if the cost of scheduling is not a significant portion of the runtime of several different application, I will consider the guarantee achieved.
     26Similarly the performance guarantee, the lack of interferance between threads, is only relevant up to a point. Ideally the cost of running and blocking would be constant regardless of contention, but the guarantee is considered satisfied if the cost is not \emph{too high} with or without contention. How much is an acceptable cost is obviously highly variable. For this document the performance experimentation will attempt to show that the cost of scheduling is at worst equivalent to existing algorithms used in popular languages. This demonstration can be made by comparing application built in \CFA to applications built with other languages or other models. Recall from a few paragraphs ago that the expectation of programmers is that the impact of the scheduler can be ignored. Therefore, if the cost of scheduling is equivalent or lower to other popular languages, I will consider the guarantee achieved.
    2727
    28 \todo{This paragraph should be moved later}
    29 % The next step is then to decide what is considered a \emph{fair share}, \ie what metric is used to measure fairness. Since \CFA is intended to allow numerous short lived threads, I decided to avoid total CPU time as the measure of fairness. Total CPU time inherently favors new \glspl{thrd} over older ones which isn't necessarily a good thing. Instead, fairness is measured in terms of opportunities to run. This metric is more appropriate for a mix of short and long lived \glspl{thrd}.
     28More precisely the scheduler should be:
     29\begin{itemize}
     30        \item As fast as other schedulers that are less fair.
     31        \item Faster than other scheduler that have equal or better fairness.
     32\end{itemize}
     33
     34\subsection{Fairness vs Scheduler Locality}
     35An important performance factor in modern architectures is cache locality. Waiting for data not present in the cache can have a major impact on performance, and having multiple \glspl{hthrd} writing to the same cache lines can lead to cache lines that need to be waited on again. It is therefore preferable to divide the data among each \gls{hthrd}\footnote{This can be an explicit division up front or using data structures where different \glspl{hthrd} are naturally routed to different cache lines.}.
     36
     37For a scheduler, having good locality\footnote{This section discusses \emph{internal} locality, \ie, the locality of the data used by the scheduler. \emph{External locality}, \ie, how the data used by the application is affected by scheduling, is a much more complicated subject and will be discussed in the chapters on evaluation.}, \ie, having the data be local to each \gls{hthrd}, generally conflicts with fairness. Indeed, good locality often requires avoiding the movement of cache lines, while fairness requires dynamically moving \gls{thrd}, and as a consequence cache lines, to \glspl{hthrd} that are currently more appropriate.
     38
     39However, I claim that in practice it is possible to strike a balance between fairness and performance because the need for these do not necessarily overlap temporaly. Figure~\ref{fig:fair} shows an visual representation of this behaviour. As mentionned, a little bit of unfairness can be acceptable, therefore it can be desirable to have an algorithm that prioritizes cache locality as long as no threads is left behind for too long.
     40
     41\begin{figure}
     42        \begin{center}
     43                \input{fairness.pstex_t}
     44        \end{center}
     45        \caption{Fairness vs Locality}
     46        \label{fig:fair}
     47        Rule of thumb graph: Importance of Fairness and Locality while a ready \gls{thrd} waits run.
     48        As the time a ready \gls{thrd} waits increases, ``Ready Time'', the chances that its data is still in cache decreases. At the same time, the need for fairness increases since other \glspl{thrd} may have the chance to run many times, breaking the fairness model mentionned above. Since the actual values and curves of this graph can be highly variable, the graph is left intentionally fuzzy and innacurate.
     49\end{figure}
    3050
    3151\section{Design}
    32 While avoiding the pitfalls of Feedback Scheduling is fairly easy, scheduling does not innately require feedback, avoiding prioritization of \glspl{thrd} is more difficult because of implicitly priorities, see Subsection~\ref{priority}. A strictly \glsxtrshort{fifo} rea
     52A naive strictly \glsxtrshort{fifo} ready-queue does not offer sufficient performance. As shown in the evaluation sections, most production schedulers scale when adding multiple \glspl{hthrd} and that is not possible with a single point of contention. Therefore it is vital to shard the ready-queue so that multiple \glspl{hthrd} can access the ready-queue without performance degradation.
    3353
    34 \subsection{Sharding}
     54\subsection{Sharding} \label{sec:sharding}
     55An interesting approach to sharding a queue is presented in \cit{Trevors paper}. This algorithm represents a queue with relaxed \glsxtrshort{fifo} guarantee using an array of strictly \glsxtrshort{fifo} sublists as shown in Figure~\ref{fig:base}. Each cell of the array contains a linked-list with a lock and each node in these list is marked with a timestamp indicating when they were added to the list. Push operations are done by picking a random cell and attempting to push to its list. If the cell is already locked, the operation is simply retried on a new cell until a lock is acquired. Pop operations are done in a similar fashion except two random cells are picked. If both cells are not already locked and both cells contain non-empty lists, the operation pops the node with the oldest timestamp. If only one of the cell is unlocked and non-empty, the operation pops from that cell. If both cells are either locked or empty, the operation picks two new cells and tries again.
    3556
    3657\begin{figure}
     
    4566
    4667\subsection{Finding threads}
    47 Once threads have been distributed onto multiple queues, indentifying which queues are empty and which aren't can become a problem.
    48 Indeed, if the number of \glspl{thrd} does not far exceed the number of queues, it is probable that several of these queues are empty.
    49 Figure~\ref{fig:empty} shows an example with 2 \glspl{thrd} running on 8 queues, where the chances of getting an empty queue is 75\% per pick, meaning two random picks yield a \gls{thrd} only half the time.
     68Once threads have been distributed onto multiple queues, indentifying which queues are empty and which are not can become a problem. Indeed, if the number of \glspl{thrd} does not far exceed the number of queues, it is probable that several of these queues are empty. Figure~\ref{fig:empty} shows an example with 2 \glspl{thrd} running on 8 queues, where the chances of getting an empty queue is 75\% per pick, meaning two random picks yield a \gls{thrd} only half the time.
    5069
    5170
     
    6180This can lead to performance problems since picks that do not yield a \gls{thrd} are not useful and do not necessarily help make more informed guesses.
    6281
    63 Solutions to this problem can take many forms, but they ultimately all have to encode where the threads are in some form. My results show that the density and locality of this encoding is generally the dominating factor in these scheme.
     82Solutions to this problem can take many forms, but they ultimately all have to encode where the threads are in some form. My results show that the density and locality of this encoding is generally the dominating factor in these scheme. Classic solutions to this problem use one of three techniques to encode the information:
    6483
    65 \paragraph{Dense Information}
     84\begin{figure}
     85        \begin{center}
     86                {\resizebox{0.73\textwidth}{!}{\input{emptybit.pstex_t}}}
     87        \end{center}
     88        \vspace*{-5pt}
     89        \caption{Underloaded queue with added bitmask to indicate which array cells have items.}
     90        \label{fig:emptybit}
     91        \begin{center}
     92                {\resizebox{0.73\textwidth}{!}{\input{emptytree.pstex_t}}}
     93        \end{center}
     94        \vspace*{-5pt}
     95        \caption{Underloaded queue with added binary search tree indicate which array cells have items.}
     96        \label{fig:emptytree}
     97        \begin{center}
     98                {\resizebox{0.9\textwidth}{!}{\input{emptytls.pstex_t}}}
     99        \end{center}
     100        \vspace*{-5pt}
     101        \caption{Underloaded queue with added per processor bitmask to indicate which array cells have items.}
     102        \label{fig:emptytls}
     103\end{figure}
     104
     105\paragraph{Dense Information} Figure~\ref{fig:emptybit} shows a dense bitmask to identify which inner queues are currently in use. This approach means processors can often find \glspl{thrd} in constant time, regardless of how many underlying queues are empty. Furthermore, modern x86 CPUs have extended bit manipulation instructions (BMI2) that allow using the bitmask with very little overhead compared to the randomized selection approach for a filled ready queue, offering good performance even in cases with many empty inner queues. However, this technique has its limits: with a single word\footnote{Word refers here to however many bits can be written atomically.} bitmask, the total number of underlying queues in the ready queue is limited to the number of bits in the word. With a multi-word bitmask, this maximum limit can be increased arbitrarily, but the look-up will nolonger be constant time. Finally, a dense bitmap, either single or multi-word, causes additional contention problems which reduces performance because of cache misses after updates. This central update bottleneck also means the information in the bitmask is more often stale before a processor can use it to find an item, \ie mask read says there are available \glspl{thrd} but none on queue.
     106
     107\paragraph{Sparse Information} Figure~\ref{fig:emptytree} shows an approach using a hierarchical tree data-structure to reduce contention and has been shown to work in similar cases~\cite{ellen2007snzi}. However, this approach may lead to poorer performance due to the inherent pointer chasing cost while still allowing more contention on the nodes of the tree if the tree is not deep enough.
     108
     109\paragraph{Local Information} Figure~\ref{fig:emptytls} shows an approach using dense information, similar to the bitmap, but have each thread keep its own independent copy of it. While this approach can offer good scalability \emph{and} low latency, the liveliness and discovery of the information can become a problem. This case is made worst in systems with few processors where even blind random picks can find \glspl{thrd} in few tries.
     110
     111I built a prototype of these approach and none of these techniques offer satisfying performance when few threads are present. All of these approach hit the same 2 problems. First, blindly picking two sub-queues is very fast which means that any improvement to the hit rate can easily be countered by a slow-down in look-up speed. Second, the array is already as sharded as is needed to avoid contention bottlenecks, so any denser data structure will tend to become a bottleneck. In all cases, these factors meant that the best cases scenerio, many threads, would get worst throughput and the worst case scenario, few threads, would get a better hit rate, but an equivalent throughput. As a result I tried an entirely different approach.
     112
     113\subsection{Dynamic Entropy}\cit{https://xkcd.com/2318/}
     114In the worst case scenario there are few \glspl{thrd} ready to run, or more accuratly given $P$ \glspl{proc}, $T$ \glspl{thrd} and $\epsilon$, as usual, a very small number, in this case $\epsilon \ll P$, we have $T = P + \epsilon$. An important thing to note is that in this case, fairness is effectively irrelevant. Indeed, this case is close to \emph{actually matching} the model of the ``Ideal multi-tasking CPU'' presented in this chapter\footnote{For simplicity, this assumes there is a one-to-one match between \glspl{proc} and \glspl{hthrd}.}. Therefore, in this context it is possible to use a purely internal locality based approach and still meet the fairness requirements. This approach would simply have each \gls{proc} running a single \gls{thrd} repeatedly. Or from the shared ready-queue viewpoint, each \gls{proc} would push to a given sub-queue and then pop from the \emph{same} subqueue. Ideally, the scheduler would achieve this without affecting the fairness guarantees in cases where $T \gg P$.
     115
     116To achieve this, I use a communication channel I have not mentionned yet and which I believe I use in a novel way : the \glsxtrshort{prng}. If the scheduler has a \glsxtrshort{prng} instance per \gls{proc} exclusively used for scheduling, its seed effectively encodes a list of all the accessed subqueues, from the latest to the oldest. The only requirement to achieve this is to be able to ``replay'' the \glsxtrshort{prng} backwards. As it turns out, this is an entirely reasonnable requirement and there already exist \glsxtrshort{prng}s that are fast, compact \emph{and} can be run forward and backwards. Linear congruential generators\cite{wiki:lcg} are an example of \glsxtrshort{prng}s that match these requirements.
     117
     118The algorithm works as follows :
     119\begin{itemize}
     120        \item Each \gls{proc} has two \glsxtrshort{prng} instances, $F$ and $B$.
     121        \item Push and Pop operations happen as mentionned in Section~\ref{sec:sharding} with the following exceptions:
     122        \begin{itemize}
     123                \item Push operations use $F$ going forward on each try and on success $F$ is copied into $B$.
     124                \item Pop operations use $B$ going backwards on each try.
     125        \end{itemize}
     126\end{itemize}
     127
     128The main benefit of this technique is that it basically repects the desired properties of Figure~\ref{fig:fair}. When looking for work, \glspl{proc} will look first at the last cells they pushed to, if any, and then move backwards through the cells. As the \glspl{proc} continue looking for work, $F$ moves back and $B$ stays in place. As a result the relation between the two becomes weaker, which means that the probablisitic fairness of the algorithm reverts to normal. Chapter~\ref{proofs} discusses more formally the fairness guarantees of this algorithm.
     129
     130\section{Details}
  • doc/theses/thierry_delisle_PhD/thesis/text/existing.tex

    rc4241b6 red1a637  
    4242\paragraph{Task Placement} Since modern computers rely heavily on cache hierarchies\cit{Do I need a citation for this}, migrating tasks from one core to another can be .  \cite{DBLP:journals/tpds/SquillanteL93}
    4343
    44 \TODO{The survey is not great on this subject}
     44\todo{The survey is not great on this subject}
    4545
    4646\paragraph{Complex Machine Architecture} Another aspect that has been looked at is how well Work Stealing is applicable to different machine architectures.
  • libcfa/src/memory.cfa

    rc4241b6 red1a637  
    6666forall(dtype T | sized(T), ttype Args | { void ?{}(T&, Args); })
    6767void ?{}(counter_ptr(T) & this, Args args) {
    68         this.data = new(args);
     68        this.data = (counter_data(T)*)new(args);
    6969}
    7070
     
    126126forall(dtype T | sized(T), ttype Args | { void ?{}(T &, Args); })
    127127void ?{}(unique_ptr(T) & this, Args args) {
    128         this.data = new(args);
     128        this.data = (T *)new(args);
    129129}
    130130
  • libcfa/src/stdlib.hfa

    rc4241b6 red1a637  
    267267static inline forall( dtype T | sized(T), ttype TT | { void ?{}( T &, TT ); } )
    268268T * new( TT p ) {
    269         return &(*malloc()){ p };                                                       // run constructor
     269        return &(*(T *)malloc()){ p };                                                  // run constructor
    270270} // new
    271271
  • src/ResolvExpr/RenameVars.cc

    rc4241b6 red1a637  
    186186}
    187187
    188 const ast::Type * renameTyVars( const ast::Type * t, RenameMode mode ) {
     188const ast::Type * renameTyVars( const ast::Type * t, RenameMode mode, bool reset ) {
    189189        // ast::Type *tc = ast::deepCopy(t);
    190190        ast::Pass<RenameVars_new> renamer;
    191191        renamer.core.mode = mode;
    192         if (mode == GEN_USAGE) {
     192        if (mode == GEN_USAGE && reset) {
    193193                renaming.nextUsage();
    194194        }
     
    198198void resetTyVarRenaming() {
    199199        renaming.reset();
     200        renaming.nextUsage();
    200201}
    201202
  • src/ResolvExpr/RenameVars.h

    rc4241b6 red1a637  
    3535                GEN_EXPR_ID // for type in decl
    3636        };
    37         const ast::Type * renameTyVars( const ast::Type *, RenameMode mode = GEN_USAGE );
     37        const ast::Type * renameTyVars( const ast::Type *, RenameMode mode = GEN_USAGE, bool reset = true );
     38       
    3839
    3940        /// resets internal state of renamer to avoid overflow
  • src/ResolvExpr/Resolver.cc

    rc4241b6 red1a637  
    11511151                        const ast::Expr * untyped, const ast::SymbolTable & symtab
    11521152                ) {
    1153                         resetTyVarRenaming();
    11541153                        ast::TypeEnvironment env;
    11551154                        ast::ptr< ast::Expr > newExpr = resolveInVoidContext( untyped, symtab, env );
  • src/ResolvExpr/SatisfyAssertions.cpp

    rc4241b6 red1a637  
    202202                        ast::ptr< ast::Type > toType = assn.first->result;
    203203                        ast::ptr< ast::Type > adjType =
    204                                 renameTyVars( adjustExprType( candidate->get_type(), newEnv, sat.symtab ) );
     204                                renameTyVars( adjustExprType( candidate->get_type(), newEnv, sat.symtab ), GEN_USAGE, false );
    205205
    206206                        // only keep candidates which unify
     
    385385
    386386        /// Limit to depth of recursion of assertion satisfaction
    387         static const int recursionLimit = 4;
     387        static const int recursionLimit = 8;
    388388        /// Maximum number of simultaneously-deferred assertions to attempt concurrent satisfaction of
    389389        static const int deferLimit = 10;
     
    417417                        if ( it != thresholds.end() && it->second < sat.costs ) goto nextSat;
    418418
    419                         // make initial pass at matching assertions
    420                         for ( auto & assn : sat.need ) {
    421                                 // fail early if any assertion is not satisfiable
    422                                 if ( ! satisfyAssertion( assn, sat ) ) {
     419                        // should a limit be imposed? worst case here is O(n^2) but very unlikely to happen.
     420                        for (unsigned resetCount = 0; ; ++resetCount) {
     421                                ast::AssertionList next;
     422                                resetTyVarRenaming();
     423                                // make initial pass at matching assertions
     424                                for ( auto & assn : sat.need ) {
     425                                        // fail early if any assertion is not satisfiable
     426                                        if ( ! satisfyAssertion( assn, sat ) ) {
     427                                                next.emplace_back(assn);
     428                                                // goto nextSat;
     429                                        }
     430                                }
     431                                // success
     432                                if (next.empty()) break;
     433                                // fail if nothing resolves
     434                                else if (next.size() == sat.need.size()) {
    423435                                        Indenter tabs{ 3 };
    424436                                        std::ostringstream ss;
     
    426438                                        print( ss, *sat.cand, ++tabs );
    427439                                        ss << (tabs-1) << "Could not satisfy assertion:\n";
    428                                         ast::print( ss, assn.first, tabs );
     440                                        ast::print( ss, next[0].first, tabs );
    429441
    430442                                        errors.emplace_back( ss.str() );
    431443                                        goto nextSat;
    432444                                }
     445                                sat.need = std::move(next);
    433446                        }
    434447
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