# Changeset 622a358

Ignore:
Timestamp:
May 18, 2022, 3:59:14 PM (3 months ago)
Branches:
master
Children:
288927f
Parents:
fa2a3b1
Message:

A whole lot of results and some text section done

Location:
doc/theses/thierry_delisle_PhD/thesis
Files:
7 edited

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

 rfa2a3b1 emptytree \ fairness \ idle \ idle1 \ idle2 \ idle_state \ io_uring \ pivot_ring \ cycle \ result.cycle.jax.ops \ result.yield.jax.ops \ result.churn.jax.ops \ result.cycle.jax.ns \ result.yield.jax.ns \ result.churn.jax.ns \ result.cycle.low.jax.ops \ result.yield.low.jax.ops \ result.churn.low.jax.ops \ result.cycle.low.jax.ns \ result.yield.low.jax.ns \ result.churn.low.jax.ns \ result.memcd.updt.qps \ result.memcd.updt.lat \ result.memcd.rate.qps \ result.memcd.rate.99th \ } python3 $<$@ build/result.%.ns.svg : data/% | ${Build} ../../../../benchmark/plot.py -f$< -o $@ -y "ns per ops" cycle_jax_ops_FLAGS = --MaxY=120000000 cycle_low_jax_ops_FLAGS = --MaxY=120000000 cycle_jax_ns_FLAGS = --MaxY=2000 cycle_low_jax_ns_FLAGS = --MaxY=2000 build/result.%.ops.svg : data/% |${Build} ../../../../benchmark/plot.py -f $< -o$@ -y "Ops per second" yield_jax_ops_FLAGS = --MaxY=150000000 yield_low_jax_ops_FLAGS = --MaxY=150000000 yield_jax_ns_FLAGS = --MaxY=1500 yield_low_jax_ns_FLAGS = --MaxY=1500 build/result.%.ns.svg : data/% Makefile | ${Build} ../../../../benchmark/plot.py -f$< -o $@ -y "ns per ops/procs"$($(subst .,_,$*)_ns_FLAGS) build/result.%.ops.svg : data/% Makefile | ${Build} ../../../../benchmark/plot.py -f$< -o $@ -y "Ops per second"$($(subst .,_,$*)_ops_FLAGS) build/result.memcd.updt.qps.svg : data/memcd.updt Makefile | ${Build} ../../../../benchmark/plot.py -f$< -o $@ -y "Actual QPS" -x "Update Ratio" build/result.memcd.updt.lat.svg : data/memcd.updt Makefile |${Build} ../../../../benchmark/plot.py -f $< -o$@ -y "Average Read Latency" -x "Update Ratio" build/result.memcd.rate.qps.svg : data/memcd.rate Makefile | ${Build} ../../../../benchmark/plot.py -f$< -o $@ -y "Actual QPS" -x "Target QPS" build/result.memcd.rate.99th.svg : data/memcd.rate Makefile |${Build} ../../../../benchmark/plot.py -f $< -o$@ -y "Tail Read Latency" -x "Target QPS" ## pstex with inverted colors

• ## doc/theses/thierry_delisle_PhD/thesis/local.bib

 rfa2a3b1 note = "[Online; accessed 12-April-2022]" } % RMR notes : % [05/04, 12:36] Trevor Brown %     i don't know where rmr complexity was first introduced, but there are many many many papers that use the term and define it % ​[05/04, 12:37] Trevor Brown %     here's one paper that uses the term a lot and links to many others that use it... might trace it to something useful there https://drops.dagstuhl.de/opus/volltexte/2021/14832/pdf/LIPIcs-DISC-2021-30.pdf % ​[05/04, 12:37] Trevor Brown %     another option might be to cite a textbook % ​[05/04, 12:42] Trevor Brown %     but i checked two textbooks in the area i'm aware of and i don't see a definition of rmr complexity in either % ​[05/04, 12:42] Trevor Brown %     this one has a nice statement about the prevelance of rmr complexity, as well as some rough definition % ​[05/04, 12:42] Trevor Brown %     https://dl.acm.org/doi/pdf/10.1145/3465084.3467938 % Race to idle notes : % [13/04, 16:56] Martin Karsten %       I don't have a citation. Google brings up this one, which might be good: % % https://doi.org/10.1137/1.9781611973099.100
• ## doc/theses/thierry_delisle_PhD/thesis/text/eval_macro.tex

 rfa2a3b1 Networked ZIPF Nginx : 5Gb still good, 4Gb starts to suffer Cforall : 10Gb too high, 4 Gb too low \section{Memcached} In Memory \subsection{Benchmark Environment} These experiments are run on a cluster of homogenous Supermicro SYS-6017R-TDF compute nodes with the following characteristics: The server runs Ubuntu 20.04.3 LTS on top of Linux Kernel 5.11.0-34. Each node has 2 Intel(R) Xeon(R) CPU E5-2620 v2 running at 2.10GHz. These CPUs have 6 cores per CPUs and 2 \glspl{hthrd} per core, for a total of 24 \glspl{hthrd}. The cpus each have 384 KB, 3 MB and 30 MB of L1, L2 and L3 caches respectively. Each node is connected to the network through a Mellanox 10 Gigabit Ethernet port. The network route uses 1 Mellanox SX1012 10/40 Gigabit Ethernet cluster switch. Networked \begin{figure} \centering \input{result.memcd.updt.qps.pstex_t} \caption[Churn Benchmark : Throughput on Intel]{Churn Benchmark : Throughput on Intel\smallskip\newline Description} \label{fig:memcd:updt:qps} \end{figure} \begin{figure} \centering \input{result.memcd.updt.lat.pstex_t} \caption[Churn Benchmark : Throughput on Intel]{Churn Benchmark : Throughput on Intel\smallskip\newline Description} \label{fig:memcd:updt:lat} \end{figure} \begin{figure} \centering \input{result.memcd.rate.qps.pstex_t} \caption[Churn Benchmark : Throughput on Intel]{Churn Benchmark : Throughput on Intel\smallskip\newline Description} \label{fig:memcd:rate:qps} \end{figure} \begin{figure} \centering \input{result.memcd.rate.99th.pstex_t} \caption[Churn Benchmark : Throughput on Intel]{Churn Benchmark : Throughput on Intel\smallskip\newline Description} \label{fig:memcd:rate:tail} \end{figure}
• ## doc/theses/thierry_delisle_PhD/thesis/text/eval_micro.tex

 rfa2a3b1 \section{Benchmark Environment} All of these benchmarks are run on two distinct hardware environment, an AMD and an INTEL machine. For all benchmarks, \texttt{taskset} is used to limit the experiment to 1 NUMA Node with no hyper threading. If more \glspl{hthrd} are needed, then 1 NUMA Node with hyperthreading is used. If still more \glspl{hthrd} are needed then the experiment is limited to as few NUMA Nodes as needed. \paragraph{AMD} The AMD machine is a server with two AMD EPYC 7662 CPUs and 256GB of DDR4 RAM. \section{Cycling latency} \begin{figure} \centering \input{cycle.pstex_t} \caption[Cycle benchmark]{Cycle benchmark\smallskip\newline Each \gls{at} unparks the next \gls{at} in the cycle before parking itself.} \label{fig:cycle} \end{figure} The most basic evaluation of any ready queue is to evaluate the latency needed to push and pop one element from the ready-queue. Since these two operation also describe a \texttt{yield} operation, many systems use this as the most basic benchmark. Note that this problem is only present on SMP machines and is significantly mitigated by the fact that there are multiple rings in the system. \begin{figure} \centering \input{cycle.pstex_t} \caption[Cycle benchmark]{Cycle benchmark\smallskip\newline Each \gls{at} unparks the next \gls{at} in the cycle before parking itself.} \label{fig:cycle} \end{figure} To avoid this benchmark from being dominated by the idle sleep handling, the number of rings is kept at least as high as the number of \glspl{proc} available. Beyond this point, adding more rings serves to mitigate even more the idle sleep handling. The actual benchmark is more complicated to handle termination, but that simply requires using a binary semphore or a channel instead of raw \texttt{park}/\texttt{unpark} and carefully picking the order of the \texttt{P} and \texttt{V} with respect to the loop condition. \begin{lstlisting} Thread.main() { count := 0 for { wait() this.next.wake() count ++ if must_stop() { break } } global.count += count } \end{lstlisting} \begin{figure} \centering \input{result.cycle.jax.ops.pstex_t} \vspace*{-10pt} \label{fig:cycle:ns:jax} \end{figure} Figure~\ref{fig:cycle:code} shows pseudo code for this benchmark. \begin{figure} \begin{lstlisting} Thread.main() { count := 0 for { wait() this.next.wake() count ++ if must_stop() { break } } global.count += count } \end{lstlisting} \caption[Cycle Benchmark : Pseudo Code]{Cycle Benchmark : Pseudo Code} \label{fig:cycle:code} \end{figure} \subsection{Results} \begin{figure} \subfloat[][Throughput, 100 \ats per \proc]{ \resizebox{0.5\linewidth}{!}{ \input{result.cycle.jax.ops.pstex_t} } \label{fig:cycle:jax:ops} } \subfloat[][Throughput, 1 \ats per \proc]{ \resizebox{0.5\linewidth}{!}{ \input{result.cycle.low.jax.ops.pstex_t} } \label{fig:cycle:jax:low:ops} } \subfloat[][Latency, 100 \ats per \proc]{ \resizebox{0.5\linewidth}{!}{ \input{result.cycle.jax.ns.pstex_t} } } \subfloat[][Latency, 1 \ats per \proc]{ \resizebox{0.5\linewidth}{!}{ \input{result.cycle.low.jax.ns.pstex_t} } \label{fig:cycle:jax:low:ns} } \caption[Cycle Benchmark on Intel]{Cycle Benchmark on Intel\smallskip\newline Throughput as a function of \proc count, using 100 cycles per \proc, 5 \ats per cycle.} \label{fig:cycle:jax} \end{figure} Figure~\ref{fig:cycle:jax} shows the throughput as a function of \proc count, with the following constants: Each run uses 100 cycles per \proc, 5 \ats per cycle. \todo{results discussion} \section{Yield} Its only interesting variable is the number of \glspl{at} per \glspl{proc}, where ratios close to 1 means the ready queue(s) could be empty. This sometimes puts more strain on the idle sleep handling, compared to scenarios where there is clearly plenty of work to be done. \todo{code, setup, results} \begin{lstlisting} Thread.main() { count := 0 while !stop { yield() count ++ } global.count += count } \end{lstlisting} Figure~\ref{fig:yield:code} shows pseudo code for this benchmark, the wait/wake-next'' is simply replaced by a yield. \begin{figure} \begin{lstlisting} Thread.main() { count := 0 for { yield() count ++ if must_stop() { break } } global.count += count } \end{lstlisting} \caption[Yield Benchmark : Pseudo Code]{Yield Benchmark : Pseudo Code} \label{fig:yield:code} \end{figure} \subsection{Results} \begin{figure} \subfloat[][Throughput, 100 \ats per \proc]{ \resizebox{0.5\linewidth}{!}{ \input{result.yield.jax.ops.pstex_t} } \label{fig:yield:jax:ops} } \subfloat[][Throughput, 1 \ats per \proc]{ \resizebox{0.5\linewidth}{!}{ \input{result.yield.low.jax.ops.pstex_t} } \label{fig:yield:jax:low:ops} } \subfloat[][Latency, 100 \ats per \proc]{ \resizebox{0.5\linewidth}{!}{ \input{result.yield.jax.ns.pstex_t} } \label{fig:yield:jax:ns} } \subfloat[][Latency, 1 \ats per \proc]{ \resizebox{0.5\linewidth}{!}{ \input{result.yield.low.jax.ns.pstex_t} } \label{fig:yield:jax:low:ns} } \caption[Yield Benchmark on Intel]{Yield Benchmark on Intel\smallskip\newline Throughput as a function of \proc count, using 1 \ats per \proc.} \label{fig:yield:jax} \end{figure} Figure~\ref{fig:yield:ops:jax} shows the throughput as a function of \proc count, with the following constants: Each run uses 100 \ats per \proc. \todo{results discussion} In either case, this benchmark aims to highlight how each scheduler handles these cases, since both cases can lead to performance degradation if they are not handled correctly. To achieve this the benchmark uses a fixed size array of \newterm{chair}s, where a chair is a data structure that holds a single blocked \gls{at}. When a \gls{at} attempts to block on the chair, it must first unblocked the \gls{at} currently blocked on said chair, if any. This creates a flow where \glspl{at} push each other out of the chairs before being pushed out themselves. For this benchmark to work however, the number of \glspl{at} must be equal or greater to the number of chairs plus the number of \glspl{proc}. To achieve this the benchmark uses a fixed size array of semaphores. Each \gls{at} picks a random semaphore, \texttt{V}s it to unblock a \at waiting and then \texttt{P}s on the semaphore. This creates a flow where \glspl{at} push each other out of the semaphores before being pushed out themselves. For this benchmark to work however, the number of \glspl{at} must be equal or greater to the number of semaphores plus the number of \glspl{proc}. Note that the nature of these semaphores mean the counter can go beyond 1, which could lead to calls to \texttt{P} not blocking. \todo{code, setup, results} for { r := random() % len(spots) next := xchg(spots[r], this) if next { next.wake() } wait() spots[r].V() spots[r].P() count ++ if must_stop() { break } } \end{lstlisting} \begin{figure} \subfloat[][Throughput, 100 \ats per \proc]{ \resizebox{0.5\linewidth}{!}{ \input{result.churn.jax.ops.pstex_t} } \label{fig:churn:jax:ops} } \subfloat[][Throughput, 1 \ats per \proc]{ \resizebox{0.5\linewidth}{!}{ \input{result.churn.low.jax.ops.pstex_t} } \label{fig:churn:jax:low:ops} } \subfloat[][Latency, 100 \ats per \proc]{ \resizebox{0.5\linewidth}{!}{ \input{result.churn.jax.ns.pstex_t} } } \subfloat[][Latency, 1 \ats per \proc]{ \resizebox{0.5\linewidth}{!}{ \input{result.churn.low.jax.ns.pstex_t} } \label{fig:churn:jax:low:ns} } \caption[Churn Benchmark on Intel]{\centering Churn Benchmark on Intel\smallskip\newline Throughput and latency of the Churn on the benchmark on the Intel machine. Throughput is the total operation per second across all cores. Latency is the duration of each opeartion.} \label{fig:churn:jax} \end{figure} \section{Locality}
• ## doc/theses/thierry_delisle_PhD/thesis/text/practice.tex

 rfa2a3b1 More precise \CFA supports adding \procs using the RAII object @processor@. These objects can be created at any time and can be destroyed at any time. They are normally create as automatic stack variables, but this is not a requirement. They are normally created as automatic stack variables, but this is not a requirement. The consequence is that the scheduler and \io subsystems must support \procs comming in and out of existence. \section{Manual Resizing} The consequence of dynamically changing the number of \procs is that all internal arrays that are sized based on the number of \procs neede to be \texttt{realloc}ed. This also means that any references into these arrays, pointers or indexes, may need to be fixed when shrinking\footnote{Indexes may still need fixing because there is no guarantee the \proc causing the shrink had the highest index. Therefore indexes need to be reassigned to preserve contiguous indexes.}. There are no performance requirements, within reason, for resizing since this is usually considered as part of setup and teardown. Manual resizing is expected to be a rare operation. Programmers are mostly expected to resize clusters on startup or teardown. Therefore dynamically changing the number of \procs is an appropriate moment to allocate or free resources to match the new state. As such all internal arrays that are sized based on the number of \procs need to be \texttt{realloc}ed. This also means that any references into these arrays, pointers or indexes, may need to be fixed when shrinking\footnote{Indexes may still need fixing when shrinkingbecause some indexes are expected to refer to dense contiguous resources and there is no guarantee the resource being removed has the highest index.}. There are no performance requirements, within reason, for resizing since it is expected to be rare. However, this operation has strict correctness requirements since shrinking and idle sleep can easily lead to deadlocks. It should also avoid as much as possible any effect on performance when the number of \procs remain constant. This later requirement prehibits simple solutions, like simply adding a global lock to these arrays. This later requirement prohibits naive solutions, like simply adding a global lock to the ready-queue arrays. \subsection{Read-Copy-Update} In this pattern, resizing is done by creating a copy of the internal data strucures, updating the copy with the desired changes, and then attempt an Idiana Jones Switch to replace the original witht the copy. This approach potentially has the advantage that it may not need any synchronization to do the switch. The switch definitely implies a race where \procs could still use the previous, original, data structure after the copy was switched in. The important question then becomes whether or not this race can be recovered from. If the changes that arrived late can be transferred from the original to the copy then this solution works. For linked-lists, dequeing is somewhat of a problem. However, there is a race where \procs could still use the previous, original, data structure after the copy was switched in. This race not only requires some added memory reclamation scheme, it also requires that operations made on the stale original version be eventually moved to the copy. For linked-lists, enqueing is only somewhat problematic, \ats enqueued to the original queues need to be transferred to the new, which might not preserve ordering. Dequeing is more challenging. Dequeing from the original will not necessarily update the copy which could lead to multiple \procs dequeing the same \at. Fixing this requires making the array contain pointers to subqueues rather than the subqueues themselves. Fixing this requires more synchronization or more indirection on the queues. Another challenge is that the original must be kept until all \procs have witnessed the change. In addition to users manually changing the number of \procs, it is desireable to support removing'' \procs when there is not enough \ats for all the \procs to be useful. While manual resizing is expected to be rare, the number of \ats is expected to vary much more which means \procs may need to be removed'' for only short periods of time. Furthermore, race conditions that spuriously lead to the impression no \ats are ready are actually common in practice. Therefore \procs should not be actually \emph{removed} but simply put into an idle state where the \gls{kthrd} is blocked until more \ats become ready. Furthermore, race conditions that spuriously lead to the impression that no \ats are ready are actually common in practice. Therefore resources associated with \procs should not be freed but \procs simply put into an idle state where the \gls{kthrd} is blocked until more \ats become ready. This state is referred to as \newterm{Idle-Sleep}. The \CFA scheduler simply follows the Race-to-Idle'\cit{https://doi.org/10.1137/1.9781611973099.100}' approach where a sleeping \proc is woken any time an \at becomes ready and \procs go to idle sleep anytime they run out of work. \section{Sleeping} As usual, the corner-stone of any feature related to the kernel is the choice of system call. In terms of blocking a \gls{kthrd} until some event occurs the linux kernel has many available options: \paragraph{\texttt{pthread\_mutex}/\texttt{pthread\_cond}} The most classic option is to use some combination of \texttt{pthread\_mutex} and \texttt{pthread\_cond}. These serve as straight forward mutual exclusion and synchronization tools and allow a \gls{kthrd} to wait on a \texttt{pthread\_cond} until signalled. While this approach is generally perfectly appropriate for \glspl{kthrd} waiting after eachother, \io operations do not signal \texttt{pthread\_cond}s. For \io results to wake a \proc waiting on a \texttt{pthread\_cond} means that a different \glspl{kthrd} must be woken up first, and then the \proc can be signalled. \subsection{\texttt{io\_uring} and Epoll} An alternative is to flip the problem on its head and block waiting for \io, using \texttt{io\_uring} or even \texttt{epoll}. This creates the inverse situation, where \io operations directly wake sleeping \procs but waking \proc from a running \gls{kthrd} must use an indirect scheme. This generally takes the form of creating a file descriptor, \eg, a dummy file, a pipe or an event fd, and using that file descriptor when \procs need to wake eachother. This leads to additional complexity because there can be a race between these artificial \io operations and genuine \io operations. If not handled correctly, this can lead to the artificial files going out of sync. \subsection{Event FDs} Another interesting approach is to use an event file descriptor\cit{eventfd}. This is a Linux feature that is a file descriptor that behaves like \io, \ie, uses \texttt{read} and \texttt{write}, but also behaves like a semaphore. Indeed, all read and writes must use 64bits large values\footnote{On 64-bit Linux, a 32-bit Linux would use 32 bits values.}. Writes add their values to the buffer, that is arithmetic addition and not buffer append, and reads zero out the buffer and return the buffer values so far\footnote{This is without the \texttt{EFD\_SEMAPHORE} flag. This flags changes the behavior of \texttt{read} but is not needed for this work.}. If a read is made while the buffer is already 0, the read blocks until a non-0 value is added. What makes this feature particularly interesting is that \texttt{io\_uring} supports the \texttt{IORING\_REGISTER\_EVENTFD} command, to register an event fd to a particular instance. Once that instance is registered, any \io completion will result in \texttt{io\_uring} writing to the event FD. This means that a \proc waiting on the event FD can be \emph{directly} woken up by either other \procs or incomming \io. \begin{figure} \centering \input{idle1.pstex_t} \caption[Basic Idle Sleep Data Structure]{Basic Idle Sleep Data Structure \smallskip\newline Each idle \proc is put unto a doubly-linked stack protected by a lock. Each \proc has a private event FD.} \label{fig:idle1} \end{figure} \section{Tracking Sleepers} Tracking which \procs are in idle sleep requires a data structure holding all the sleeping \procs, but more importantly it requires a concurrent \emph{handshake} so that no \at is stranded on a ready-queue with no active \proc. The classic challenge is when a \at is made ready while a \proc is going to sleep, there is a race where the new \at may not see the sleeping \proc and the sleeping \proc may not see the ready \at. Furthermore, the Race-to-Idle'' approach means that there is some \section{Sleeping} \subsection{Event FDs} \subsection{Epoll} \subsection{\texttt{io\_uring}} \section{Reducing Latency} Since \ats can be made ready by timers, \io operations or other events outside a clusre, this race can occur even if the \proc going to sleep is the only \proc awake. As a result, improper handling of this race can lead to all \procs going to sleep and the system deadlocking. Furthermore, the Race-to-Idle'' approach means that there may be contention on the data structure tracking sleepers. Contention slowing down \procs attempting to sleep or wake-up can be tolerated. These \procs are not doing useful work and therefore not contributing to overall performance. However, notifying, checking if a \proc must be woken-up and doing so if needed, can significantly affect overall performance and must be low cost. \subsection{Sleepers List} Each cluster maintains a list of idle \procs, organized as a stack. This ordering hopefully allows \proc at the tail to stay in idle sleep for extended period of times. Because of these unbalanced performance requirements, the algorithm tracking sleepers is designed to have idle \proc handle as much of the work as possible. The idle \procs maintain the of sleepers among themselves and notifying a sleeping \proc takes as little work as possible. This approach means that maintaining the list is fairly straightforward. The list can simply use a single lock per cluster and only \procs that are getting in and out of idle state will contend for that lock. This approach also simplifies notification. Indeed, \procs need to be notify when a new \at is readied, but they also must be notified during resizing, so the \gls{kthrd} can be joined. This means that whichever entity removes idle \procs from the sleeper list must be able to do so in any order. Using a simple lock over this data structure makes the removal much simpler than using a lock-free data structure. The notification process then simply needs to wake-up the desired idle \proc, using \texttt{pthread\_cond\_signal}, \texttt{write} on an fd, etc., and the \proc will handle the rest. \subsection{Reducing Latency} As mentioned in this section, \procs going idle for extremely short periods of time is likely in certain common scenarios. Therefore, the latency of doing a system call to read from and writing to the event fd can actually negatively affect overall performance in a notable way. Is it important to reduce latency and contention of the notification as much as possible. Figure~\ref{fig:idle1} shoes the basic idle sleep data structure. For the notifiers, this data structure can cause contention on the lock and the event fd syscall can cause notable latency. \begin{figure} \centering \input{idle2.pstex_t} \caption[Improved Idle Sleep Data Structure]{Improved Idle Sleep Data Structure \smallskip\newline An atomic pointer is added to the list, pointing to the Event FD of the first \proc on the list.} \label{fig:idle2} \end{figure} The contention is mostly due to the lock on the list needing to be held to get to the head \proc. That lock can be contended by \procs attempting to go to sleep, \procs waking or notification attempts. The contentention from the \procs attempting to go to sleep can be mitigated slightly by using \texttt{try\_acquire} instead, so the \procs simply continue searching for \ats if the lock is held. This trick cannot be used for waking \procs since they are not in a state where they can run \ats. However, it is worth nothing that notification does not strictly require accessing the list or the head \proc. Therefore, contention can be reduced notably by having notifiers avoid the lock entirely and adding a pointer to the event fd of the first idle \proc, as in Figure~\ref{fig:idle2}. To avoid contention between the notifiers, instead of simply reading the atomic pointer, notifiers atomically exchange it to \texttt{null} so only only notifier will contend on the system call. \begin{figure} \centering \input{idle_state.pstex_t} \caption[Improved Idle Sleep Data Structure]{Improved Idle Sleep Data Structure \smallskip\newline An atomic pointer is added to the list, pointing to the Event FD of the first \proc on the list.} \label{fig:idle:state} \end{figure} The next optimization that can be done is to avoid the latency of the event fd when possible. This can be done by adding what is effectively a benaphore\cit{benaphore} in front of the event fd. A simple three state flag is added beside the event fd to avoid unnecessary system calls, as shown in Figure~\ref{fig:idle:state}. The flag starts in state \texttt{SEARCH}, while the \proc is searching for \ats to run. The \proc then confirms the sleep by atomically swaping the state to \texttt{SLEEP}. If the previous state was still \texttt{SEARCH}, then the \proc does read the event fd. Meanwhile, notifiers atomically exchange the state to \texttt{AWAKE} state. if the previous state was \texttt{SLEEP}, then the notifier must write to the event fd. However, if the notify arrives almost immediately after the \proc marks itself idle, then both reads and writes on the event fd can be omitted, which reduces latency notably. This leads to the final data structure shown in Figure~\ref{fig:idle}. \begin{figure} \centering \input{idle.pstex_t} \caption[Low-latency Idle Sleep Data Structure]{Low-latency Idle Sleep Data Structure \smallskip\newline Each idle \proc is put unto a doubly-linked stack protected by a lock. Each \proc has a private event FD with a benaphore in front of it. The list also has an atomic pointer to the event fd and benaphore of the first \proc on the list.} \label{fig:idle} \end{figure}
• ## doc/theses/thierry_delisle_PhD/thesis/thesis.tex

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