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-                      re5e2334
+                      re76fa30
         \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.}
+        \caption[Cycle benchmark]{Cycle benchmark\smallskip\newline Each \at unparks the next \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 @yield@ operation, many systems use this operation as the most basic benchmark.
 However, yielding can be treated as a special case by optimizing it away since the number of ready \glspl{at} does not change.
+However, yielding can be treated as a special case by optimizing it away since the number of ready \ats does not change.
 Not all systems perform this optimization, but those that do have an artificial performance benefit because the yield becomes a \emph{nop}.
 For this reason, I chose a different first benchmark, called \newterm{Cycle Benchmark}.
 This benchmark arranges a number of \glspl{at} into a ring, as seen in Figure~\ref{fig:cycle}, where the ring is a circular singly-linked list.
 At runtime, each \gls{at} unparks the next \gls{at} before parking itself.
 Unparking the next \gls{at} pushes that \gls{at} onto the ready queue as does the ensuing park.
 Hence, the underlying runtime cannot rely on the number of ready \glspl{at} staying constant over the duration of the experiment.
 In fact, the total number of \glspl{at} waiting on the ready queue is expected to vary because of the race between the next \gls{at} unparking and the current \gls{at} parking.
+This benchmark arranges a number of \ats into a ring, as seen in Figure~\ref{fig:cycle}, where the ring is a circular singly-linked list.
+At runtime, each \at unparks the next \at before parking itself.
+Unparking the next \at pushes that \at onto the ready queue while the ensuing park leads to a \at being popped from the ready queue.
+Hence, the underlying runtime cannot rely on the number of ready \ats staying constant over the duration of the experiment.
+In fact, the total number of \ats waiting on the ready queue is expected to vary because of the race between the next \at unparking and the current \at parking.
 That is, the runtime cannot anticipate that the current task will immediately park.
 As well, the size of the cycle is also decided based on this race, \eg a small cycle may see the chain of unparks go full circle before the first \gls{at} parks because of time-slicing or multiple \procs.
+As well, the size of the cycle is also decided based on this race, \eg a small cycle may see the chain of unparks go full circle before the first \at parks because of time-slicing or multiple \procs.
 Every runtime system must handle this race and cannot optimized away the ready-queue pushes and pops.
 To prevent any attempt of silently omitting ready-queue operations, the ring of \glspl{at} is made big enough so the \glspl{at} have time to fully park before being unparked again.
+To prevent any attempt of silently omitting ready-queue operations, the ring of \ats is made big enough so the \ats have time to fully park before being unparked again.
 (Note, an unpark is like a V on a semaphore, so the subsequent park (P) may not block.)
 Finally, to further mitigate any underlying push/pop optimizations, especially on SMP machines, multiple rings are created in the experiment.
-To avoid this benchmark being affected by idle-sleep handling, the number of rings is multiple times greater than the number of \glspl{proc}.
-This design avoids the case where one of the \glspl{proc} runs out of work because of the variation on the number of ready \glspl{at} mentioned above.
 Figure~\ref{fig:cycle:code} shows the pseudo code for this benchmark.
 …
         count := 0
         for {
+                @this.next.wake()@
                 @wait()@
-                @this.next.wake()@
                 count ++
                 if must_stop() { break }
 …
                 \label{fig:cycle:jax:low:ns}
+        }
         \caption[Cycle Benchmark on Intel]{Cycle Benchmark on Intel\smallskip\newline Throughput and Scalability as a function of \proc count 5 \ats per cycle and different cycle count. For Throughput higher is better, for Scalability lower is better.}
+        \caption[Cycle Benchmark on Intel]{Cycle Benchmark on Intel\smallskip\newline Throughput and Scalability as a function of \proc count 5 \ats per cycle and different cycle count. For Throughput higher is better, for Scalability lower is better. Each series represent 15 independent runs, the dotted lines are extremums while the solid line is the medium.}
         \label{fig:cycle:jax}
 \end{figure}
 …
                         \input{result.cycle.nasus.ns.pstex_t}
+                }
+                \label{fig:cycle:nasus:ns}
+        }
         \subfloat[][Scalability, 1 cycle per \proc]{
 …
                 \label{fig:cycle:nasus:low:ns}
+        }
         \caption[Cycle Benchmark on AMD]{Cycle Benchmark on AMD\smallskip\newline Throughput and Scalability as a function of \proc count 5 \ats per cycle and different cycle count. For Throughput higher is better, for Scalability lower is better.}
+        \caption[Cycle Benchmark on AMD]{Cycle Benchmark on AMD\smallskip\newline Throughput and Scalability as a function of \proc count 5 \ats per cycle and different cycle count. For Throughput higher is better, for Scalability lower is better. Each series represent 15 independent runs, the dotted lines are extremums while the solid line is the medium.}
         \label{fig:cycle:nasus}
 \end{figure}
 Figure~\ref{fig:cycle:jax} and Figure~\ref{fig:cycle:nasus} shows the throughput as a function of \proc count on Intel and AMD respectively, where each cycle has 5 \ats.
 The graphs show traditional throughput on the top row and \newterm{scalability} on the bottom row.
 Where scalability uses the same data but the Y axis is calculated as throughput over the number of \procs.
+Where scalability uses the same data but the Y axis is calculated as the number of \procs over the throughput.
 In this representation, perfect scalability should appear as a horizontal line, \eg, if doubling the number of \procs doubles the throughput, then the relation stays the same.
 The left column shows results for 100 cycles per \proc, enough cycles to always keep every \proc busy.
 …
 This effect is again repeated from 73 and 96 \procs, where it happens on the second CPU.
 When running only a single cycle, most runtime achieve lower throughput because of the idle-sleep mechanism.
-In Figure~\ref{fig:cycle:jax:ops} and \ref{fig:cycle:jax:ns}
 Figure~\ref{fig:cycle:nasus} show effectively the same story happening on AMD as it does on Intel.
 …
 \section{Yield}
 For completion, the classic yield benchmark is included.
 This benchmark is simpler than the cycle test: it creates many \glspl{at} that call @yield@.
+This benchmark is simpler than the cycle test: it creates many \ats that call @yield@.
 As mentioned, this benchmark may not be representative because of optimization shortcuts in @yield@.
 The only interesting variable in this benchmark is the number of \glspl{at} per \glspl{proc}, where ratios close to 1 means the ready queue(s) can be empty.
+The only interesting variable in this benchmark is the number of \ats per \procs, where ratios close to 1 means the ready queue(s) can be empty.
 This scenario can put a strain on the idle-sleep handling compared to scenarios where there is plenty of work.
 Figure~\ref{fig:yield:code} shows pseudo code for this benchmark, where the @wait/next.wake@ is replaced by @yield@.
 …
                 \label{fig:yield:jax:low:ns}
+        }
         \caption[Yield Benchmark on Intel]{Yield Benchmark on Intel\smallskip\newline Throughput and Scalability as a function of \proc count, using 1 \ats per \proc. For Throughput higher is better, for Scalability lower is better.}
+        \caption[Yield Benchmark on Intel]{Yield Benchmark on Intel\smallskip\newline Throughput and Scalability as a function of \proc count, using 1 \ats per \proc. For Throughput higher is better, for Scalability lower is better. Each series represent 15 independent runs, the dotted lines are extremums while the solid line is the medium.}
         \label{fig:yield:jax}
 \end{figure}
 …
                         \input{result.yield.nasus.ns.pstex_t}
+                }
+                \label{fig:yield:nasus:ns}
+        }
         \subfloat[][Scalability, 1 \at per \proc]{
 …
                 \label{fig:yield:nasus:low:ns}
+        }
         \caption[Yield Benchmark on AMD]{Yield Benchmark on AMD\smallskip\newline Throughput and Scalability as a function of \proc count, using 1 \ats per \proc. For Throughput higher is better, for Scalability lower is better.}
+        \caption[Yield Benchmark on AMD]{Yield Benchmark on AMD\smallskip\newline Throughput and Scalability as a function of \proc count, using 1 \ats per \proc. For Throughput higher is better, for Scalability lower is better. Each series represent 15 independent runs, the dotted lines are extremums while the solid line is the medium.}
         \label{fig:yield:nasus}
 \end{figure}
+Figure~\ref{fig:yield:jax} shows the throughput as a function of \proc count, where each run uses 100 \ats per \proc.
+Figure~\ref{fig:yield:jax} shows the throughput as a function of \proc count on Intel.
 It is fairly obvious why I claim this benchmark is more artificial.
 The throughput is dominated by the mechanism used to handle the @yield@.
 …
 \section{Churn}
 The Cycle and Yield benchmark represent an \emph{easy} scenario for a scheduler, \eg an embarrassingly parallel application.
 In these benchmarks, \glspl{at} can be easily partitioned over the different \glspl{proc} upfront and none of the \glspl{at} communicate with each other.
 The Churn benchmark represents more chaotic execution, where there is no relation between the last \gls{proc} on which a \gls{at} ran and blocked and the \gls{proc} that subsequently unblocks it.
 With processor-specific ready-queues, when a \gls{at} is unblocked by a different \gls{proc} that means the unblocking \gls{proc} must either ``steal'' the \gls{at} from another processor or find it on a global queue.
 This dequeuing results in either contention on the remote queue and/or \glspl{rmr} on \gls{at} data structure.
 In either case, this benchmark aims to highlight how each scheduler handles these cases, since both cases can lead to performance degradation if not handled correctly.
+In these benchmarks, \ats can be easily partitioned over the different \procs upfront and none of the \ats communicate with each other.
+The Churn benchmark represents more chaotic executions, where there is more communication among \ats but no relation between the last \proc on which a \at ran and blocked and the \proc that subsequently unblocks it.
+With processor-specific ready-queues, when a \at is unblocked by a different \proc that means the unblocking \proc must either ``steal'' the \at from another processor or find it on a global queue.
+This dequeuing results in either contention on the remote queue and/or \glspl{rmr} on \at data structure.
+In either case, this benchmark aims to measure how well each scheduler handles these cases, since both cases can lead to performance degradation if not handled correctly.
 This benchmark uses a fixed-size array of counting semaphores.
 Each \gls{at} picks a random semaphore, @V@s it to unblock any \at waiting, and then @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, the number of \glspl{at} must be equal or greater than the number of semaphores plus the number of \glspl{proc}.
+Each \at picks a random semaphore, @V@s it to unblock any \at waiting, and then @P@s on the semaphore.
+This creates a flow where \ats push each other out of the semaphores before being pushed out themselves.
+For this benchmark to work, the number of \ats must be equal or greater than the number of semaphores plus the number of \procs.
 Note, the nature of these semaphores mean the counter can go beyond 1, which can lead to nonblocking calls to @P@.
 Figure~\ref{fig:churn:code} shows pseudo code for this benchmark, where the @yield@ is replaced by @V@ and @P@.
 …
 \subsection{Results}
-Figure~\ref{fig:churn:jax} shows the throughput as a function of \proc count, where each run uses 100 cycles per \proc and 5 \ats per cycle.
 \begin{figure}
         \subfloat[][Throughput, 100 \ats per \proc]{
 …
                         \input{result.churn.jax.ns.pstex_t}
+                }
+                \label{fig:churn:jax:ns}
+        }
         \subfloat[][Latency, 1 \ats per \proc]{
 …
                 \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 operation.}
+        \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. For Throughput higher is better, for Scalability lower is better. Each series represent 15 independent runs, the dotted lines are extremums while the solid line is the medium.}
         \label{fig:churn:jax}
 \end{figure}
+\todo{results discussion}
+\begin{figure}
+        \subfloat[][Throughput, 100 \ats per \proc]{
+                \resizebox{0.5\linewidth}{!}{
+                        \input{result.churn.nasus.ops.pstex_t}
+                }
+                \label{fig:churn:nasus:ops}
+        }
+        \subfloat[][Throughput, 1 \ats per \proc]{
+                \resizebox{0.5\linewidth}{!}{
+                        \input{result.churn.low.nasus.ops.pstex_t}
+                }
+                \label{fig:churn:nasus:low:ops}
+        }
+        \subfloat[][Latency, 100 \ats per \proc]{
+                \resizebox{0.5\linewidth}{!}{
+                        \input{result.churn.nasus.ns.pstex_t}
+                }
+                \label{fig:churn:nasus:ns}
+        }
+        \subfloat[][Latency, 1 \ats per \proc]{
+                \resizebox{0.5\linewidth}{!}{
+                        \input{result.churn.low.nasus.ns.pstex_t}
+                }
+                \label{fig:churn:nasus:low:ns}
+        }
+        \caption[Churn Benchmark on AMD]{\centering Churn Benchmark on AMD\smallskip\newline Throughput and latency of the Churn on the benchmark on the AMD machine.
+        For Throughput higher is better, for Scalability lower is better. Each series represent 15 independent runs, the dotted lines are extremums while the solid line is the medium.}
+        \label{fig:churn:nasus}
+\end{figure}
+Figure~\ref{fig:churn:jax} shows the throughput as a function of \proc count on Intel.
+Like for the cycle benchmark, here are runtimes achieve fairly similar performance.
+Scalability is notably worst than the previous benchmarks since there is inherently more communication between processors.
+Indeed, once the number of \glspl{hthrd} goes beyond a single socket, performance ceases to improve.
+An interesting aspect to note here is that the runtimes differ in how they handle this situation.
+Indeed, when a \proc unparks a \at that was last run on a different \proc, the \at could be appended to the ready-queue local \proc or to the ready-queue of the remote \proc, which previously ran the \at.
+\CFA, tokio and Go all use the approach of unparking to the local \proc while Libfibre unparks to the remote \proc.
+In this particular benchmark, the inherent chaos of the benchmark in addition to small memory footprint means neither approach wins over the other.
+Figure~\ref{fig:churn:nasus} shows effectively the same picture.
+Performance improves as long as all \procs fit on a single socket.
+Beyond that performance plateaus.
+Again this performance demonstrate \CFA achieves satisfactory performance.
 \section{Locality}
+\todo{code, setup, results}
+\begin{figure}
+\begin{cfa}
+Thread.main() {
+        count := 0
+        for {
+                r := random() % len(spots)
+                // go through the array
+                @work( a )@
+                spots[r].V()
+                spots[r].P()
+                count ++
+                if must_stop() { break }
+        }
+        global.count += count
+}
+\end{cfa}
+\begin{cfa}
+Thread.main() {
+        count := 0
+        for {
+                r := random() % len(spots)
+                // go through the array
+                @work( a )@
+                // pass array to next thread
+                spots[r].V( @a@ )
+                @a = @spots[r].P()
+                count ++
+                if must_stop() { break }
+        }
+        global.count += count
+}
+\end{cfa}
+\caption[Locality Benchmark : Pseudo Code]{Locality Benchmark : Pseudo Code}
+\label{fig:locality:code}
+\end{figure}
+As mentionned in the churn benchmark, when unparking a \at, it is possible to either unpark to the local or remote ready-queue.
+\footnote{It is also possible to unpark to a third unrelated ready-queue, but unless the scheduler has additional knowledge about the situation, it is unlikely to result in good cache locality.}
+The locality experiment includes two variations of the churn benchmark, where an array of data is added.
+In both variations, before @V@ing the semaphore, each \at increment random cells inside the array.
+The @share@ variation then passes the array to the shadow-queue of the semaphore, effectively transferring ownership of the array to the woken thread.
+In the @noshare@ variation the array is not passed on and each thread continously accesses its private array.
+The objective here is to highlight the different decision made by the runtime when unparking.
+Since each thread unparks a random semaphore, it means that it is unlikely that a \at will be unparked from the last \proc it ran on.
+In the @share@ version, this means that unparking the \at on the local \proc is appropriate since the data was last modified on that \proc.
+In the @noshare@ version, the reverse is true.
+The expectation for this benchmark is to see a performance inversion, where runtimes will fare notably better in the variation which matches their unparking policy.
+This should lead to \CFA, Go and Tokio achieving better performance in @share@ while libfibre achieves better performance in @noshare@.
+\subsection{Results}
+\begin{figure}
+        \subfloat[][Throughput share]{
+                \resizebox{0.5\linewidth}{!}{
+                        \input{result.locality.share.jax.ops.pstex_t}
+                }
+                \label{fig:locality:jax:share:ops}
+        }
+        \subfloat[][Throughput noshare]{
+                \resizebox{0.5\linewidth}{!}{
+                        \input{result.locality.noshare.jax.ops.pstex_t}
+                }
+                \label{fig:locality:jax:noshare:ops}
+        }
+        \subfloat[][Scalability share]{
+                \resizebox{0.5\linewidth}{!}{
+                        \input{result.locality.share.jax.ns.pstex_t}
+                }
+                \label{fig:locality:jax:share:ns}
+        }
+        \subfloat[][Scalability noshare]{
+                \resizebox{0.5\linewidth}{!}{
+                        \input{result.locality.noshare.jax.ns.pstex_t}
+                }
+                \label{fig:locality:jax:noshare:ns}
+        }
+        \caption[Locality Benchmark on Intel]{Locality Benchmark on Intel\smallskip\newline Throughput and Scalability as a function of \proc count. For Throughput higher is better, for Scalability lower is better. Each series represent 15 independent runs, the dotted lines are extremums while the solid line is the medium.}
+        \label{fig:locality:jax}
+\end{figure}
+\begin{figure}
+        \subfloat[][Throughput share]{
+                \resizebox{0.5\linewidth}{!}{
+                        \input{result.locality.share.nasus.ops.pstex_t}
+                }
+                \label{fig:locality:nasus:share:ops}
+        }
+        \subfloat[][Throughput noshare]{
+                \resizebox{0.5\linewidth}{!}{
+                        \input{result.locality.noshare.nasus.ops.pstex_t}
+                }
+                \label{fig:locality:nasus:noshare:ops}
+        }
+        \subfloat[][Scalability share]{
+                \resizebox{0.5\linewidth}{!}{
+                        \input{result.locality.share.nasus.ns.pstex_t}
+                }
+                \label{fig:locality:nasus:share:ns}
+        }
+        \subfloat[][Scalability noshare]{
+                \resizebox{0.5\linewidth}{!}{
+                        \input{result.locality.noshare.nasus.ns.pstex_t}
+                }
+                \label{fig:locality:nasus:noshare:ns}
+        }
+        \caption[Locality Benchmark on AMD]{Locality Benchmark on AMD\smallskip\newline Throughput and Scalability as a function of \proc count. For Throughput higher is better, for Scalability lower is better. Each series represent 15 independent runs, the dotted lines are extremums while the solid line is the medium.}
+        \label{fig:locality:nasus}
+\end{figure}
+Figure~\ref{fig:locality:jax} shows that the results somewhat follow the expectation.
+On the left of the figure showing the results for the shared variation, where \CFA and tokio outperform libfibre as expected.
+And correspondingly on the right, we see the expected performance inversion where libfibre now outperforms \CFA and tokio.
+Otherwise the results are similar to the churn benchmark, with lower throughtput due to the array processing.
+It is unclear why Go's performance is notably worst than the other runtimes.
+Figure~\ref{fig:locality:nasus} shows the same experiment on AMD.
+\todo{why is cfa slower?}
+Again, we see the same story, where tokio and libfibre swap places and Go trails behind.
 \section{Transfer}
 The last benchmark is more of an experiment than a benchmark.
 It tests the behaviour of the schedulers for a misbehaved workload.
 In this workload, one of the \gls{at} is selected at random to be the leader.
 The leader then spins in a tight loop until it has observed that all other \glspl{at} have acknowledged its leadership.
 The leader \gls{at} then picks a new \gls{at} to be the ``spinner'' and the cycle repeats.
 The benchmark comes in two flavours for the non-leader \glspl{at}:
+In this workload, one of the \at is selected at random to be the leader.
+The leader then spins in a tight loop until it has observed that all other \ats have acknowledged its leadership.
+The leader \at then picks a new \at to be the next leader and the cycle repeats.
+The benchmark comes in two flavours for the non-leader \ats:
 once they acknowledged the leader, they either block on a semaphore or spin yielding.
 The experiment is designed to evaluate the short-term load-balancing of a scheduler.
 Indeed, schedulers where the runnable \glspl{at} are partitioned on the \glspl{proc} may need to balance the \glspl{at} for this experiment to terminate.
 This problem occurs because the spinning \gls{at} is effectively preventing the \gls{proc} from running any other \glspl{thrd}.
 In the semaphore flavour, the number of runnable \glspl{at} eventually dwindles down to only the leader.
 This scenario is a simpler case to handle for schedulers since \glspl{proc} eventually run out of work.
 In the yielding flavour, the number of runnable \glspl{at} stays constant.
+Indeed, schedulers where the runnable \ats are partitioned on the \procs may need to balance the \ats for this experiment to terminate.
+This problem occurs because the spinning \at is effectively preventing the \proc from running any other \at.
+In the semaphore flavour, the number of runnable \ats eventually dwindles down to only the leader.
+This scenario is a simpler case to handle for schedulers since \procs eventually run out of work.
+In the yielding flavour, the number of runnable \ats stays constant.
 This scenario is a harder case to handle because corrective measures must be taken even when work is available.
 Note, runtime systems with preemption circumvent this problem by forcing the spinner to yield.
+\todo{code, setup, results}
+I both flavours, the experiment effectively measures how long it takes for all \ats to run once after a given synchronization point.
+In an ideal scenario where the scheduler is strictly FIFO, every thread would run once after the synchronization and therefore the delay between leaders would be given by:
+$ \frac{CSL + SL}{NP - 1}$, where $CSL$ is the context switch latency, $SL$ is the cost for enqueuing and dequeuing a \at and $NP$ is the number of \procs.
+However, if the scheduler allows \ats to run many times before other \ats are able to run once, this delay will increase.
+The semaphore version is an approximation of the strictly FIFO scheduling, where none of the \ats \emph{attempt} to run more than once.
+The benchmark effectively provides the fairness guarantee in this case.
+In the yielding version however, the benchmark provides no such guarantee, which means the scheduler has full responsability and any unfairness will be measurable.
+While this is a fairly artificial scenario, it requires only a few simple pieces.
+The yielding version of this simply creates a scenario where a \at runs uninterrupted in a saturated system, and starvation has a easily measured impact.
+However, \emph{any} \at that runs uninterrupted for a significant period of time in a saturated system could lead to this kind of starvation.
 \begin{figure}
 …
                 return
+        }
         // Wait for everyone to acknowledge my leadership
         start: = timeNow()
 …
+                }
+        }
         // pick next leader
         leader := threads[ prng() % len(threads) ]
         // wake every one
         if ! exhaust {
 …
+        }
+}
 Thread.wait() {
         this.idx_seen := lead_idx
 …
         else { yield() }
+}
 Thread.main() {
         while !done  {
 …
 \subsection{Results}
+Figure~\ref{fig:transfer:jax} shows the throughput as a function of \proc count, where each run uses 100 cycles per \proc and 5 \ats per cycle.
+\todo{results discussion}
+\begin{figure}
+\begin{centering}
+\begin{tabular}{r | c c c c | c c c c }
+Machine   &                     \multicolumn{4}{c |}{Intel}                &          \multicolumn{4}{c}{AMD}                    \\
+Variation & \multicolumn{2}{c}{Park} & \multicolumn{2}{c |}{Yield} & \multicolumn{2}{c}{Park} & \multicolumn{2}{c}{Yield} \\
+\procs    &      2      &      192   &      2      &      192      &      2      &      256   &      2      &      256    \\
+\hline
+\CFA      & 106 $\mu$s  & j200       & 68.4 $\mu$s & ~1.2 ms       & 174 $\mu$s  & ~28.4 ms   & 78.8~~$\mu$s& ~~1.21 ms   \\
+libfibre  & 127 $\mu$s  &            & DNC         & DNC           & 156 $\mu$s  & ~36.7 ms   & DNC         & DNC         \\
+Go        & 106 $\mu$s  & j200       & 24.6 ms     & 74.3 ms       & 271 $\mu$s  & 121.6 ms   & ~~1.21~ms   & 117.4 ms    \\
+tokio     & 289 $\mu$s  &            & DNC         & DNC           & 157 $\mu$s  & 111.0 ms   & DNC         & DNC
+\end{tabular}
+\end{centering}
+\caption[Transfer Benchmark on Intel and AMD]{Transfer Benchmark on Intel and AMD\smallskip\newline Average measurement of how long it takes for all \ats to acknowledge the leader \at. DNC stands for ``did not complete'', meaning that after 5 seconds of a new leader being decided, some \ats still had not acknowledged the new leader. }
+\label{fig:transfer:res}
+\end{figure}
+Figure~\ref{fig:transfer:res} shows the result for the transfer benchmark with 2 \procs and all \procs, where each experiement runs 100 \at per \proc.
+Note that the results here are only meaningful as a coarse measurement of fairness, beyond which small cost differences in the runtime and concurrent primitives begin to matter.
+As such, data points that are the on the same order of magnitude as eachother should be basically considered equal.
+The takeaway of this experiement is the presence of very large differences.
+The semaphore variation is denoted ``Park'', where the number of \ats dwindles down as the new leader is acknowledged.
+The yielding variation is denoted ``Yield''.
+The experiement was only run for the extremums of the number of cores since the scaling per core behaves like previous experiements.
+This experiments clearly demonstrate that while the other runtimes achieve similar performance, \CFA achieves significantly better fairness.
+The semaphore variation serves as a control group, where all runtimes are expected to transfer leadership fairly quickly.
+Since \ats block after acknowledging the leader, this experiment effectively measures how quickly \procs can steal \ats from the \proc running leader.
+Figure~\ref{fig:transfer:res} shows that while Go and Tokio are slower, all runtime achieve decent latency.
+However, the yielding variation shows an entirely different picture.
+Since libfibre and tokio have a traditional work-stealing scheduler, \procs that have \ats on their local queues will never steal from other \procs.
+The result is that the experiement simply does not complete for these runtime.
+Without \procs stealing from the \proc running the leader, the experiment will simply never terminate.
+Go manages to complete the experiement because it adds preemption on top of classic work-stealing.
+However, since preemption is fairly costly it achieves significantly worst performance.
+In contrast, \CFA achieves equivalent performance in both variations, demonstrating very good fairness.
+Interestingly \CFA achieves better delays in the yielding version than the semaphore version, however, that is likely due to fairness being equivalent but removing the cost of the semaphores.

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