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\section{Experimental Evaluation}  \label{se:experiments}  In this section we present a preliminar experimental study of \osrkit \osrkit\  aimed at addressing the following questions: \ifdefined \noauthorea  \begin{description}  \else 
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\end{description}  \subsection{Benchmarks and Setup}  We address questions Q1-3 by analyzing the performance of \osrkit \osrkit\  on a selection of the \shootout\ benchmarks~\cite{shootout} running in a proof-of-concept virtual machine. In particular, we focus on single-threaded benchmarks that do not rely on external libraries to perform their core computations. Benchmarks and their description are reported in \mytable\ref{tab:shootout}; four of them ({\tt b-trees}, {\tt mbrot}, {\tt n-body} and {\tt sp-norm}) are evaluated against two workloads of different size. %In this section we present a preliminar experimental study of our OSR implementation in TinyVM. Our experiments are based on the \shootout\ test suite, also known as the Computer Language Benchmark Game~\cite{shootout}. In particular, we focus on single-threaded benchmarks that do not rely on external libraries to perform their core computations. 
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%\item {\bf Message 3}: what is the overhead of the library for inserting OSR points? We compute for each benchmark the time required by insertOpenOSR (OSR point insertion + stub creation) and insertFinalizedOSR (OSR point insertion + generation of continuation function).  %\end{itemize}  \paragraph{[Q1] \paragraph{Q1:  Impact on Code Quality.} In order to measure how much a never-firing OSR point might impact code quality, we analyzed the source-code structure of each benchmark and profiled its run-time behavior to identify performance-critical sections for OSR point insertion. The distinction between open and resolved OSR points is nearly irrelevant in this context: we choose to focus on open OSR points, passing {\tt null} as the {\tt val} argument for the stub.  % figure 
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%We analyzed the code produced by the x86-64 back-end: the OSR machinery is lowered into three native instructions that load a counter in a register, compare it against a constant value and jump to the OSR block accordingly.  The number of times the OSR condition is checked for each benchmark is the same as in the experiments reported in \mytable\ref{tab:sameFun}.  \paragraph{[Q2] \paragraph{Q2:  Overhead of OSR Transitions.} \mytable\ref{tab:sameFun} reports estimates of the average cost of performing an OSR transition to a clone of the running function. For each benchmark we compute the time difference between the scenarios in which an always- and a never-firing resolved OSR point is inserted in the code, respectively; we then normalize this difference against the number of fired OSR transitions. 
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Normalized differences reported in the table represent a reasonable estimate of the average cost of firing a single OSR transition, which in other words is the cost of performing a function call passing the live variables as arguments. Reported numbers are in the order of nanoseconds, and might be negative due to instruction cache effects.  \paragraph{[Q3] \paragraph{Q3:  OSR Machinery Generation.} We now discuss the overhead of the \osrkit\ library for inserting OSR machinery in the IR of a function. \mytable\ref{tab:instrTime} reports for each benchmark the number of IR instructions in the instrumented function, the number of live values to transfer and the time spent in the IR manipulation. Locations for OSR points are chosen as in the experiments about code quality, and the target function is a clone of the source function.  For open OSR points, we report the time spent in inserting the OSR point in the function and in generating the stub; both operations do not depend on the size of the function. For resolved OSR points, we report the time spent in inserting the OSR point and in generating the \fosrto\ function. 
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Experimental results presented in this section suggest that inserting an OSR point is a quick operation and is unlikely to degrade the quality of generated code. The time required to fire an OSR transition is negligible (i.e., order of nanoseconds), while the cost of generating a continuation function - either when inserting a resolved OSR point, or from the callback method invoked at an open OSR transition - is likely to be dominated by the cost of its compilation. For a front-end, the choice whether to insert an OSR point into a function for dynamic optimization merely depends on the trade-off between the expected benefits in terms of execution time and the overheads from generating and JIT-compiling an optimized version of the function; compared to these two operations, the cost of OSR-related operations is negligible.  \paragraph{[Q4] \paragraph{Q4  Optimizing {\tt feval} in MATLAB.} %\label{sse:feval-results}  %We conclude this section with a discussion on the effectiveness of our optimization technique for McVM. In particular, we analyze its impact on the running time of a few numeric benchmarks, namely {\tt odeEuler}, {\tt odeMidpt}, {\tt odeRK4}, and {\tt sim\_anl}. The first three benchmarks solve an ODE for heat treating simulation using the Euler, midpoint, and Range-Kutta method, respectively\footnote{\url{http://web.cecs.pdx.edu/~gerry/nmm/mfiles/}}; the last benchmark minimizes the six-hump camelback function with the method of simulated annealing\footnote{\url{http://www.mathworks.com/matlabcentral/fileexchange/33109-simulated-annealing-optimization}}.  We report the speed-ups enabled by our technique in \mytable\ref{tab:feval}, using the running times for McVM's \feval\ default dispatcher as baseline. As the dispatcher typically JIT-compiles the invoked function, we also analyzed running times when the dispatcher calls a previously compiled function. In the last column, we show speed-ups from a modified version of the benchmarks in which each \feval\ call is replaced by hand with a direct call to the function in use for the specific benchmark.