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Once a state mapping object has been constructed, the optimizer calls our OSR library to generate the continuation function for the OSR transition and eventually compiles it. A pointer to the compiled function is stored in the code cache and returned to the stub, which invokes it through an indirect call passing the live state saved at the OSR point.  \subsection{Experimental Results}  We evaluated the effectiveness of our technique on four 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; 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. 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.   \begin{table}  \begin{small}