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A previous study by Lameed and Hendren~\cite{lameed2013feval} shows that the overhead of an \feval\ call is significantly higher than a direct call, especially in JIT-based execution environments such as McVM~\cite{chevalier2010mcvm} and the proprietary MATLAB JIT accelerator by Mathworks. In fact, the presence of an \feval\ instruction can disrupt the results of intra- and inter-procedural level for type and array shape inference analyses, which are key factors for efficient code generation.  Lameed and Hendren thus propose and implement in McVM two dynamic techniques for optimizing \feval\ instructions. \ifdefined\fullver  The first one is based on OSR: using the McOSR library~\cite{lameed2013modular}, \feval\ calls inside loops are instrumented with an OSR point and profiling code to cache the last-known types for the arguments of each \feval\ instruction. When an OSR is fired at run-time, a code generator modifies the original function by inserting a guard to choose between a fast path containing a direct call and a slow path with the original \feval\ call. The second technique is less general and uses value-based JIT compilation: when the first argument of an \feval\ call is an argument of the enclosing function, the compiler replaces each call to this function in all of its callers with a call to a special dispatcher. At run-time, the dispatcher evaluates the value of the argument to use for the \feval\ and executes either a previously compiled cached code or generates and JIT-compiles a version of the function optimized for the current value. Although the OSR-based approach is more general, it generates much less efficient code compared to the JIT-based one for three reasons:  \begin{enumerate} 
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  Lameed and Hendren conclude their paper by stating, ``It would be interesting to look at future work that combine the  strengths of both approaches". In the remaining part of this section, we extend McVM by implementing a novel optimization mechanism for \feval\ based on our OSR technique: we will show that our mechanism is as efficient as their JIT-based approach in terms of quality of generated code, and is even more general than their OSR-based approach, as it can optimize also \feval\ calls not enclosed in a loop.  \fi  \subsection{Extending McVM}  The McVM virtual machine is a complex research project developed at McGill~\cite{mcvm} and made of several software components, including: a front-end for lowering MATLAB programs to an intermediate representation called IIR that captures the high-level features of the language; an interpreter for running MATLAB functions and scripts in IIR format; a manager component to perform analyses on IIR; a JIT compiler based on LLVM for generating native code for a function, lowering McVM IIR to LLVM IR; a set of helper components to perform fast vector and matrix operations using optimized libraries such as ATLAS, BLAS and LAPACK. %The architecture of McVM is illustrated in Figure [...]