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\section{Case Study}  \label{se:case-study}  MATLAB is a popular dynamic language for scientific and numerical programming. Introduced in the late 1970s mainly as a scripting language for performing computations through efficient libraries, it has evolved over the years into a more complex programming language with support for high-level features such as functions, packages and object orientation. A popular feature of the language is the \feval\ construct, a built-in higher-order function that enables the invocation of the function specified as first argument with the remaining arguments for the \feval\ call, returning eventually the computed result. This feature is heavily used in many classes of numerical computations, computations.\fullver{,  such as iterative methods for approximate solutions of an ordinary differential equation (ODE) and simulated annealing heuristics to locate a good approximation to the global optimum of a function in a large search space. space.}  A previous study by Lameed and Hendren~\cite{lameed2013feval} shows that the overhead of an \feval\ call is significantly high compared to a direct call, especially in JIT-based execution environments such as McVM 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.  diff --git a/header.tex b/header.tex  index e566c42..b953abc 100644  --- a/header.tex  +++ b/header.tex 
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\usepackage{listings}  % short vs long version  \newcommand{\fullver}{} \newcommand{\fullver}[1]{}  % clever references  \ifdefined\noauthorea  diff --git a/osr-llvm.tex b/osr-llvm.tex  index 5a7b7b7..832adaf 100644  --- a/osr-llvm.tex  +++ b/osr-llvm.tex 
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%\subsection{Example}  In this section we discuss one possible embodiment of the OSR approach of \mysection\ref{se:overview} in LLVM. Our discussion is based on a simple running example that illustrates a profile-driven optimization scenario. We start from a simple base function ({\tt isord}) that checks whether an array of numbers is ordered according to some criterion specified by a comparator (see \myfigure\ref{fi:isord-example}). Our goal is to instrument {\tt isord} so that, whenever the number of loop iterations exceeds a certain threshold, control is dynamically diverted to a faster version generated on the fly by inlining the comparator.   The IR code shown in this section\footnote{Virtual register names and labels in the LLVM-produced IR code shown in this paper have been refactored to make the code more readable.} has been generated with \clang\ and instrumented with \osrkit, a library we prototyped to help VM builders deploy OSR in LLVM. \osrkit\ provides a number of useful abstractions that include open and resolved OSR instrumentation of IR base functions without breaking the SSA form, liveness analysis, generation of OSR continuation functions, and mapping of LLVM values between different versions of a program along with compensation code generation\footnote{The generation\footnote{An  accompanying artifact allows will allow  the interested reader to get acquainted with \osrkit\ and repeat the sample scenario described in this section.}. %To explain how the OSR approach of \mysection\ref{se:overview} can be implemented in LLVM, we consider the simple example of \myfigure\ref{fi:isord-example}. Function {\tt isord} checks whether an array of numbers is ordered according to some criterion specified by a comparator.