Depsim: add chapter conclusion

manuscrit/60_staticdeps/99_conclusion.tex

@@ -0,0 +1,29 @@
+\section*{Conclusion}
+
+In this chapter, we studied data dependencies within assembly kernels; and more
+specifically, data dependencies occurring through memory accesses, which we
+call \emph{memory-carried dependencies}. \cesasme{}'s analysis showed in
+\autoref{chap:CesASMe} that this kind of dependency was responsible for a
+significant portion of state-of-the-art analyzers' prediction errors.
+
+We introduce \staticdeps{}, a heuristic approach based on random values as
+representatives of abstract values. This approach is able to find data
+dependencies, including memory-carried ones, loop-carried or not, leveraging
+semantics of the assembly code provided by \valgrind{}'s \vex. It is, however,
+still unable to find aliasing addresses whose source of aliasing is outside of
+the studied block's scope ---~and, as such, suffers from the \emph{lack of
+context} pointed out in the previous chapter.
+
+\medskip{}
+
+Our evaluation of \staticdeps{} against a dynamic analysis baseline,
+\depsim{}, shows that it only finds about 60\,\% of the existing dependencies.
+We however enrich \uica{} with \staticdeps{}, and find that it performs on the
+full \cesasme{}'s dataset as well as \uica{} alone on the pruned dataset of
+\cesasme{}, removing memory-carried bottlenecks. From this, we conclude that
+\staticdeps{} is very successful at finding the data dependencies through
+memory that actually matter from a performance analysis perspective. We also
+find that, despite being written in pure Python, \staticdeps{} is at least
+30$\times$ faster than its C dynamic counterpart, \depsim; as such, we expect
+a compiled and optimized implementation of \staticdeps{} to be two to three
+orders of magnitude faster than \depsim{}.

manuscrit/60_staticdeps/main.tex

@@ -7,3 +7,4 @@ dependencies}\label{chap:staticdeps}
 \input{30_static_principle.tex}
 \input{40_staticdeps.tex}
 \input{50_eval.tex}
+\input{99_conclusion.tex}