21 lines
1,001 B
TeX
21 lines
1,001 B
TeX
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\section{Related works}
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Throughput prediction tools, however, are not all static.
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\gus~\cite{phd:gruber} dynamically predicts the throughput of a whole program
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region, instrumenting it to retrieve the exact events occurring through its
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execution. This way, \gus{} can more finely detect bottlenecks by
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sensitivity analysis, at the cost of a significantly longer run time.
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\smallskip
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The \bhive{} profiler~\cite{bhive} takes another approach to basic block
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throughput measurement: by mapping memory at any address accessed by a basic
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block, it can effectively run and measure arbitrary code without context, often
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---~but not always, as we discuss later~--- yielding good results.
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\smallskip
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The \anica{} framework~\cite{anica} also attempts to evaluate throughput
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predictors by finding examples on which they are inaccurate. \anica{} starts
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with randomly generated assembly snippets, and refines them through a process
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derived from abstract interpretation to reach general categories of problems.
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