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@@ -1114,7 +1114,7 @@ input sizes, by applying a*n^b+c regression over observed execution times.
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@node Theoretical lower bound on execution time
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@node Theoretical lower bound on execution time
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@section Theoretical lower bound on execution time
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@section Theoretical lower bound on execution time
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-For history-based kernels, StarPU can very easily provide a theoretical lower
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+For kernels with history-based performance models, StarPU can very easily provide a theoretical lower
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bound for the execution time of a whole set of tasks. See for
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bound for the execution time of a whole set of tasks. See for
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instance @code{examples/lu/lu_example.c}: before submitting tasks,
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instance @code{examples/lu/lu_example.c}: before submitting tasks,
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call @code{starpu_bound_start}, and after complete execution, call
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call @code{starpu_bound_start}, and after complete execution, call
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@@ -1123,7 +1123,7 @@ call @code{starpu_bound_start}, and after complete execution, call
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problem corresponding to the schedule of your tasks. Run it through
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problem corresponding to the schedule of your tasks. Run it through
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@code{lp_solve} or any other linear programming solver, and that will give you a
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@code{lp_solve} or any other linear programming solver, and that will give you a
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lower bound for the total execution time of your tasks. If StarPU was compiled
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lower bound for the total execution time of your tasks. If StarPU was compiled
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-with the glpk library installed, starpu_bound_compute can be used to solve it
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+with the glpk library installed, @code{starpu_bound_compute} can be used to solve it
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immediately and get the optimized minimum.
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immediately and get the optimized minimum.
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Note that this is not taking into account task dependencies and data
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Note that this is not taking into account task dependencies and data
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