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@@ -1123,20 +1123,21 @@ 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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@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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-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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+with the glpk library installed, @code{starpu_bound_compute} can be used to
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+solve it immediately and get the optimized minimum.
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The @code{deps} parameter tells StarPU whether to take tasks and implicit data
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dependencies into account. It must be understood that the linear programming
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problem size is quadratic with the number of tasks and thus the time to solve it
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will be very long, it could be minutes for just a few dozen tasks. You should
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probably use @code{lp_solve -timeout 1 test.pl -wmps test.mps} to convert the
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-problem to MPS format and then
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-use a better solver, @code{glpsol} might be better than @code{lp_solve} for
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-instance (the @code{--pcost} option may be useful), but sometimes doesn't manage
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-to converge. Be sure to try all the @code{-B} options of @code{lp_solve}. For
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-instance, we often just use @code{lp_solve -cc -B1 -Bb -Bg -Bp -Bf -Br -BG -Bd
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--Bs -BB -Bo -Bc -Bi} , and the @code{-gr} option can be quite useful.
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+problem to MPS format and then use a better solver, @code{glpsol} might be
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+better than @code{lp_solve} for instance (the @code{--pcost} option may be
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+useful), but sometimes doesn't manage to converge. @code{cbc} might look
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+slower, but it is parallel. Be sure to try at least all the @code{-B} options
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+of @code{lp_solve}. For instance, we often just use
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+@code{lp_solve -cc -B1 -Bb -Bg -Bp -Bf -Br -BG -Bd -Bs -BB -Bo -Bc -Bi} , and
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+the @code{-gr} option can also be quite useful.
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Setting @code{deps} to 0 will only take into account the actual computations
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on processing units. It however still properly takes into account the varying
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