|
|
@@ -1196,7 +1196,7 @@ input sizes, by applying a*n^b+c regression over observed execution times.
|
|
|
@end itemize
|
|
|
|
|
|
How to use schedulers which can benefit from such performance model is explained
|
|
|
-in section @ref{Task scheduling policy}.
|
|
|
+in @ref{Task scheduling policy}.
|
|
|
|
|
|
The same can be done for task power consumption estimation, by setting the
|
|
|
@code{power_model} field the same way as the @code{model} field. Note: for
|
|
|
@@ -1445,7 +1445,7 @@ priority information to StarPU.
|
|
|
By default, StarPU uses the @code{eager} simple greedy scheduler. This is
|
|
|
because it provides correct load balance even if the application codelets do not
|
|
|
have performance models. If your application codelets have performance models
|
|
|
-(see section @ref{Performance model example} for examples showing how to do it),
|
|
|
+(@pxref{Performance model example} for examples showing how to do it),
|
|
|
you should change the scheduler thanks to the @code{STARPU_SCHED} environment
|
|
|
variable. For instance @code{export STARPU_SCHED=dmda} . Use @code{help} to get
|
|
|
the list of available schedulers.
|
