When absolute mpicc/mpicxx paths are given, avoid AC_PATH_PROG looking in PATH only

configure.ac

@@ -323,7 +323,11 @@ if test x$enable_simgrid = xyes ; then
 else
     DEFAULT_MPICC=mpicc
 fi
-AC_ARG_WITH(mpicc, [AS_HELP_STRING([--with-mpicc=<path to mpicc>], [Path of the mpicc compiler])], [DEFAULT_MPICC=$withval])
+AC_ARG_WITH(mpicc, [AS_HELP_STRING([--with-mpicc=<mpicc name or path to mpicc>], [Name or path of the mpicc compiler])], [DEFAULT_MPICC=$withval])
+case $DEFAULT_MPICC in
+	# Absolute path, avoid AC_PATH_PROG looking in PATH only...
+	/*) mpicc_path="$DEFAULT_MPICC" ;;
+esac
 AC_PATH_PROG(mpicc_path, $DEFAULT_MPICC, [no], [$simgrid_dir/bin:$PATH])
 AC_MSG_CHECKING(whether mpicc is available)
 AC_MSG_RESULT($mpicc_path)
@@ -335,7 +339,11 @@ if test x$enable_simgrid = xyes ; then
 else
     DEFAULT_MPICXX=mpicxx
 fi
-AC_ARG_WITH(mpicxx, [AS_HELP_STRING([--with-mpicxx=<path to mpicxx>], [Path of the mpicxx/mpic++ compiler])], [DEFAULT_MPICXX=$withval])
+AC_ARG_WITH(mpicxx, [AS_HELP_STRING([--with-mpicxx=<mpicxx name or path to mpicxx>], [Name or path of the mpicxx/mpic++ compiler])], [DEFAULT_MPICXX=$withval])
+case $DEFAULT_MPICXX in
+	# Absolute path, avoid AC_PATH_PROG looking in PATH only...
+	/*) mpicxx_path="$DEFAULT_MPICXX" ;;
+esac
 AC_PATH_PROG(mpicxx_path, $DEFAULT_MPICXX, [no], [$simgrid_dir/bin:$PATH])
 
 # try with mpic++ if mpicxx was not found

doc/doxygen/chapters/320_scheduling.doxy

@@ -39,7 +39,7 @@ STARPU_SCHED. For instance <c>export STARPU_SCHED=dmda</c> . Use <c>help</c> to
 get the list of available schedulers.
 
 
-<b>Non Performance Modelling Policies:</b>
+\subsection NonPerformanceModelingPolicies Non Performance Modelling Policies
 
 The <b>eager</b> scheduler uses a central task queue, from which all workers draw tasks
 to work on concurrently. This however does not permit to prefetch data since the scheduling
@@ -65,7 +65,7 @@ The <b>heteroprio</b> scheduler uses different priorities for the different proc
 This scheduler must be configured to work correclty and to expect high-performance
 as described in the corresponding section.
 
-\section DMTaskSchedulingPolicy Performance Model-Based Task Scheduling Policies
+\subsection DMTaskSchedulingPolicy Performance Model-Based Task Scheduling Policies
 
 If (<b>and only if</b>) your application <b>codelets have performance models</b> (\ref
 PerformanceModelExample), you should change the scheduler thanks to the
@@ -87,8 +87,6 @@ family policy using performance model hints. A low or zero percentage may be
 the sign that performance models are not converging or that codelets do not
 have performance models enabled.
 
-<b>Performance Modelling Policies:</b>
-
 The <b>dm</b> (deque model) scheduler takes task execution performance models into account to
 perform a HEFT-similar scheduling strategy: it schedules tasks where their
 termination time will be minimal. The difference with HEFT is that <b>dm</b>
@@ -127,6 +125,47 @@ The <b>peager</b> (parallel eager) scheduler is similar to eager, it also
 supports parallel tasks (still experimental). Should not be used when several 
 contexts using it are being executed simultaneously.
 
+\subsection ExistingModularizedSchedulers Modularized Schedulers
+
+StarPU provides a powerful way to implement schedulers, as documented in \ref
+DefiningANewModularSchedulingPolicy . It is currently shipped with the following
+pre-defined Modularized Schedulers :
+
+
+<b>modular-eager</b> , <b>modular-eager-prefetching</b> are eager-based Schedulers (without and with prefetching)), they are \n
+naive schedulers, which try to map a task on the first available resource
+they find. The prefetching variant queues several tasks in advance to be able to
+do data prefetching. This may however degrade load balancing a bit.
+
+<b>modular-prio</b>, <b>modular-prio-prefetching</b>, <b>modular-eager-prio</b> are prio-based Schedulers (without / with prefetching):,
+similar to Eager-Based Schedulers. Can handle tasks which have a defined
+priority and schedule them accordingly.
+The <b>modular-eager-prio</b> variant integrates the eager and priority queue in a
+single component. This allows it to do a better job at pushing tasks.
+
+<b>modular-random</b>, <b>modular-random-prio</b>, <b>modular-random-prefetching</b>, <b>modular-random-prio-prefetching</b> are random-based Schedulers (without/with prefetching) : \n
+Select randomly a resource to be mapped on for each task.
+
+<b>modular-ws</b>) implements Work Stealing:
+Maps tasks to workers in round robin, but allows workers to steal work from other workers.
+
+<b>modular-heft</b>, <b>modular-heft2</b>, and <b>modular-heft-prio</b> are
+HEFT Schedulers : \n
+Maps tasks to workers using a heuristic very close to
+Heterogeneous Earliest Finish Time.
+It needs that every task submitted to StarPU have a
+defined performance model (\ref PerformanceModelCalibration)
+to work efficiently, but can handle tasks without a performance
+model. <b>modular-heft</b> just takes tasks by priority order. <b>modular-heft2</b> takes
+at most 5 tasks of the same priority and checks which one fits best.
+<b>modular-heft-prio</b> is similar to <b>modular-heft</b>, but only decides the memory
+node, not the exact worker, just pushing tasks to one central queue per memory
+node.
+
+<b>modular-heteroprio</b> is a Heteroprio Scheduler: \n
+Maps tasks to worker similarly to HEFT, but first attribute accelerated tasks to
+GPUs, then not-so-accelerated tasks to CPUs.
+
 \section TaskDistributionVsDataTransfer Task Distribution Vs Data Transfer
 
 Distributing tasks to balance the load induces data transfer penalty. StarPU
@@ -196,51 +235,6 @@ use starpu_task_expected_length() on the task (in µs), multiplied by the
 typical power consumption of the device, e.g. in W, and divided by 1000000. to
 get Joules.
 
-\section ExistingModularizedSchedulers Modularized Schedulers
-
-StarPU provides a powerful way to implement schedulers, as documented in \ref
-DefiningANewModularSchedulingPolicy . It is currently shipped with the following
-pre-defined Modularized Schedulers :
-
-- Eager-based Schedulers (with/without prefetching : <b>modular-eager</b> ,
-<b>modular-eager-prefetching</b>) : \n
-Naive scheduler, which tries to map a task on the first available resource
-it finds. The prefecthing variant queues several tasks in advance to be able to
-do data prefetching. This may however degrade load balancing a bit.
-
-- Prio-based Schedulers (with/without prefetching :
-<b>modular-prio</b>, <b>modular-prio-prefetching</b>, <b>modular-eager-prio</b>) : \n
-Similar to Eager-Based Schedulers. Can handle tasks which have a defined
-priority and schedule them accordingly.
-The <b>modular-eager-prio</b> variant integrates the eager and priority queue in a
-single component. This allows it to do a better job at pushing tasks.
-
-- Random-based Schedulers (with/without prefetching: <b>modular-random</b>,
-<b>modular-random-prio</b>, <b>modular-random-prefetching</b>,
-<b>modular-random-prio-prefetching</b>) : \n
-Selects randomly a resource to be mapped on for each task.
-
-- Work Stealing (<b>modular-ws</b>) : \n
-Maps tasks to workers in round robin, but allows workers to steal work from other workers.
-
-- HEFT Scheduler : \n
-Maps tasks to workers using a heuristic very close to
-Heterogeneous Earliest Finish Time.
-It needs that every task submitted to StarPU have a
-defined performance model (\ref PerformanceModelCalibration)
-to work efficiently, but can handle tasks without a performance
-model. <b>modular-heft</b> just takes tasks by priority order. <b>modular-heft</b> takes
-at most 5 tasks of the same priority and checks which one fits best.
-<b>modular-heft-prio</b> is similar to <b>modular-heft</b>, but only decides the memory
-node, not the exact worker, just pushing tasks to one central queue per memory
-node.
-
-- Heteroprio Scheduler: \n
-<b>modular-heteroprio</b>Maps tasks to worker similarly to HEFT, but first attribute accelerated tasks to
-GPUs, then not-so-accelerated tasks to CPUs.
-
-To use one of these schedulers, one can set the environment variable \ref STARPU_SCHED.
-
 \section StaticScheduling Static Scheduling
 
 In some cases, one may want to force some scheduling, for instance force a given