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@@ -41,27 +41,27 @@ get the list of available schedulers.
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\subsection NonPerformanceModelingPolicies Non Performance Modelling Policies
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-The <b>eager</b> scheduler uses a central task queue, from which all workers draw tasks
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+- The <b>eager</b> scheduler uses a central task queue, from which all workers draw tasks
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to work on concurrently. This however does not permit to prefetch data since the scheduling
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decision is taken late. If a task has a non-0 priority, it is put at the front of the queue.
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-The <b>random</b> scheduler uses a queue per worker, and distributes tasks randomly according to assumed worker
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+- The <b>random</b> scheduler uses a queue per worker, and distributes tasks randomly according to assumed worker
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overall performance.
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-The <b>ws</b> (work stealing) scheduler uses a queue per worker, and schedules
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+- The <b>ws</b> (work stealing) scheduler uses a queue per worker, and schedules
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a task on the worker which released it by
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default. When a worker becomes idle, it steals a task from the most loaded
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worker.
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-The <b>lws</b> (locality work stealing) scheduler uses a queue per worker, and schedules
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+- The <b>lws</b> (locality work stealing) scheduler uses a queue per worker, and schedules
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a task on the worker which released it by
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default. When a worker becomes idle, it steals a task from neighbour workers. It
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also takes into account priorities.
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-The <b>prio</b> scheduler also uses a central task queue, but sorts tasks by
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+- The <b>prio</b> scheduler also uses a central task queue, but sorts tasks by
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priority specified by the programmer (between -5 and 5).
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-The <b>heteroprio</b> scheduler uses different priorities for the different processing units.
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+- The <b>heteroprio</b> scheduler uses different priorities for the different processing units.
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This scheduler must be configured to work correclty and to expect high-performance
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as described in the corresponding section.
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@@ -87,41 +87,41 @@ family policy using performance model hints. A low or zero percentage may be
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the sign that performance models are not converging or that codelets do not
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have performance models enabled.
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-The <b>dm</b> (deque model) scheduler takes task execution performance models into account to
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+- The <b>dm</b> (deque model) scheduler takes task execution performance models into account to
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perform a HEFT-similar scheduling strategy: it schedules tasks where their
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termination time will be minimal. The difference with HEFT is that <b>dm</b>
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schedules tasks as soon as they become available, and thus in the order they
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become available, without taking priorities into account.
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-The <b>dmda</b> (deque model data aware) scheduler is similar to dm, but it also takes
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+- The <b>dmda</b> (deque model data aware) scheduler is similar to dm, but it also takes
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into account data transfer time.
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-The <b>dmdap</b> (deque model data aware prio) scheduler is similar to dmda,
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+- The <b>dmdap</b> (deque model data aware prio) scheduler is similar to dmda,
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except that it sorts tasks by priority order, which allows to become even closer
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to HEFT by respecting priorities after having made the scheduling decision (but
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it still schedules tasks in the order they become available).
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-The <b>dmdar</b> (deque model data aware ready) scheduler is similar to dmda,
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+- The <b>dmdar</b> (deque model data aware ready) scheduler is similar to dmda,
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but it also privileges tasks whose data buffers are already available
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on the target device.
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-The <b>dmdas</b> combines dmdap and dmdas: it sorts tasks by priority order,
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+- The <b>dmdas</b> combines dmdap and dmdas: it sorts tasks by priority order,
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but for a given priority it will privilege tasks whose data buffers are already
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available on the target device.
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-The <b>dmdasd</b> (deque model data aware sorted decision) scheduler is similar
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+- The <b>dmdasd</b> (deque model data aware sorted decision) scheduler is similar
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to dmdas, except that when scheduling a task, it takes into account its priority
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when computing the minimum completion time, since this task may get executed
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before others, and thus the latter should be ignored.
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-The <b>heft</b> (heterogeneous earliest finish time) scheduler is a deprecated
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+- The <b>heft</b> (heterogeneous earliest finish time) scheduler is a deprecated
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alias for <b>dmda</b>.
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-The <b>pheft</b> (parallel HEFT) scheduler is similar to dmda, it also supports
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+- The <b>pheft</b> (parallel HEFT) scheduler is similar to dmda, it also supports
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parallel tasks (still experimental). Should not be used when several contexts using
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it are being executed simultaneously.
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-The <b>peager</b> (parallel eager) scheduler is similar to eager, it also
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+- The <b>peager</b> (parallel eager) scheduler is similar to eager, it also
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supports parallel tasks (still experimental). Should not be used when several
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contexts using it are being executed simultaneously.
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@@ -132,24 +132,24 @@ DefiningANewModularSchedulingPolicy . It is currently shipped with the following
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pre-defined Modularized Schedulers :
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-<b>modular-eager</b> , <b>modular-eager-prefetching</b> are eager-based Schedulers (without and with prefetching)), they are \n
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+- <b>modular-eager</b> , <b>modular-eager-prefetching</b> are eager-based Schedulers (without and with prefetching)), they are \n
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naive schedulers, which try to map a task on the first available resource
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they find. The prefetching variant queues several tasks in advance to be able to
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do data prefetching. This may however degrade load balancing a bit.
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-<b>modular-prio</b>, <b>modular-prio-prefetching</b>, <b>modular-eager-prio</b> are prio-based Schedulers (without / with prefetching):,
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+- <b>modular-prio</b>, <b>modular-prio-prefetching</b>, <b>modular-eager-prio</b> are prio-based Schedulers (without / with prefetching):,
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similar to Eager-Based Schedulers. Can handle tasks which have a defined
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priority and schedule them accordingly.
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The <b>modular-eager-prio</b> variant integrates the eager and priority queue in a
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single component. This allows it to do a better job at pushing tasks.
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-<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
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+- <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
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Select randomly a resource to be mapped on for each task.
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-<b>modular-ws</b>) implements Work Stealing:
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+- <b>modular-ws</b>) implements Work Stealing:
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Maps tasks to workers in round robin, but allows workers to steal work from other workers.
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-<b>modular-heft</b>, <b>modular-heft2</b>, and <b>modular-heft-prio</b> are
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+- <b>modular-heft</b>, <b>modular-heft2</b>, and <b>modular-heft-prio</b> are
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HEFT Schedulers : \n
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Maps tasks to workers using a heuristic very close to
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Heterogeneous Earliest Finish Time.
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@@ -162,7 +162,7 @@ at most 5 tasks of the same priority and checks which one fits best.
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node, not the exact worker, just pushing tasks to one central queue per memory
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node.
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-<b>modular-heteroprio</b> is a Heteroprio Scheduler: \n
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+- <b>modular-heteroprio</b> is a Heteroprio Scheduler: \n
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Maps tasks to worker similarly to HEFT, but first attribute accelerated tasks to
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GPUs, then not-so-accelerated tasks to CPUs.
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