jiangxb1987 commented on a change in pull request #24374: [SPARK-27366][CORE]
Support GPU Resources in Spark job scheduling
URL: https://github.com/apache/spark/pull/24374#discussion_r288360609
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File path:
core/src/main/scala/org/apache/spark/scheduler/cluster/CoarseGrainedSchedulerBackend.scala
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@@ -139,12 +140,17 @@ class CoarseGrainedSchedulerBackend(scheduler:
TaskSchedulerImpl, val rpcEnv: Rp
}
override def receive: PartialFunction[Any, Unit] = {
- case StatusUpdate(executorId, taskId, state, data) =>
+ case StatusUpdate(executorId, taskId, state, data, resources) =>
scheduler.statusUpdate(taskId, state, data.value)
if (TaskState.isFinished(state)) {
executorDataMap.get(executorId) match {
case Some(executorInfo) =>
executorInfo.freeCores += scheduler.CPUS_PER_TASK
+ for ((k, v) <- resources) {
+ executorInfo.availableResources.get(k).foreach { r =>
+ r.releaseAddresses(v.addresses)
Review comment:
I refactored the Executor Resource information class, now it only does
reservation inside TaskScheduler, and manage resources at SchedulerBackend side.
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