tgravescs 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_r289959498
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File path:
core/src/main/scala/org/apache/spark/scheduler/TaskSchedulerImpl.scala
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@@ -335,16 +339,22 @@ private[spark] class TaskSchedulerImpl(
for (i <- 0 until shuffledOffers.size) {
val execId = shuffledOffers(i).executorId
val host = shuffledOffers(i).host
- if (availableCpus(i) >= CPUS_PER_TASK) {
+ if (availableCpus(i) >= CPUS_PER_TASK &&
+ resourcesMeetTaskRequirements(availableResources(i))) {
try {
- for (task <- taskSet.resourceOffer(execId, host, maxLocality)) {
+ for (task <- taskSet.resourceOffer(execId, host, maxLocality,
availableResources(i))) {
tasks(i) += task
val tid = task.taskId
taskIdToTaskSetManager.put(tid, taskSet)
taskIdToExecutorId(tid) = execId
executorIdToRunningTaskIds(execId).add(tid)
availableCpus(i) -= CPUS_PER_TASK
assert(availableCpus(i) >= 0)
+ task.resources.foreach { case (rName, rInfo) =>
+ availableResources(i).getOrElse(rName,
+ throw new SparkException(s"Try to acquire resource $rName that
doesn't exist."))
+ .remove(0, rInfo.addresses.size)
Review comment:
I think this is worth a comment saying removing the first x elements which
is be the same as we allocated in taskSet.resourceOffer since its synchronized
(rather then the exact ones allocated)
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