HyukjinKwon commented on a change in pull request #25545: [SPARK-28843][PYTHON]
Set OMP_NUM_THREADS to executor cores for python
URL: https://github.com/apache/spark/pull/25545#discussion_r317423466
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File path: core/src/main/scala/org/apache/spark/api/python/PythonRunner.scala
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@@ -106,6 +106,13 @@ private[spark] abstract class BasePythonRunner[IN, OUT](
val startTime = System.currentTimeMillis
val env = SparkEnv.get
val localdir = env.blockManager.diskBlockManager.localDirs.map(f =>
f.getPath()).mkString(",")
+ // if OMP_NUM_THREADS is not explicitly set, override it with the number
of cores
+ if (conf.getOption("spark.executorEnv.OMP_NUM_THREADS").isEmpty) {
+ // SPARK-28843: limit the OpenMP thread pool to the number of cores
assigned to this executor
+ // this avoids high memory consumption with pandas/numpy because of a
large OpenMP thread pool
+ // see https://github.com/numpy/numpy/issues/10455
+
conf.getOption("spark.executor.cores").foreach(envVars.put("OMP_NUM_THREADS",
_))
Review comment:
Seems `spark.executor.cores` can mean different things, for instance, Yarn's
dynamic allocation mode:
> spark.executor.cores | 1 in YARN mode, all the available cores on the
worker in standalone
Can we investigate such cases?
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