Make ARENA the default allocator for 22.04

NVIDIA · Mar 7, 2022 · a82d8af · a82d8af
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diff --git a/docs/configs.md b/docs/configs.md
@@ -39,7 +39,7 @@ Name | Description | Default Value
 <a name="memory.gpu.maxAllocFraction"></a>spark.rapids.memory.gpu.maxAllocFraction|The fraction of total GPU memory that limits the maximum size of the RMM pool. The value must be greater than or equal to the setting for spark.rapids.memory.gpu.allocFraction. Note that this limit will be reduced by the reserve memory configured in spark.rapids.memory.gpu.reserve.|1.0
 <a name="memory.gpu.minAllocFraction"></a>spark.rapids.memory.gpu.minAllocFraction|The fraction of total GPU memory that limits the minimum size of the RMM pool. The value must be less than or equal to the setting for spark.rapids.memory.gpu.allocFraction.|0.25
 <a name="memory.gpu.oomDumpDir"></a>spark.rapids.memory.gpu.oomDumpDir|The path to a local directory where a heap dump will be created if the GPU encounters an unrecoverable out-of-memory (OOM) error. The filename will be of the form: "gpu-oom-<pid>.hprof" where <pid> is the process ID.|None
-<a name="memory.gpu.pool"></a>spark.rapids.memory.gpu.pool|Select the RMM pooling allocator to use. Valid values are "DEFAULT", "ARENA", "ASYNC", and "NONE". With "DEFAULT", the RMM pool allocator is used; with "ARENA", the RMM arena allocator is used; with "ASYNC", the new CUDA stream-ordered memory allocator in CUDA 11.2+ is used. If set to "NONE", pooling is disabled and RMM just passes through to CUDA memory allocation directly.|ASYNC
+<a name="memory.gpu.pool"></a>spark.rapids.memory.gpu.pool|Select the RMM pooling allocator to use. Valid values are "DEFAULT", "ARENA", "ASYNC", and "NONE". With "DEFAULT", the RMM pool allocator is used; with "ARENA", the RMM arena allocator is used; with "ASYNC", the new CUDA stream-ordered memory allocator in CUDA 11.2+ is used. If set to "NONE", pooling is disabled and RMM just passes through to CUDA memory allocation directly.|ARENA
 <a name="memory.gpu.pooling.enabled"></a>spark.rapids.memory.gpu.pooling.enabled|Should RMM act as a pooling allocator for GPU memory, or should it just pass through to CUDA memory allocation directly. DEPRECATED: please use spark.rapids.memory.gpu.pool instead.|true
 <a name="memory.gpu.reserve"></a>spark.rapids.memory.gpu.reserve|The amount of GPU memory that should remain unallocated by RMM and left for system use such as memory needed for kernels and kernel launches.|671088640
 <a name="memory.gpu.unspill.enabled"></a>spark.rapids.memory.gpu.unspill.enabled|When a spilled GPU buffer is needed again, should it be unspilled, or only copied back into GPU memory temporarily. Unspilling may be useful for GPU buffers that are needed frequently, for example, broadcast variables; however, it may also increase GPU memory usage|false

diff --git a/sql-plugin/src/main/scala/com/nvidia/spark/rapids/RapidsConf.scala b/sql-plugin/src/main/scala/com/nvidia/spark/rapids/RapidsConf.scala
@@ -408,7 +408,7 @@ object RapidsConf {
       "memory allocator in CUDA 11.2+ is used. If set to \"NONE\", pooling is disabled and RMM " +
       "just passes through to CUDA memory allocation directly.")
     .stringConf
-    .createWithDefault("ASYNC")
+    .createWithDefault("ARENA")
 
   val CONCURRENT_GPU_TASKS = conf("spark.rapids.sql.concurrentGpuTasks")
       .doc("Set the number of tasks that can execute concurrently per GPU. " +