使用GPU的Spark：如何为每个执行程序强制执行1个任务

Question

I have Spark 2.1.0 running on a cluster with N slave nodes. Each node has 16 cores (8 cores/cpu and 2 cpus) and 1 GPU. I want to use the map process to launch a GPU kernel. Since there is only 1 GPU per node, I need to ensure that two executors are not on the same node (at the same time) trying to use the GPU and that two tasks are not submitted to the same executor at the same time.

How can I force Spark to have one executor per node?

I have tried the following:

--Setting: spark.executor.cores 16 in $SPARK_HOME/conf/spark-defaults.conf

--Setting: SPARK_WORKER_CORES = 16 and SPARK_WORKER_INSTANCES = 1 in $SPARK_HOME/conf/spark-env.sh

and,

--Setting conf = SparkConf().set('spark.executor.cores', 16).set('spark.executor.instances', 6) directly in my spark script (when I wanted N =6 for debugging purposes).

These options create 6 executors on different nodes as desired, but it seems that each task is assigned to the same executor.

Here are some snippets from my most the most recent output (which lead me to believe it should be working as I want).

17/02/17 11:09:10 INFO StandaloneAppClient$ClientEndpoint: Executor added: app-20170217110910-0000/0 on worker-20170217110853-10.128.14.208-35771 (10.128.14.208:35771) with 16 cores
17/02/17 11:09:10 INFO StandaloneSchedulerBackend: Granted executor ID app-20170217110910-0000/0 on hostPort 10.128.14.208:35771 with 16 cores, 16.0 GB RAM 
17/02/17 11:09:10 INFO StandaloneAppClient$ClientEndpoint: Executor added: app-20170217110910-0000/1 on worker-20170217110853-10.128.9.95-59294 (10.128.9.95:59294) with 16 cores
17/02/17 11:09:10 INFO StandaloneSchedulerBackend: Granted executor ID app-20170217110910-0000/1 on hostPort 10.128.9.95:59294 with 16 cores, 16.0 GB RAM 
17/02/17 11:09:10 INFO StandaloneAppClient$ClientEndpoint: Executor added: app-20170217110910-0000/2 on worker-20170217110853-10.128.3.71-47507 (10.128.3.71:47507) with 16 cores
17/02/17 11:09:10 INFO StandaloneSchedulerBackend: Granted executor ID app-20170217110910-0000/2 on hostPort 10.128.3.71:47507 with 16 cores, 16.0 GB RAM 
17/02/17 11:09:10 INFO StandaloneAppClient$ClientEndpoint: Executor added: app-20170217110910-0000/3 on worker-20170217110853-10.128.9.96-50800 (10.128.9.96:50800) with 16 cores
17/02/17 11:09:10 INFO StandaloneSchedulerBackend: Granted executor ID app-20170217110910-0000/3 on hostPort 10.128.9.96:50800 with 16 cores, 16.0 GB RAM 
17/02/17 11:09:10 INFO StandaloneAppClient$ClientEndpoint: Executor added: app-20170217110910-0000/4 on worker-20170217110853-10.128.3.73-60194 (10.128.3.73:60194) with 16 cores
17/02/17 11:09:10 INFO StandaloneSchedulerBackend: Granted executor ID app-20170217110910-0000/4 on hostPort 10.128.3.73:60194 with 16 cores, 16.0 GB RAM 
17/02/17 11:09:10 INFO StandaloneAppClient$ClientEndpoint: Executor added: app-20170217110910-0000/5 on worker-20170217110853-10.128.3.74-42793 (10.128.3.74:42793) with 16 cores
17/02/17 11:09:10 INFO StandaloneSchedulerBackend: Granted executor ID app-20170217110910-0000/5 on hostPort 10.128.3.74:42793 with 16 cores, 16.0 GB RAM 
17/02/17 11:09:10 INFO StandaloneAppClient$ClientEndpoint: Executor updated: app-20170217110910-0000/1 is now RUNNING
17/02/17 11:09:10 INFO StandaloneAppClient$ClientEndpoint: Executor updated: app-20170217110910-0000/3 is now RUNNING
17/02/17 11:09:10 INFO StandaloneAppClient$ClientEndpoint: Executor updated: app-20170217110910-0000/4 is now RUNNING
17/02/17 11:09:10 INFO StandaloneAppClient$ClientEndpoint: Executor updated: app-20170217110910-0000/2 is now RUNNING
17/02/17 11:09:10 INFO StandaloneAppClient$ClientEndpoint: Executor updated: app-20170217110910-0000/0 is now RUNNING
17/02/17 11:09:10 INFO StandaloneAppClient$ClientEndpoint: Executor updated: app-20170217110910-0000/5 is now RUNNING
17/02/17 11:09:11 INFO StandaloneSchedulerBackend: SchedulerBackend is ready for scheduling beginning after reached minRegisteredResourcesRatio: 0.0 

My RDD has 6 partitions.

The important thing is that 6 Executors were started, each with a different IP address and each getting 16 cores (exactly what I expected). The phrase My RDD has 6 partitions. is a print statement from my code after repartitioning my RDD (to make sure I had 1 partition per executor).

Then, THIS happens... each of the 6 tasks are sent to the same executor!

17/02/17 11:09:12 INFO TaskSchedulerImpl: Adding task set 0.0 with 6 tasks
17/02/17 11:09:17 INFO CoarseGrainedSchedulerBackend$DriverEndpoint: Registered executor NettyRpcEndpointRef(null) (10.128.9.95:34059) with ID 1
17/02/17 11:09:17 INFO TaskSetManager: Starting task 0.0 in stage 0.0 (TID 0, 10.128.9.95, executor 1, partition 0, PROCESS_LOCAL, 6095 bytes)
17/02/17 11:09:17 INFO TaskSetManager: Starting task 1.0 in stage 0.0 (TID 1, 10.128.9.95, executor 1, partition 1, PROCESS_LOCAL, 6095 bytes)
17/02/17 11:09:17 INFO TaskSetManager: Starting task 2.0 in stage 0.0 (TID 2, 10.128.9.95, executor 1, partition 2, PROCESS_LOCAL, 6095 bytes)
17/02/17 11:09:17 INFO TaskSetManager: Starting task 3.0 in stage 0.0 (TID 3, 10.128.9.95, executor 1, partition 3, PROCESS_LOCAL, 6095 bytes)
17/02/17 11:09:17 INFO TaskSetManager: Starting task 4.0 in stage 0.0 (TID 4, 10.128.9.95, executor 1, partition 4, PROCESS_LOCAL, 6095 bytes)
17/02/17 11:09:17 INFO TaskSetManager: Starting task 5.0 in stage 0.0 (TID 5, 10.128.9.95, executor 1, partition 5, PROCESS_LOCAL, 6095 bytes)

Why? and How can I fix it? The problem is that at this point, all 6 tasks compete for the same GPU and the GPU cannot be shared.

Answer 1

I tried the suggestion in the comments of Samson Scharfrichter, but they didn't seem to work. However, I found: http://spark.apache.org/docs/latest/configuration.html#scheduling which includes spark.task.cpus . If I set that to 16 and spark.executor.cores to 16 then I appear to get one task assigned to each executor.