Thanks for the explanation, Steve. I don't want to control where the work is done. What I wanted to understand is if Spark could take advantage of the underlying architecture features. For example, if the CPUs on the nodes support some improved vector instructions, can the Spark jobs (if they have a lot of vector operations) benefit from this? If yes, how does it happen, inside Spark, or the JVM where the the job TAR is running on?
Also, for the GPU part you mentioned, labeling the GPU nodes, and scheduling work to those GPU-enabled system does not mean the GPU computation power will be utilized, right? The user has to provide CUDE codes (openCL/CUDA/etc) and somehow link them to the system. Is my understanding correct? Thanks, Boric -- View this message in context: http://apache-spark-developers-list.1001551.n3.nabble.com/How-Spark-utilize-low-level-architecture-features-tp16052p16072.html Sent from the Apache Spark Developers List mailing list archive at Nabble.com. --------------------------------------------------------------------- To unsubscribe, e-mail: dev-unsubscr...@spark.apache.org For additional commands, e-mail: dev-h...@spark.apache.org
