Saisai Shao created SPARK-24615:
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Summary: Accelerator aware task scheduling for Spark
Key: SPARK-24615
URL: https://issues.apache.org/jira/browse/SPARK-24615
Project: Spark
Issue Type: Improvement
Components: Spark Core
Affects Versions: 2.4.0
Reporter: Saisai Shao
In the machine learning area, accelerator card (GPU, FPGA, TPU) is predominant
compared to CPUs. To make the current Spark architecture to work with
accelerator cards, Spark itself should understand the existence of accelerators
and know how to schedule task onto the executors where accelerators are
equipped.
Current Spark’s scheduler schedules tasks based on the locality of the data
plus the available of CPUs. This will introduce some problems when scheduling
tasks with accelerators required.
# CPU cores are usually more than accelerators on one node, using CPU cores to
schedule accelerator required tasks will introduce the mismatch.
# In one cluster, we always assume that CPU is equipped in each node, but this
is not true of accelerator cards.
# The existence of heterogeneous tasks (accelerator required or not) requires
scheduler to schedule tasks with a smart way.
So here propose to improve the current scheduler to support heterogeneous tasks
(accelerator requires or not). Details is attached in google doc.
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