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Ray Scalability Envelope

NOTE: the Ray scalability benchmarks are in the process of being refreshed. If you have questions about a specific workload or limit, please get in touch by filing a GitHub issue.

Distributed Benchmarks

All distributed tests are run on 64 nodes with 64 cores/node. Maximum number of nodes is achieved by adding 4 core nodes.

Dimension Quantity
# nodes in cluster (with trivial task workload) 2k+
# actors in cluster (with trivial workload) 40k+
# simultaneously running tasks 10k+
# simultaneously running placement groups 1k+

Object Store Benchmarks

Dimension Quantity
1 GiB object broadcast (# of nodes) 50+

Single Node Benchmarks.

All single node benchmarks are run on a single m4.16xlarge.

Dimension Quantity
# of object arguments to a single task 10000+
# of objects returned from a single task 3000+
# of plasma objects in a single ray.get call 10000+
# of tasks queued on a single node 1,000,000+
Maximum ray.get numpy object size 100GiB+