ray/benchmarks

Ray Scalability Envelope

Note: This document is a WIP. This is not a scalability guarantee (yet).

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)	1000+
# actors in cluster (with trivial workload)	10k+
# 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+