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35 lines
1.3 KiB
Markdown
35 lines
1.3 KiB
Markdown
# Ray Scalability Envelope
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### Note: This document is a WIP. This is not a scalability guarantee (yet).
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## Distributed Benchmarks
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All distributed tests are run on 64 nodes with 64 cores/node. Maximum number of nodes is achieved by adding 4 core nodes.
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| Dimension | Quantity |
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| --------- | -------- |
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| # nodes in cluster (with trivial task workload) | 1000+ |
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| # actors in cluster (with trivial workload) | 10k+ |
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| # simultaneously running tasks | 10k+ |
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| # simultaneously running placement groups | 1k+ |
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## Object Store Benchmarks
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| Dimension | Quantity |
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| --------- | -------- |
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| 1 GiB object broadcast (# of nodes) | 50+ |
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## Single Node Benchmarks.
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All single node benchmarks are run on a single m4.16xlarge.
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| Dimension | Quantity |
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| --------- | -------- |
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| # of object arguments to a single task | 10000+ |
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| # of objects returned from a single task | 3000+ |
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| # of plasma objects in a single `ray.get` call | 10000+ |
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| # of tasks queued on a single node | 1,000,000+ |
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| Maximum `ray.get` numpy object size | 100GiB+ |
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