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a051e693c1
In test_many_tasks.py case, we usually found the case failing and found the reason.
We sleep for sleep_time seconds to wait all tasks to be finished, but the computation of actual sleep time is done by 0.1 * #rounds, where 0.1 is the sleep time every round.
It looks perfect but one factor was missed, and that's the computation time elapsed. In this case, it is the time consumed by
cur_cpus = ray.available_resources().get("CPU", 0)
min_cpus_available = min(min_cpus_available, cur_cpus)
especially the ray.available_resources() took a quite time when the cluster is large. (in our case it took beyond 1s with 1500 nodes).
The situation we thought it would be:
for _ in range(sleep_time / 0.1):
sleep(0.1)
The actual situation happens:
for _ in range(sleep_time / 0.1):
do_something(); # it costs time, sometimes pretty much
sleep(0.1)
We don't know why ray.available_resources() is slow and if it's logical, but we can add a time checker to make the sleep time precise.
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| .. | ||
| distributed | ||
| object_store | ||
| single_node | ||
| app_config.yaml | ||
| distributed.yaml | ||
| distributed_smoke_test.yaml | ||
| many_nodes.yaml | ||
| object_store.yaml | ||
| README.md | ||
| scheduling.yaml | ||
| single_node.yaml | ||
Ray Scalability Envelope
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) | 250+ |
| # 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+ |