hunk.code_window:     node_table_.reset(new GcsNodeTable(store_client_));
       node_resource_table_.reset(new GcsNodeResourceTable(store_client_));
       placement_group_schedule_table_.reset(
           new GcsPlacementGroupScheduleTable(store_client_));
       heartbeat_table_.reset(new GcsHeartbeatTable(store_client_));
       resource_usage_batch_table_.reset(new GcsResourceUsageBatchTable(store_client_));
       profile_table_.reset(new GcsProfileTable(store_client_));
       worker_table_.reset(new GcsWorkerTable(store_client_));
       system_config_table_.reset(new GcsInternalConfigTable(store_client_));
  
hunk.labels: keep keep keep keep replace keep keep keep keep
hunk.file_path: src/ray/gcs/gcs_server/gcs_table_storage.h
hunk.type: replace
file: # 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+    |
  
      
  