Merge branch 'branch-23.08' into cugraph-nx

benchmarks/cugraph/standalone/bulk_sampling/bulk_sampling.sh

@@ -0,0 +1,50 @@
+# Copyright (c) 2023, NVIDIA CORPORATION.
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+export RAPIDS_NO_INITIALIZE="1"
+export CUDF_SPILL="1"
+export LIBCUDF_CUFILE_POLICY=OFF
+
+
+dataset_name=$1
+dataset_root=$2
+output_root=$3
+batch_sizes=$4
+fanouts=$5
+reverse_edges=$6
+
+rm -rf $output_root
+mkdir -p $output_root
+
+# Change to 2 in Selene
+gpu_per_replica=4
+#--add_edge_ids \
+
+# Expand to 1, 4, 8 in Selene
+for i in 1,2,3,4:
+do 
+    for replication in 2;
+    do
+        dataset_name_with_replication="${dataset_name}[${replication}]"
+        dask_worker_devices=$(seq -s, 0 $((gpu_per_replica*replication-1)))
+        echo "Sampling dataset = $dataset_name_with_replication on devices = $dask_worker_devices"
+        python3 cugraph_bulk_sampling.py --datasets $dataset_name_with_replication \
+                --dataset_root $dataset_root \
+                --batch_sizes $batch_sizes \
+                --output_root $output_root \
+                --dask_worker_devices $dask_worker_devices \
+                --fanouts $fanouts \
+                --batch_sizes $batch_sizes \
+                --reverse_edges
+    done
+done

benchmarks/cugraph/standalone/bulk_sampling/cugraph_bulk_sampling.py

@@ -33,8 +33,6 @@
 
 import cugraph
 
-from datetime import datetime
-
 import json
 import re
 import os
@@ -50,6 +48,7 @@
 import dask_cudf
 import dask.dataframe as ddf
 from dask.distributed import default_client
+from cugraph.dask import get_n_workers
 
 from typing import Optional, Union, Dict
 
@@ -173,6 +172,7 @@ def sample_graph(G, label_df, output_path,seed=42, batch_size=500, seeds_per_cal
         random_state=seed,
         seeds_per_call=seeds_per_call,
         batches_per_partition=batches_per_partition,
+        log_level = logging.INFO
     )
 
     n_workers = len(default_client().scheduler_info()['workers'])
@@ -182,10 +182,10 @@ def sample_graph(G, label_df, output_path,seed=42, batch_size=500, seeds_per_cal
         'batch': cudf.Series(dtype='int32')
     })
 
-
     batch_df = label_df.map_partitions(_make_batch_ids, batch_size, n_workers, meta=meta)
+    #batch_df = batch_df.sort_values(by='node')
 
-    # should always persist the batch dataframe or performace may be suboptimal
+    # should always persist the batch dataframe or performance may be suboptimal
     batch_df = batch_df.persist()
 
     del label_df
@@ -278,6 +278,8 @@ def load_disk_dataset(dataset, dataset_dir='.', reverse_edges=True, replication_
     path = Path(dataset_dir) / dataset
     parquet_path = path / 'parquet'
 
+    n_workers = get_n_workers()
+
     with open(os.path.join(path, 'meta.json')) as meta_file:
         meta = json.load(meta_file)
 
@@ -289,7 +291,9 @@ def load_disk_dataset(dataset, dataset_dir='.', reverse_edges=True, replication_
         print(f'Loading edge index for edge type {edge_type}')
 
         can_edge_type = tuple(edge_type.split('__'))
-        edge_index_dict[can_edge_type] = dask_cudf.read_parquet(os.path.join(os.path.join(parquet_path, edge_type), 'edge_index.parquet'))
+        edge_index_dict[can_edge_type] = dask_cudf.read_parquet(
+            Path(parquet_path) / edge_type / 'edge_index.parquet'
+        ).repartition(n_workers*2)
 
         edge_index_dict[can_edge_type]['src'] += node_offsets_replicated[can_edge_type[0]]
         edge_index_dict[can_edge_type]['dst'] += node_offsets_replicated[can_edge_type[-1]]
@@ -344,7 +348,7 @@ def load_disk_dataset(dataset, dataset_dir='.', reverse_edges=True, replication_
         print(f'Loading node labels for node type {node_type} (offset={offset})')
         node_label_path = os.path.join(os.path.join(parquet_path, node_type), 'node_label.parquet')
         if os.path.exists(node_label_path):
-            node_labels[node_type] = dask_cudf.read_parquet(node_label_path).drop('label',axis=1).persist()
+            node_labels[node_type] = dask_cudf.read_parquet(node_label_path).repartition(n_workers).drop('label',axis=1).persist()
             node_labels[node_type]['node'] += offset
             node_labels[node_type] = node_labels[node_type].persist()
 

mg_utils/run-dask-process.sh

@@ -102,6 +102,7 @@ function buildTcpArgs {
                 "
 
     WORKER_ARGS="--rmm-pool-size=$WORKER_RMM_POOL_SIZE
+             --rmm-async
              --local-directory=/tmp/$LOGNAME
              --scheduler-file=$SCHEDULER_FILE
              --memory-limit=$DASK_HOST_MEMORY_LIMIT