diff --git a/algorithms.py b/algorithms.py
index 22a53ba..65d06b6 100644
--- a/algorithms.py
+++ b/algorithms.py
@@ -25,6 +25,7 @@
 # OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 
 from abc import ABC, abstractmethod
+import os
 import time
 import pandas as pd
 import numpy as np
@@ -311,9 +312,21 @@ def get_slices(self, n_slices, X, y):
 
     def fit(self, data, args):
         params = self.configure(data, args)
-        n_workers = None if args.gpus < 0 else args.gpus
-        cluster = LocalCUDACluster(n_workers=n_workers,
-                                   local_directory=args.root)
+        clusterargs={
+            'n_workers':int(os.environ.get("NUM_WORKERS", None if args.gpus < 0 else args.gpus)),
+            'local_directory':args.root,
+            'memory_limit':os.environ.get("DEVICE_MEMORY_LIMIT", None),
+            'device_memory_limit':os.getenv('DASK_DEVICE_MEMORY_LIMIT',None),
+            'protocol':"ucx" if os.environ.get("CLUSTER_MODE", "TCP")=="NVLINK" else "tcp",
+            'enable_tcp_over_ucx':os.environ.get("CLUSTER_MODE", "TCP")=="NVLINK",
+            'enable_nvlink':os.environ.get("CLUSTER_MODE", "TCP")=="NVLINK",
+            'enable_infiniband':os.getenv('CLUSTER_CONFIG_TYPE', "").endswith("ib"),
+            'enable_rdmacm':bool(os.getenv('ENABLE_RDMACM', False)),
+            'jit_unspill':True,
+            'rmm_pool_size':os.environ.get("POOL_SIZE", "29GB")
+        }
+
+        cluster = LocalCUDACluster( n_workers=clusterargs['n_workers'],  )
         client = Client(cluster)
         n_partitions = len(client.scheduler_info()['workers'])
         X_sliced, y_sliced = self.get_slices(n_partitions,
diff --git a/runme.py b/runme.py
index 89ff84b..f3dc1a0 100755
--- a/runme.py
+++ b/runme.py
@@ -31,6 +31,7 @@
 import json
 import ast
 import psutil
+import datetime
 import algorithms
 from metrics import get_metrics
 from datasets import prepare_dataset
@@ -126,6 +127,7 @@ def main():
     args.cpus = get_number_processors(args)
     args.extra = ast.literal_eval(args.extra)
     print_sys_info(args)
+    ts=datetime.datetime.utcnow().strftime('%Y%m%d.%H%M%S%f')
     if args.warmup:
         benchmark(args, os.path.join(args.root, "fraud"), "fraud")
     if args.dataset == 'all':
@@ -133,7 +135,8 @@ def main():
     results = {}
     for dataset in args.dataset.split(","):
         folder = os.path.join(args.root, dataset)
-        results.update({dataset: benchmark(args, folder, dataset)})
+        results.update({ 'timestamp_utc': ts,
+                         dataset: benchmark(args, folder, dataset)})
         print(json.dumps({dataset: results[dataset]}, indent=2, sort_keys=True))
     output = json.dumps(results, indent=2, sort_keys=True)
     output_file = open(args.output, "w")